diff --git a/.Rbuildignore b/.Rbuildignore index f616b607..5b261fd3 100755 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,24 +1,19 @@ +^renv$ +^renv\.lock$ +^CRAN-RELEASE$ # Extra material related but not to be included in the package inst/extras README.Rmd +README.html index.* # pkgdown docs + # Vignette -./vignettes/2015-RJournal -./vignettes/2017_RJournal_manuscript/* -./vignettes/cheatsheet/ -./vignettes/cheatsheet.Rmd -./vignettes/blogposts.Rmd -./vignettes/publications.Rmd -./vignettes/NUTS_2010_60M_SH/* -./vignettes/NUTS_2010_60M_SH.zip -./vignettes/NUTS_2010.xls -./vignettes/zipfile -./vignettes/figure -./vignettes/fig -./vignettes/cheatsheet +./vignettes/*.Rmd +#./vignettes/fig +#./vignettes/fig/ ./figure$ ./cache$ # Git related @@ -32,7 +27,6 @@ docs ^\.Rproj\.user$ ^packrat/ ^\.Rprofile$ -^NEWS.md$ ^cran-comments\.md$ ^data-raw$ ^revdep$ @@ -41,3 +35,11 @@ docs ^appveyor\.yml$ ^_pkgdown\.yml$ ^docs$ +^doc$ +^Meta$ +^\.github$ +^codecov\.yml$ +^pkgdown$ +^\._pkgdown\.yml$ +^vignettes/articles$ +^CRAN-SUBMISSION$ diff --git a/.Rinstignore b/.Rinstignore new file mode 100755 index 00000000..39b10844 --- /dev/null +++ b/.Rinstignore @@ -0,0 +1,2 @@ +inst/doc/fig + diff --git a/.github/.gitignore b/.github/.gitignore new file mode 100755 index 00000000..00fdd192 --- /dev/null +++ b/.github/.gitignore @@ -0,0 +1,3 @@ +*.html +R-version +*.Rds diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 00000000..3de2551d --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,12 @@ +# To get started with Dependabot version updates, you'll need to specify which +# package ecosystems to update and where the package manifests are located. +# Please see the documentation for all configuration options: +# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates + +version: 2 +updates: + - package-ecosystem: "github-actions" # See documentation for possible values + directory: "/" # Location of package manifests + schedule: + interval: "weekly" + diff --git a/.github/workflows/check-full.yaml b/.github/workflows/check-full.yaml new file mode 100644 index 00000000..7a47123a --- /dev/null +++ b/.github/workflows/check-full.yaml @@ -0,0 +1,47 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + workflow_dispatch: + schedule: + - cron: '0 0 * * 0' + +name: R-CMD-check (full) +jobs: + R-CMD-check: + runs-on: ${{ matrix.config.os }} + + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + + strategy: + fail-fast: false + matrix: + config: + - {os: macos-latest, r: 'release'} + - {os: windows-latest, r: 'release'} + - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} + - {os: ubuntu-latest, r: 'release'} + - {os: ubuntu-latest, r: 'oldrel-1'} + + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + R_KEEP_PKG_SOURCE: yes + + steps: + - uses: actions/checkout@v5 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::rcmdcheck + needs: check + + - uses: r-lib/actions/check-r-package@v2 + with: + upload-snapshots: true \ No newline at end of file diff --git a/.github/workflows/check-standard.yaml b/.github/workflows/check-standard.yaml new file mode 100644 index 00000000..b60da2fb --- /dev/null +++ b/.github/workflows/check-standard.yaml @@ -0,0 +1,29 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + +name: R-CMD-check (standard) + +jobs: + R-CMD-check: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + R_KEEP_PKG_SOURCE: yes + steps: + - uses: actions/checkout@v5 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::rcmdcheck + needs: check + + - uses: r-lib/actions/check-r-package@v2 \ No newline at end of file diff --git a/.github/workflows/rogtemplate-gh-pages.yaml b/.github/workflows/rogtemplate-gh-pages.yaml new file mode 100644 index 00000000..027815f9 --- /dev/null +++ b/.github/workflows/rogtemplate-gh-pages.yaml @@ -0,0 +1,58 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/master/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +# Workflow triggering derived from: https://stevenmortimer.com/running-github-actions-sequentially/ +on: + push: + branches: [main, master] + tags: ['*'] + paths-ignore: + - 'README.Rmd' + repository_dispatch: + types: [trigger-pkgdown-workflow] + workflow_dispatch: + +name: rogtemplate-gh-pages + +jobs: + rogtemplate-gh-pages: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v5 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + needs: website + extra-packages: + local::. + any::pkgdown + ropengov/rogtemplate + any::rcmdcheck + + - name: Build logo if not present and prepare template + run: | + # Check that logo is not present + if (isFALSE(file.exists(file.path("man", "figures", "logo.png")) || + file.exists(file.path("man", "figures", "logo.png")))) { + rogtemplate::rog_logo() + } else { + + message("The package already has a logo") + } + + rogtemplate::rog_add_template_pkgdown() + + shell: Rscript {0} + + - name: Deploy package + run: | + git config --local user.name "github-actions[bot]" + git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com" + Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)' diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml new file mode 100644 index 00000000..6aa95277 --- /dev/null +++ b/.github/workflows/test-coverage.yaml @@ -0,0 +1,32 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + +name: test-coverage + +jobs: + test-coverage: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} + + steps: + - uses: actions/checkout@v5 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::covr + needs: coverage + + - name: Test coverage + run: covr::codecov(token = Sys.getenv("CODECOV_TOKEN"), quiet = FALSE) + shell: Rscript {0} \ No newline at end of file diff --git a/.github/workflows/tidy_code.yaml b/.github/workflows/tidy_code.yaml new file mode 100644 index 00000000..48cb262a --- /dev/null +++ b/.github/workflows/tidy_code.yaml @@ -0,0 +1,63 @@ +# Workflow derived from https://github.com/r-lib/actions/blob/master/.github/workflows/render-readme.yaml +# and https://fromthebottomoftheheap.net/2020/04/30/rendering-your-readme-with-github-actions/ +# Workflow triggering derived from: https://stevenmortimer.com/running-github-actions-sequentially/ +on: + workflow_dispatch: + +# This is triggered manually + + +name: Tidy code + +jobs: + tidy: + name: Tidy code + runs-on: macOS-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v5 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: | + local::. + any::usethis + any::styler + any::urlchecker + any::devtools + any::roxygen2 + ropengov/rogtemplate + needs: website + - name: Tidy code + run: | + usethis::use_tidy_description() + tools::resaveRdaFiles("data") + tools::resaveRdaFiles("R") + styler::style_pkg(filetype = c("R", "Rmd")) + urlchecker::url_update() + devtools::build_readme() + roxygen2::roxygenise() + shell: Rscript {0} + + + - name: Commit results + run: | + git config --local user.name "github-actions[bot]" + git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com" + git add -A + git commit -m 'Tidy code' || echo "No changes to commit" + git push origin || echo "No changes to commit" + + - name: Trigger pkgdown workflow + if: success() + run: | + git config --local user.name "github-actions[bot]" + git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com" + Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)' \ No newline at end of file diff --git a/.gitignore b/.gitignore index e9fa3acc..20546d8a 100755 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,15 @@ -_pkgdown.yml -docs/docsearch.js -docs/articles/2015-RJournal/response.txt +.Rproj.user +.Rhistory +.RData +.Ruserdata +.Rdata +.httr-oauth +.DS_Store +inst/doc *Rcheck* -inst/extras +inst/extras/* +application/* +!inst/extras/cheatsheet/ inst/extras/..Rcheck *~ */~ @@ -12,7 +19,6 @@ vignettes/*_tutorial.html vignettes/*~ vignettes/tmp* vignettes/fig/*.pdf -.Rproj.user eurostat.Rproj vignettes/NUTS_2010.xls vignettes/NUTS_2010_60M_SH* @@ -20,6 +26,9 @@ vignettes/zipfile NUTS_2010.xls zipfile packrat/lib*/ -.Rhistory - /revdep/.cache.rds +doc +Meta +/doc/ +/Meta/ +docs diff --git a/.travis.yml b/.travis.yml deleted file mode 100755 index a8766783..00000000 --- a/.travis.yml +++ /dev/null @@ -1,173 +0,0 @@ -language: r -sudo: required -cache: packages -dist: trusty - -warnings_are_errors: false - -notifications: - email: - - leo.lahti@iki.fi - - -r_packages: - - broom - - Cairo - - classInt - - curl - - devtools - - ggplot2 - - httr - - jsonlite - - knitr - - lubridate - - gdalUtils - - geojson - - geojsonio - - geojsonlint - - jqr - - mapview - - mapproj - - plotrix - - protolite - - rgdal - - RColorBrewer - - readr - - rmarkdown - - rmapshaper - - roxygen2 - - rsdmx - - rvest - - sf - - sp - - tmap - - tmaptools - - testthat - - V8 - -r_github_packages: - - jimhester/covr - - r-dbi/DBI - - r-dbi/RPostgres - - -addons: - postgresql: "9.6" - apt: - sources: - - sourceline: 'ppa:opencpu/jq' - - sourceline: 'ppa:ubuntugis/ubuntugis-unstable' - - sourceline: 'deb http://apt.postgresql.org/pub/repos/apt/ trusty-pgdg main' - key_url: 'https://www.postgresql.org/media/keys/ACCC4CF8.asc' - packages: - - libprotobuf-dev - - protobuf-compiler - - libv8-3.14-dev - - libjq-dev - - libudunits2-dev - - libproj-dev - - libgeos-dev - - libspatialite-dev - - libgdal-dev - - libjson-c-dev - - postgresql-server-dev-9.6 - -env: - global: - - WARNINGS_ARE_ERRORS=1 - - GITHUB_REPO: ropengov/eurostat - -r_build_args: "--no-resave-data --no-build-vignettes" -r_check_args: "--as-cran --no-build-vignettes" - -apt_packages: - - libudunits2-dev - - udunits2 - - libv8-dev - - libprotobuf-dev - - libprotoc-dev - - libcairo2-dev - - protobuf-compiler - - valgrind - - libgdal20 - - libspatialite-dev - - libproj-dev - - libgeos-dev - - libgdal1-dev - - libgdal-dev - - libgeos-c1v5 - - libjq-dev - - netcdf-bin - -before_install: - - pip install --user codecov - - sudo apt-get -qq update - - sudo apt-get install -y libxml2-dev - - sudo apt-get install -y libprotobuf-dev protobuf-compiler - - sudo apt-get install libssl-dev - - sudo apt-get install libcairo2-dev - - sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable --yes - - sudo add-apt-repository -y ppa:opencpu/jq - - sudo apt-get --yes --force-yes update -qq - - sudo apt-get --yes install npm - - # install postgis from source: - - wget http://download.osgeo.org/postgis/source/postgis-2.3.2.tar.gz - - (mv postgis* /tmp; cd /tmp; tar xzf postgis-2.3.2.tar.gz) - - (cd /tmp/postgis-2.3.2 ; ./configure; make; sudo make install) - - # activate liblwgeom by: - - sudo ldconfig - # create postgis databases: - - sudo service postgresql restart - - createdb postgis - - psql -d postgis -c "CREATE EXTENSION postgis;" - - psql -d postgis -c "GRANT CREATE ON DATABASE postgis TO travis" - - psql -d postgis -c "GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO travis" - - createdb empty - - psql -d empty -c "CREATE EXTENSION postgis;" - # - R -q -e 'install.packages("devtools"); devtools::install_github("ropenscilabs/tic"); tic::prepare_all_stages()' - - - - Rscript -e 'install.packages("broom",repos="http://cran.us.r-project.org")' - - Rscript -e 'install.packages("classInt")' - - Rscript -e 'install.packages("jsonlite")' - - Rscript -e 'install.packages("curl")' - - Rscript -e 'install.packages("httr")' - - Rscript -e 'install.packages("RColorBrewer")' - - Rscript -e 'install.packages("sp")' - - Rscript -e 'install.packages("readr")' - - Rscript -e 'install.packages("Cairo")' - - Rscript -e 'install.packages("ggplot2")' - - Rscript -e 'install.packages("lubridate")' - - Rscript -e 'install.packages("mapproj")' - - Rscript -e 'install.packages("plotrix")' - - Rscript -e 'install.packages("rmarkdown")' - - Rscript -e 'install.packages("roxygen2")' - - Rscript -e 'install.packages("rsdmx")' - - Rscript -e 'install.packages("rvest")' - - Rscript -e 'install.packages("testthat")' - - Rscript -e 'install.packages("knitr")' - - Rscript -e 'install.packages("tmap", repos="http://cran.us.r-project.org")' - - Rscript -e 'install.packages("sf", repos="http://cran.us.r-project.org")' - - -install: - - . $HOME/.nvm/nvm.sh - - nvm install stable - - nvm use stable - - npm install -g mapshaper - - npm install -g geojson - -r: -- release -- devel - -after_success: - #- Rscript -e 'covr::codecov()' - - dropdb postgis - - createdb postgis - - psql -d postgis -c "CREATE EXTENSION postgis;" - - psql -d postgis -c "GRANT CREATE ON DATABASE postgis TO travis" - - R -q -e 'covr::codecov(quiet = FALSE)' - diff --git a/DESCRIPTION b/DESCRIPTION index 9083550c..ea2c8b76 100755 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,58 +1,76 @@ -Package: eurostat Type: Package +Package: eurostat Title: Tools for Eurostat Open Data -Date: 2018-10-16 -Version: 3.2.93 -Encoding: UTF-8 +Version: 4.0.0 +Date: 2023-12-19 Authors@R: c( - person("Leo", "Lahti", email = "leo.lahti@iki.fi", role = c("aut", "cre")), - person("Janne", "Huovari", role = "aut"), - person("Markus", "Kainu", role = "aut"), - person("Przemyslaw" , "Biecek", role = "aut"), - person("Joona", "Lehtomaki", role = "ctb"), - person("Francois", "Briatte", role = "ctb"), - person("Oliver", "Reiter", role = "ctb")) -Maintainer: Leo Lahti -MailingList: rOpenGov + person("Leo", "Lahti", , "leo.lahti@iki.fi", role = c("aut", "cre"), + comment = c(ORCID = "0000-0001-5537-637X")), + person("Janne", "Huovari", role = "aut"), + person("Markus", "Kainu", role = "aut"), + person("Przemyslaw", "Biecek", role = "aut"), + person("Daniel", "Antal", role = "ctb"), + person("Diego", "Hernangomez", role = "ctb", + comment = c(ORCID = "0000-0001-8457-4658")), + person("Joona", "Lehtomaki", role = "ctb"), + person("Francois", "Briatte", role = "ctb"), + person("Reto", "Stauffer", role = "ctb"), + person("Paul", "Rougieux", role = "ctb"), + person("Anna", "Vasylytsya", role = "ctb"), + person("Oliver", "Reiter", role = "ctb"), + person("Pyry", "Kantanen", role = "ctb", + comment = c(ORCID = "0000-0003-2853-2765")), + person("Enrico", "Spinielli", , "enrico.spinielli@gmail.com", role = "ctb", + comment = c(ORCID = "0000-0001-8584-9131")) + ) Description: Tools to download data from the Eurostat database - together with search and - manipulation utilities. + together with search and manipulation + utilities. License: BSD_2_clause + file LICENSE +URL: https://ropengov.github.io/eurostat/, + https://github.com/rOpenGov/eurostat +BugReports: https://github.com/rOpenGov/eurostat/issues Depends: - methods, - R (>= 3.4.0) + R (>= 3.6.0) Imports: - broom, classInt, - dplyr, - httr, + countrycode, + curl, + digest, + dplyr, + httr2 (>= 0.2.3), + ISOweek, jsonlite, - RColorBrewer, - readr, - sf, - sp, + lubridate, + rappdirs, + readr, + RefManageR, + regions, + rlang, stringi, stringr, tibble, - tidyr, - countrycode + tidyr (>= 1.0.0), + xml2, + data.table (>= 1.14.8) Suggests: - Cairo, - ggplot2, - knitr, - lubridate, - mapproj, - plotrix, + giscoR, + knitr, rmarkdown, - roxygen2, - rsdmx, - rvest, - testthat, - tmap + sf, + testthat (>= 3.0.0) +VignetteBuilder: + knitr +Config/Needs/website: ggplot2, tmap, styler, sessioninfo, + ropengov/rogtemplate, ragg +Config/testthat/edition: 3 +Config/testthat/parallel: false +Encoding: UTF-8 LazyData: true -URL: https://ropengov.github.io/eurostat -BugReports: https://github.com/ropengov/eurostat/issues -VignetteBuilder: knitr +MailingList: rOpenGov NeedsCompilation: no Repository: CRAN -RoxygenNote: 6.1.0 +Roxygen: list(markdown = TRUE) +RoxygenNote: 7.2.3 +X-schema.org-isPartOf: http://ropengov.org/ +X-schema.org-keywords: ropengov diff --git a/LICENSE b/LICENSE index 684e34df..1de69c39 100755 --- a/LICENSE +++ b/LICENSE @@ -1,2 +1,2 @@ -YEAR: 2014-2018 -COPYRIGHT HOLDER: Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek +YEAR: 2014-2022 +COPYRIGHT HOLDER: Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek \ No newline at end of file diff --git a/NAMESPACE b/NAMESPACE index ccc68cf6..2ecf819b 100755 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,48 +1,96 @@ # Generated by roxygen2: do not edit by hand +export(add_nuts_level) +export(check_access_to_data) export(clean_eurostat_cache) export(cut_to_classes) export(dic_order) export(eurotime2date) export(eurotime2num) +export(get_bibentry) export(get_eurostat) export(get_eurostat_dic) +export(get_eurostat_folder) export(get_eurostat_geospatial) +export(get_eurostat_interactive) export(get_eurostat_json) export(get_eurostat_toc) export(grepEurostatTOC) export(harmonize_country_code) +export(harmonize_geo_code) export(label_eurostat) export(label_eurostat_tables) export(label_eurostat_vars) +export(list_eurostat_cache_items) +export(recode_nuts) +export(recode_to_nuts_2013) +export(recode_to_nuts_2016) export(search_eurostat) -import(methods) -importFrom(RColorBrewer,brewer.pal) -importFrom(broom,tidy) +export(set_eurostat_cache_dir) +export(validate_geo_code) +export(validate_nuts_regions) +importFrom(ISOweek,ISOweek2date) +importFrom(RefManageR,BibEntry) +importFrom(RefManageR,toBiblatex) importFrom(classInt,classIntervals) +importFrom(countrycode,countrycode) +importFrom(curl,curl_download) +importFrom(data.table,":=") +importFrom(data.table,.SD) +importFrom(data.table,fread) +importFrom(data.table,melt) +importFrom(data.table,setDT) +importFrom(digest,digest) importFrom(dplyr,"%>%") +importFrom(dplyr,case_when) +importFrom(dplyr,coalesce) importFrom(dplyr,filter) -importFrom(dplyr,left_join) +importFrom(dplyr,inner_join) importFrom(dplyr,mutate) -importFrom(dplyr,select) -importFrom(httr,GET) -importFrom(httr,build_url) -importFrom(httr,content) -importFrom(httr,status_code) +importFrom(httr2,"%>%") +importFrom(httr2,req_error) +importFrom(httr2,req_perform) +importFrom(httr2,req_proxy) +importFrom(httr2,req_retry) +importFrom(httr2,req_user_agent) +importFrom(httr2,request) +importFrom(httr2,resp_body_json) +importFrom(httr2,resp_content_type) +importFrom(httr2,resp_is_error) +importFrom(httr2,url_build) +importFrom(httr2,url_parse) importFrom(jsonlite,fromJSON) -importFrom(methods,as) +importFrom(jsonlite,toJSON) +importFrom(lubridate,day) +importFrom(lubridate,dmy) +importFrom(lubridate,month) +importFrom(lubridate,year) +importFrom(lubridate,ymd) importFrom(readr,col_character) +importFrom(readr,cols) importFrom(readr,read_tsv) -importFrom(sf,as_Spatial) -importFrom(sf,st_as_sf) -importFrom(sf,st_read) -importFrom(sp,merge) -importFrom(stringi,stri_match_first_regex) +importFrom(regions,recode_nuts) +importFrom(regions,validate_geo_code) +importFrom(regions,validate_nuts_regions) +importFrom(rlang,"!!") +importFrom(rlang,sym) +importFrom(stats,na.omit) +importFrom(stringi,stri_extract_first_regex) +importFrom(stringi,stri_replace_all_fixed) +importFrom(stringi,stri_replace_all_regex) +importFrom(stringr,str_extract) +importFrom(stringr,str_glue) importFrom(stringr,str_replace_all) -importFrom(tibble,as_data_frame) -importFrom(tibble,data_frame) +importFrom(tibble,as_tibble) importFrom(tibble,is_tibble) -importFrom(tidyr,gather_) +importFrom(tidyr,pivot_longer) importFrom(tidyr,separate) -importFrom(utils,data) +importFrom(utils,capture.output) importFrom(utils,download.file) +importFrom(utils,hasName) +importFrom(utils,menu) +importFrom(utils,person) +importFrom(utils,toBibtex) +importFrom(xml2,read_xml) +importFrom(xml2,xml_find_all) +importFrom(xml2,xml_text) diff --git a/NEWS.md b/NEWS.md old mode 100755 new mode 100644 index 1993dc18..22c8436a --- a/NEWS.md +++ b/NEWS.md @@ -1,8 +1,163 @@ -# eurostat next +# eurostat 4.0.0 -* The `label_eurostat()` has new countrycode and countrycode_nomatch arguments to - label with countrycode package and custom_dic argument to add custom dictionary. +## Major updates + +* Add data.table to package Imports and make using data.table functions optional with `get_eurostat()` `use.data.table` argument. This is especially useful with big datasets that would otherwise take a long time to go through the different data cleaning functions or crash R with their large memory footprint. (issue #277, PR #278) +* switch from `httr` package to `httr2` (issue #273, PR #276) +* Rewritten caching functionalities, making it possible to cache filtered queries and rely on local caches if the user attempt to filter a complete dataset that has already been cached. A list of queries and cached item hashes is stored in a cache_list.json file in cache folder. This can be viewed with a new function: `list_eurostat_cache_items()`. (Affects issues mentioned in #144, #257, #258, fixed in PR #267) +* Column names in `.eurostatTOC` object (returned by `get_eurostat_toc()`) now use dots instead of spaces in the style of `base::make.names()`, e.g. turning `last update of data` to `last.update.of.data` (PR #271) +* `.eurostatTOC` object includes a new hierarchy column that represents the position of each folder, dataset and table in the folder structure. +* `search_eurostat()` includes the option to search Table of Content items by dataset codes in addition to titles. This makes it possible to make further queries from similar datasets (e.g. "nama_10_gdp", "nama_10r_2gdp", "nama_10r_3popgdp") that might have different titles. +* `label_eurostat_tables()` has been rewritten to use the new SDMX API instead of `table_dic.dic` file in Eurostat Bulk Download Listing (PR #271) +* Remove legacy code related to downloading data from old bulk download facilities and temporary functions added in package version 3.7.14. +* `get_eurostat_geospatial()` now leverages on `giscoR::gisco_get_nuts()` for +downloading geospatial data (PR #264, thanks to @dieghernan): + * `"spdf"` output class soft-deprecated, it would return a `sf` object with a message. + * `make_valid` parameter soft-deprecated. + * Added `...` to the function so additional parametes can be passed to `giscoR::gisco_get_nuts()`. + * Dataset `eurostat_geodata_60_2016` updated. +* `get_eurostat_geospatial()` now requires sf package to work at all (PR #280, thanks to @dieghernan) + +## Minor updates + +* Added suppressWarnings() to some of the tests that use TOC's directly or indirectly as the tests are not directly related to TOC files. +* Use more parameter inheritance in package function documentation to reduce discrepancies between different functions (DRY-principle) (PR #270) +* Documentation more explicitly explains how to use filter parameters in `get_eurostat()` and `get_eurostat_json()` functions. The documentation now warns users about potential problems caused by `time` / `TIME_PERIOD` parameters when used to query datasets that contain quarterly data (issue #260) +* As continuation of the update done in 3.7.14, started to use the new URL also for dictionary files in `get_eurostat_dic()` and `label_eurostat()` functions. +* `get_bibentry()` now outputs "Accessed YYYY-MM-DD" and "dataset last updated YYYY-MM-DD" in note field as otherwise it would be sporadically printed or not at all printed from `urldate` field. +* Print more informative API error messages. (issue #261, PR #262, thanks to @ake123) +* Removed `sp`, `methods` and `broom` packages from dependencies. +* Added `giscoR` to Suggests. (PR #264) + +## New features + +* Added new function: `get_eurostat_interactive()` for interactively searching and downloading data from Eurostat SDMX API. The function aims to make good data citation practices more prominently visible and also make it easier to explore what different arguments in `get_eurostat()` function do. +* There is also a new internal function `eurostat:::fixity_checksum()` to easily calculate a fixity checksum for datasets downloaded from Eurostat. The fixity checksum can, for example, be saved in research notes and reported in as part of data appendices. Printing the fixity checksum is encouraged by including an option to print it in every `get_eurostat_interactive()` query. +* Added a new internal function `clean_eurostat_toc()` for easy removal of TOC objects from .EurostatEnv environment. (PR #278) +* New internal function `check_lang()` (PR #270) +* `get_eurostat()` function now explicity accepts a 'lang' argument, for passing onwards to `get_eurostat_json()` and `label_eurostat()` (PR #270) +* New user facing function: `get_eurostat_folder()` for downloading all datasets in a folder. The function is limited to downloading folders that contain at maximum 20 datasets. This function relies on new internal helper functions: `toc_count_whitespace()`, `toc_determine_hierarchy()`, `toc_count_children()` and `toc_list_children()`. (PR #270) +* EXPERIMENTAL: `get_eurostat_toc()` and `set_eurostat_toc()` now have experimental features that support downloading TOCs in French and German as well. This support, in turn, is leveraged in `get_bibentry()` which now has a language parameter: `lang` (PR #270) +* Related to updates to `get_eurostat_toc()`, `search_eurostat()` now supports searching from French and German TOC-files as well (PR #270) + +## Deprecated and defunct + +* `grepEurostatTOC()` is completely marked as defunct and is enroute to being removed from the package as `search_eurostat()` is now the only way to fetch Eurostat TOC items and search (grep) them (PR #270) +* During the development of the 4.0.0 version there was a temporary function called `label_eurostat_vars2` that has been removed in the final version, as promised earlier: "The old function will be completely removed after October 2023 when Eurostat Bulk Download Listing website is retired and `label_eurostat_vars2` will be renamed to `label_eurostat_vars()`". The new `label_eurostat_vars()` function uses the new SDMX API to retrieve names for dataset columns. Function evolution is subject to ongoing Eurostat API developments. (PR #270) + +## Bug fixes + +* Added a more informatic warning message in situations where TOC datasets downloaded from Eurostat might not have proper titles. For some reason this was isolated to German and French language versions of TOC while English language TOC had proper titles for all items. (PR #278) +* `get_bibentry()` returns correct codes for titles and warns the user if some / all of the requested codes were not found in the TOC (PR #270) +* `get_bibentry()` uses the date field with the internal BibEntry format that can be easily translated to other formats: bibtex, bibentry (PR #270) +* `get_bibentry()` now outputs dataset codes in titles correctly so that `bibtex` and `biblatex` entries can be copypasted into bibliographies without adding escape characters manually (PR #270) +* Fix issue related to downloading quarterly data (issue #260, PR #271) +* Reduce RAM usage in `eurotime2date()` when handling big datasets containing weekly data and tens of millions of rows (dataset used for testing mentioned in issue #200). + +# eurostat 3.8.3 (2023-03-07) + +## Bug fixes + +* Fix date handling bug in the `get_eurostat_json()` and `eurotime2date()` functions (issue #251, reported by @lz1nwm). The `get_eurostat_json()` function uses the temporary `eurotime2date()` function for date handling until the old bulk download API is deprecated. + +# eurostat 3.8.2 (2023-03-06) + +## Minor updates + +* use `curl::curl_download` on Windows platforms instead of `utils::download.file` as the latter causes the following error: "error reading from the connection [...] invalid or incomplete compressed data". This affects only files downloaded from the new API. + +# eurostat 3.7.14 (2023-02-22) + +## Major updates + +* Updated `get_eurostat()` and its assorted functions to download data from the new dissemination API (related to issues #251, #243). See Eurostat web page Transition - from Eurostat Bulk Download to API for a list of differences between old and new data sources: https://wikis.ec.europa.eu/display/EUROSTATHELP/Transition+-+from+Eurostat+Bulk+Download+to+API +* Added new temporary functions for downloading and handling data from the new dissemination API: `get_eurostat_raw2`, `tidy_eurostat2`, `convert_time_col2`, `eurotime2date2`, `eurotime2num2` and `label_eurostat2`. When the old bulk download facilities are decommissioned, these functions will replace the old functions with old naming schemes (without the 2s at the end). +* `tidy_eurostat2` function is now able to handle multiple time frequencies in one call: For example, you can download annual, quarterly, and monthly data simply by using a vector c("A", "Q", "M") in select_time instead of using these singular frequencies in separate calls. The function will also return multiple time series in one dataset if select_time is NULL (as it is by default). If the dataset contains multiple time series and these are explicitly downloaded / no select_time parameter is given, a message will be printed. +* `eurotime2num` can now handle monthly and weekly data as well. +* Added a new parameter to `get_eurostat()` function: legacy_bulk_download (default = TRUE). By setting this parameter to FALSE the user can download data from the new dissemination API. If you want to test the new API before it becomes the only way to download the data (and we very much encourage you to do so), set this parameter to FALSE. + +## Minor updates + +* Removed render-rmarkdown.yaml workflow used for rendering README.md file. README.md must be generated locally from now on. + +# eurostat 3.7.13 (2023-02-01) + +* Updated `get_eurostat_json()` to migrate from JSON web service to API Statistics (addressed in issues #243, #251). Please note that the output from JSON API is now slightly different than before: the datasets now contain a freq column to indicate the frequency with which data has been collected, for example annually "A", monthly "M" or quarterly "Q". See Eurostat - Data browser online help website for more information: https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+migrating+from+JSON+web+service+to+API+Statistics +* Minor fixes in `get_bibentry()` and `get_eurostat_geospatial()` + +# eurostat 3.7.12 (2022-06-28) + +* Updated included dataset `eurostat_geodata_60_2016` to fix the issue of old-style crs object (#237) +* Added information about different variables in `eurostat_geodata_60_2016` so that the dataset is more understandable and usable for testing purposes. Added the same information to `get_eurostat_geospatial()` documentation as well. +* Added the GISCO copyright disclaimer to `eurostat_geodata_60_2016` and `get_eurostat_geospatial()` documentation. +* Get rid of unnecessary "No encoding supplied: defaulting to UTF-8." messages in `get_eurostat_geospatial()` by setting content encoding to UTF-8 when `httr::content()` function is called +* dplyr and tidyr namespaces are no longer imported completely, only selected few functions with importFrom + +# eurostat 3.7.10 (2022-02-09) + +* Fixed URL issues in tests and examples + +# eurostat 3.7.9 (2020-10-01) + +* Function documentation migrated from old `\code{}`, `\link{}` syntax to markdown (issue #230, PR #231 by @dieghernan) + +# eurostat 3.7.8 (2020-09-30) + +* Package cache management updated: `options()` command is no longer needed and the cache dir can be modified persistently with a custom function (issue #223, PR #228 by @dieghernan) + +# eurostat 3.7.7 (2020-06-24) +* Maps vignette fixed + +# eurostat 3.7.6 (2021-05-20) + +* Deprecated `add_nuts_level()`, `harmonize_geo_code()`, `recode_to_nuts_2016()` and `recode_to_nuts_2013()`; these functions were moved to the new package regions. The problem of sub-national geo codes is explained in the new vignette "Mapping Regional Data, Mapping Metadata Problems", which replaces the "Regional data examples for the eurostat R package" vignette. This is a shared vignette, but the new regions package has more articles on how to work with sub-national data. (issues #218 and #219, PR #220 by @antaldaniel) + +# eurostat 3.7.5 (2020-05-12) + +* Moved sf from Imports to Suggests and made `get_eurostat_geospatial()` return a message if sf is not installed. This is to increase compatibility of eurostat-package on systems that have trouble installing sf (issue #213) +* Wrapped some problem causing examples to `\dontrun{}` for a quick CRAN release + +# eurostat 3.7.3 + +* Removed outdated dependencies (mapproj, plotrix, rsdmx) + +# eurostat 3.7.2 + +* Non-intersecting sf-geometries in get_eurostat_geospatial (PR #202 by @retostauffer) + +# eurostat 3.6.4 (2020-05-12) + +* Fixed stringsAsFactors for R-4.0.0 and moved default to FALSE + +# eurostat 3.6.3 (2020-04-21) + +* Stabilized http requests (PR by @annnvv) + +# eurostat 3.5.3 + +* get_eurostat switched to v2.1 + +# eurostat 3.5.2 + +* internet and proxy setting fixes +* bibentry fix + +# eurostat 3.4.1 + +* Fixed vignette +* Added automated error messages to URL download failures + +# eurostat 3.3.3 + +* Countries and Country Codes data.frames get label column for country names in the Eurostat database. +* Fixed vignette duplicate entry issue and smaller issues +* Added get_bibentry + +# eurostat 3.3.1 + +* The `label_eurostat()` has new countrycode and countrycode_nomatch arguments to label with countrycode package and custom_dic argument to add custom dictionary. +* Vignette updated # eurostat 3.2.3 @@ -21,8 +176,7 @@ ## Minor features -* data/ generation script in data-raw/ updated to make all data reproducible - +* ./data/ generation script in ./data-raw/ updated to make all data reproducible ## Bug fixes @@ -32,19 +186,13 @@ ## Minor features -* Added new example data set to reduce repeated downloads from - eurostat service - -* Now `label_eurostat()` gives always an error by default, if labelling - introduces duplicated labels. A new `fix_duplicated` argument is add to fix - duplicated labels automatically. (#79, #90) - +* Added new example data set to reduce repeated downloads from eurostat service +* Now `label_eurostat()` gives always an error by default, if labelling introduces duplicated labels. A new `fix_duplicated` argument is add to fix duplicated labels automatically. (#79, #90) * Shrinked the package tarball size ## Bug fixes * Modified tutorial to accommodate the CRAN error - * Fixed cut_to_classes to generate unique breaks # eurostat 3.1.1 @@ -65,14 +213,10 @@ ## Bug fixes -* `search_eurostat()` accepts new argument `fixed`: if `TRUE` -(default), `pattern` provided will used as is; if `FALSE`, -`pattern` will be interpreted as a true regex pattern. -* Augmented the list of Suggested packages in the DESCRIPTION file, - including the Cairo package (#70) +* `search_eurostat()` accepts new argument `fixed`: if `TRUE` (default), `pattern` provided will used as is; if `FALSE`, `pattern` will be interpreted as a true regex pattern. +* Augmented the list of Suggested packages in the DESCRIPTION file, including the Cairo package (#70) * Updated the journal manuscript based on reviewer feedback - # eurostat 2.2.20001 * Development version opened @@ -107,30 +251,17 @@ # eurostat 1.2.21 -* The `get_eurostat()` can now get data also from the Eurostat JSON API via - `get_eurostat_json()`. It also have a new argument `type` to select labels - for variable values instead of codes. +* The `get_eurostat()` can now get data also from the Eurostat JSON API via `get_eurostat_json()`. It also have a new argument `type` to select labels for variable values instead of codes. * Fix an error after update to `tidyr 0.4.0` (#47). - # eurostat 1.2.13 -* New `select_time` argument for `get_eurostat()` to select a time frequency - in case of multi-frequency datasets. Now the `get_eurostat()` also gives an - error if you try to get multi-frequency with other time formats - than `time_format = "raw"`. (#30) `time` column is also now in ascending - order. -* `get_eurostat()` gets a new argument `compress_file` to control compression - of the cache file. Also cache filenames includes now all relevant arguments. (#28) +* New `select_time` argument for `get_eurostat()` to select a time frequency in case of multi-frequency datasets. Now the `get_eurostat()` also gives an error if you try to get multi-frequency with other time formats than `time_format = "raw"`. (#30) `time` column is also now in ascending order. +* `get_eurostat()` gets a new argument `compress_file` to control compression of the cache file. Also cache filenames includes now all relevant arguments. (#28) * For `search_eurostat()` a new type option `type = "all"` to search all types. -* For `label_eurostat()` new arguments. A `code` to retain also codes - for spesified colums. A `eu_order` to order factor levels in Eurostat order, - which uses the new function `dic_order()`. -* Now `label_eurostat_vars(x)` gives labels for names, if x is other than - a character or a factor and `label_eurostat_tables(x)` does not accept other - than a character or a factor. -* For `get_eurostat()` a new argument `stringsAsFactors` to control the - factor conversion of variables. +* For `label_eurostat()` new arguments. A `code` to retain also codes for specified columns. A `eu_order` to order factor levels in Eurostat order, which uses the new function `dic_order()`. +* Now `label_eurostat_vars(x)` gives labels for names, if x is other than a character or a factor and `label_eurostat_tables(x)` does not accept other than a character or a factor. +* For `get_eurostat()` a new argument `stringsAsFactors` to control the factor conversion of variables. * `eurotime2date` (and `get_eurostat`) convers now also daily data. # eurostat 1.0.16 @@ -146,4 +277,3 @@ # eurostat 0.9.1 (2014-04-24) * Package collected from statfi and smarterpoland - diff --git a/R/check_access_to_data.R b/R/check_access_to_data.R new file mode 100755 index 00000000..f5e26776 --- /dev/null +++ b/R/check_access_to_data.R @@ -0,0 +1,43 @@ +#' @title Check access to ec.europe.eu +#' @description Check if R has access to resources at http://ec.europa.eu +#' @author Markus Kainu +#' @return a logical. +#' @examples +#' \donttest{ +#' check_access_to_data() +#' } +#' +#' @importFrom curl curl_download +#' @importFrom utils download.file +#' +#' @export + +check_access_to_data <- function() { + temp <- tempfile() + http_url <- "https://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/geojson/NUTS_RG_60M_2006_4326_LEVL_0.geojson" + # If unix use curl::curl_download to test connection + # If windows use download.file with default method 'wininet' + if (.Platform$OS.type == "unix") { + suppressWarnings( + try( + curl::curl_download(http_url, temp, quiet = TRUE, ), + silent = TRUE + ) + ) + } else { + suppressWarnings( + try( + utils::download.file(http_url, + temp, + quiet = TRUE + ), + silent = TRUE + ) + ) + } + if (is.na(file.info(temp)$size)) { + FALSE + } else { + TRUE + } +} diff --git a/R/clean_eurostat_cache.R b/R/clean_eurostat_cache.R index 7e05b433..66ecb3cc 100755 --- a/R/clean_eurostat_cache.R +++ b/R/clean_eurostat_cache.R @@ -1,27 +1,59 @@ -#' @title Clean Eurostat Cache -#' @description Delete all .rds files from the eurostat cache directory. -#' See \code{\link{get_eurostat}} for more on cache. -#' @param cache_dir A path to cache directory. If \code{NULL} (default) -#' tries to clean default temporary cache directory. +#' @title +#' Clean Eurostat Cache +#' @description +#' Delete all .rds files from the eurostat cache directory. +#' See [get_eurostat()] for more on cache. +#' @param cache_dir +#' A path to cache directory. If `NULL` (default) +#' tries to clean default temporary cache directory. +#' @param config +#' Logical `TRUE/FALSE`. Should the cached path be deleted? +#' @family cache utilities +#' @author +#' Przemyslaw Biecek, Leo Lahti, Janne Huovari, Markus Kainu and +#' Diego Hernangómez +#' @examples +#' \dontrun{ +#' clean_eurostat_cache() +#' } #' @export -#' @author Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu \email{ropengov-forum@@googlegroups.com} \url{http://github.com/ropengov/eurostat} -#' @examples clean_eurostat_cache() -clean_eurostat_cache <- function(cache_dir = NULL){ - if (is.null(cache_dir)){ - cache_dir <- getOption("eurostat_cache_dir", file.path(tempdir(), "eurostat")) - if (!file.exists(cache_dir)) { - message("The cache does not exist") - return(invisible(TRUE)) - } +clean_eurostat_cache <- function(cache_dir = NULL, config = FALSE) { + + # Config + # nocov start + if (config) { + config_dir <- rappdirs::user_config_dir("eurostat", "R") + + if (dir.exists(config_dir)) { + unlink(config_dir, recursive = TRUE, force = TRUE) + message("eurostat cache config deleted") + } + + set_eurostat_cache_dir(file.path(tempdir(), "eurostat")) } - if (!file.exists(cache_dir)) stop("The cache folder ", cache_dir, " does not exist") - - files <- list.files(cache_dir, pattern = "rds", full.names = TRUE) + # nocov end + + cache_dir <- eur_helper_cachedir(cache_dir) + + if (!file.exists(cache_dir)) { + # nocov start + stop( + "The cache folder ", cache_dir, + " does not exist" + ) + # nocov end + } + + files <- list.files( + cache_dir, + pattern = "rds|RData|cache_list.json", + full.names = TRUE + ) if (length(files) == 0) { message("The cache folder ", cache_dir, " is empty.") } else { unlink(files) - message("Deleted .rds files from ", cache_dir) + message("Deleted .rds/.RData files and cache_list.json from ", cache_dir) } invisible(TRUE) } diff --git a/R/cut_to_classes.R b/R/cut_to_classes.R index 658fb3a8..50c38ca1 100755 --- a/R/cut_to_classes.R +++ b/R/cut_to_classes.R @@ -1,92 +1,106 @@ #' @title Cuts the Values Column into Classes and Polishes the Labels -#' @description Categorises a numeric vector into automatic or manually defined categories. -#' and polishes the labels ready for used in mapping with \code{merge_with_geodata} function and ggplot2. -#' @param x A numeric vector, eg. \code{values} variable in data returned by \code{get_eurostat} +#' @description Categorises a numeric vector into automatic or manually defined +#' categories and polishes the labels ready for used in mapping with `ggplot2`. +#' @param x A numeric vector, eg. `values` variable in data returned by +#' [get_eurostat()]. #' @param n A numeric. number of classes/categories -#' @param style Chosen style: one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", -#' "bclust", "fisher", or "jenks" #' @param manual Logical. If manual breaks are being used #' @param manual_breaks Numeric vector with manual threshold values #' @param decimals Number of decimals to include with labels #' @param nodata_label String. Text label for NA category. -#' @export +#' @inheritParams classInt::classIntervals #' @author Markus Kainu #' @return a factor. -#' @examples -#' \dontrun{ -#' lp <- get_eurostat("nama_aux_lp") -#' lp$class <- cut_to_classes(lp$values, n=5, style="equal", decimals=1) -#' } -cut_to_classes <- function(x, n=5, style="equal", manual=FALSE, - manual_breaks = NULL, decimals=0, - nodata_label="No data") { - - manual_breaks_orig <- manual_breaks - if (!is.null(manual_breaks) && (length(unique(manual_breaks)) == length(manual_breaks))) { - warning("manual_breaks in cut_to_classes are not unique. Using unique breaks only.") +#' @examplesIf check_access_to_data() +#' \donttest{ +#' # lp <- get_eurostat("nama_aux_lp") +#' lp <- get_eurostat("nama_10_lp_ulc") +#' lp$class <- cut_to_classes(lp$values, n = 5, style = "equal", decimals = 1) +#' } +#' +#' @importFrom classInt classIntervals +#' @importFrom stringr str_replace_all +#' @seealso [classInt::classIntervals()] +#' @family helpers +#' @export +cut_to_classes <- function(x, n = 5, style = "equal", manual = FALSE, + manual_breaks = NULL, decimals = 0, + nodata_label = "No data") { + # manual_breaks_orig <- manual_breaks + if (!is.null(manual_breaks) && + (length(unique(manual_breaks)) == length(manual_breaks))) { + warning(paste("manual_breaks in cut_to_classes are not unique.", + "Using unique breaks only.")) manual_breaks <- unique(manual_breaks) - } if (manual) { - levs <- as.data.frame(levels(cut(x, - breaks=manual_breaks, - include.lowest=T, - dig.lab=5))) + levs <- as.data.frame( + levels( + cut( + x, + breaks = manual_breaks, + include.lowest = TRUE, + dig.lab = 5 + ) + ) + ) } else { - - brs <- data.frame(classInt::classIntervals(x,n=n,style=style)[2])[,1] + brs <- data.frame(classInt::classIntervals(x, n = n, style = style)[2])[, 1] # Ensure that the breaks are unique brs <- unique(brs) - cutting <- cut(x, breaks=brs, include.lowest=T, dig.lab=5) + cutting <- cut(x, breaks = brs, include.lowest = TRUE, dig.lab = 5) levs <- levels(cutting) levs <- as.data.frame(levs) - } names(levs) <- "orig" - levs$mod <- str_replace_all(levs$orig, "\\[", "") - levs$mod <- str_replace_all(levs$mod, "\\]", "") - levs$mod <- str_replace_all(levs$mod, "\\(", "") - levs$lower <- gsub(",.*$","", levs$mod) - levs$upper <- gsub(".*,","", levs$mod) - + levs$mod <- stringr::str_replace_all(levs$orig, "\\[", "") + levs$mod <- stringr::str_replace_all(levs$mod, "\\]", "") + levs$mod <- stringr::str_replace_all(levs$mod, "\\(", "") + levs$lower <- gsub(",.*$", "", levs$mod) + levs$upper <- gsub(".*,", "", levs$mod) + levs$lower <- factor(levs$lower) - levs$lower <- round(as.numeric(levels(levs$lower))[levs$lower],decimals) - levs$lower <- prettyNum(levs$lower, big.mark=" ") - + levs$lower <- round(as.numeric(levels(levs$lower))[levs$lower], decimals) + levs$lower <- prettyNum(levs$lower, big.mark = " ") + levs$upper <- factor(levs$upper) - levs$upper <- round(as.numeric(levels(levs$upper))[levs$upper],decimals) - levs$upper <- prettyNum(levs$upper, big.mark=" ") - - levs$labs <- paste(levs$lower,levs$upper, sep=" ~< ") + levs$upper <- round(as.numeric(levels(levs$upper))[levs$upper], decimals) + levs$upper <- prettyNum(levs$upper, big.mark = " ") + + levs$labs <- paste(levs$lower, levs$upper, sep = " ~< ") labs <- as.character(c(levs$labs)) if (manual) { - y <- cut(x, breaks = manual_breaks, - include.lowest=T, - dig.lab=5, labels = labs) + y <- cut(x, + breaks = manual_breaks, + include.lowest = TRUE, + dig.lab = 5, labels = labs + ) rm(manual_breaks) } else { - brs2 <- data.frame(classIntervals(x,n = n, style = style)[2])[,1] + brs2 <- data.frame(classIntervals(x, n = n, style = style)[2])[, 1] # Ensure the breaks are unique brs2 <- unique(brs2) - y <- cut(x, breaks = brs2, - include.lowest=T, - dig.lab=5, labels = labs) + y <- cut(x, + breaks = brs2, + include.lowest = TRUE, + dig.lab = 5, labels = labs + ) } y <- as.character(y) - + y[is.na(y)] <- nodata_label - - y <- factor(y, levels=c(nodata_label,labs[1:n])) - + + y <- factor(y, levels = c(nodata_label, labs[1:n])) + y -} \ No newline at end of file +} diff --git a/R/data.R b/R/data.R index 1c10eb17..c9b88754 100755 --- a/R/data.R +++ b/R/data.R @@ -1,7 +1,10 @@ #' @title Auxiliary Data #' @description Auxiliary Data Sets -#' @details Retrieved with: \code{tgs00026 <- get_eurostat("tgs00026", time_format = "raw") } +#' @family datasets +#' @details +#' Disposable income of private households by NUTS 2 regions +#' Retrieved with: `tgs00026 <- get_eurostat("tgs00026", time_format = "raw") ` +#' Data retrieval date: 2022-06-27 #' @format data_frame #' @rdname tgs00026 -"tgs00026" - +"tgs00026" diff --git a/R/data_countries.R b/R/data_countries.R index 61044622..7d09d677 100755 --- a/R/data_countries.R +++ b/R/data_countries.R @@ -1,17 +1,18 @@ -#' @title Countries and Country Codes +#' @title Countries and Country Codes #' @description Countries and country codes in EU, #' Euro area, EFTA and EU candidate countries. #' @format A data_frame: -#' \describe{ -#' \item{code}{Country code in the Eurostat database} -#' \item{name}{Country name in English} -#' } -#' @source \url{http://ec.europa.eu/eurostat/statistics-explained/index.php/Tutorial:Country_codes_and_protocol_order}, -#' \url{http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Euro_area} -"eu_countries" +#' * **code**: Country code in the Eurostat database. +#' * **name**: Country name in English. +#' * **label**: Country name in the Eurostat database. +#' +#' @family datasets +#' @source , +#' +"eu_countries" #' @rdname eu_countries -"ea_countries" +"ea_countries" #' @rdname eu_countries "efta_countries" diff --git a/R/data_spatial.R b/R/data_spatial.R index c38d9c19..a1aacf8a 100755 --- a/R/data_spatial.R +++ b/R/data_spatial.R @@ -1,69 +1,128 @@ -#' @title Geospatial data of Europe from Gisco in 1:60 million scale from year 2016 -#' @description Geospatial data of Europe from Gisco in 1:60 million scale from year 2016 -#' @format sf -#' \describe{ -#' \item{id}{Country code in the Eurostat database} -#' \item{CNTRY_CODE}{Country code} -#' \item{NUTS_NAME}{NUTS name in local language} -#' \item{LEVL_CODE}{NUTS code} -#' \item{FID}{Country code} -#' \item{NUTS_ID}{NUTS code} -#' \item{geometry}{geospatial information} -#' \item{geo}{NUTS code} +#' Geospatial data of Europe from GISCO in 1:60 million scale from +#' year 2016 +#' +#' @description Geospatial data of Europe from GISCO in 1:60 million scale +#' from year 2016 +#' +#' @family datasets +#' @family geospatial +#' @format sf object +#' @docType data +#' @name eurostat_geodata_60_2016 +#' +#' @details +#' The dataset contains 2016 observations (rows) and 12 variables (columns). +#' +#' The object contains the following columns: +#' * **id**: JSON id code, the same as **NUTS_ID**. See **NUTS_ID** below for +#' further clarification. +#' * **LEVL_CODE**: NUTS level code: 0 (national level), 1 (major +#' socio-economic regions), 2 (basic regions for the application of regional +#' policies) or 3 (small regions). +#' * **NUTS_ID**: NUTS ID code, consisting of country code and numbers (1 for +#' NUTS 1, 2 for NUTS 2 and 3 for NUTS 3) +#' * **CNTR_CODE**: Country code: two-letter ISO code (ISO 3166 alpha-2), except +#' in the case of Greece (EL). +#' * **NAME_LATN**: NUTS name in local language, transliterated to Latin script +#' * **NUTS_NAME**: NUTS name in local language, in local script. +#' * **MOUNT_TYPE**: Mountain typology for NUTS 3 regions. +#' * 1: "where more than 50 % of the surface is covered by topographic +#' mountain areas" +#' * 2: "in which more than 50 % of the regional population lives in +#' topographic mountain areas" +#' * 3: "where more than 50 % of the surface is covered by topographic +#' mountain areas and where more than 50 % of the regional population lives +#' in these mountain areas" +#' * 4: non-mountain region / other region +#' * 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 and +#' non-EU countries) +#' * **URBN_TYPE**: Urban-rural typology for NUTS 3 regions. +#' * 1: predominantly urban region +#' * 2: intermediate region +#' * 3: predominantly rural region +#' * 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 +#' regions) +#' * **COAST_TYPE**: Coastal typology for NUTS 3 regions. +#' * 1: coastal (on coast) +#' * 2: coastal (>= 50% of population living within 50km of the coastline) +#' * 3: non-coastal region +#' * 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 +#' regions) +#' * **FID**: Same as NUTS_ID. +#' * **geo**: Same as NUTS_ID, added for for easier joins with dplyr. However, +#' it is recommended to use other identical fields for this purpose. +#' * **geometry**: geospatial information. +#' +#' Dataset updated: 2023-06-29. For a more recent version, please use +#' [giscoR::gisco_get_nuts()] function. +#' +#' @source +#' Data source: Eurostat via [giscoR::gisco_get_nuts()]. +#' +#' © EuroGeographics for the administrative boundaries +#' +#' Data downloaded from: +#' +#' +#' @references +#' The following copyright notice is provided for end user convenience. +#' Please check up-to-date copyright information from the eurostat website: +#' [GISCO: Geographical information and maps - Administrative units/statistical units](https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units) +#' +#' "In addition to the +#' [general copyright and licence policy](https://ec.europa.eu/eurostat/web/main/about/policies/copyright) +#' applicable to the whole Eurostat website, the following +#' specific provisions apply to the datasets you are downloading. The download +#' and usage of these data is subject to the acceptance of the following +#' clauses: +#' +#' 1. The Commission agrees to grant the non-exclusive and not transferable +#' right to use and process the Eurostat/GISCO geographical data downloaded +#' from this page (the "data"). +#' +#' 1. The permission to use the data is granted on condition that: +#' 1. the data will not be used for commercial purposes; +#' 2. the source will be acknowledged. A copyright notice, as specified +#' below, will have to be visible on any printed or electronic publication +#' using the data downloaded from this page. +#' +#' ## Copyright notice +#' +#' When data downloaded from this page is used in any printed or electronic +#' publication, in addition to any other provisions applicable to the whole +#' Eurostat website, data source will have to be acknowledged in the legend of +#' the map and in the introductory page of the publication with the following +#' copyright notice: +#' +#' EN: © EuroGeographics for the administrative boundaries +#' +#' FR: © EuroGeographics pour les limites administratives +#' +#' DE: © EuroGeographics bezüglich der Verwaltungsgrenzen +#' +#' For publications in languages other than English, French or German, the +#' translation of the copyright notice in the language of the publication shall +#' be used. +#' +#' If you intend to use the data commercially, please contact EuroGeographics +#' for information regarding their licence agreements." +#' +#' @seealso [giscoR::gisco_get_nuts()] and +#' [Eurostat. (2019). Methodological manual on territorial typologies -- 2018 edition. Manuals and guidelines.](https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-gq-18-008) +#' +#' +#' @examples +#' +#' eurostat_geodata_60_2016 <- eurostat::eurostat_geodata_60_2016 +#' +#' # Manipulate and plot +#' if (require(sf)) { +#' library(sf) +#' # Filter NUTS3 from select countries like in a regular data frame +#' example_nuts <- subset(eurostat_geodata_60_2016, LEVL_CODE == 3 & +#' CNTR_CODE %in% c("DK", "DE", "PL")) +#' +#' plot(example_nuts["CNTR_CODE"]) #' } -#' @source \url{http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units} -"eurostat_geodata_60_2016" - - -## -- 2016 -# library(sf) -# library(httr) -# -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/geojson/NUTS_RG_03M_2016_4258_LEVL_0.geojson") -# n0 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/geojson/NUTS_RG_03M_2016_4258_LEVL_1.geojson") -# n1 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/geojson/NUTS_RG_03M_2016_4258_LEVL_2.geojson") -# n2 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/geojson/NUTS_RG_03M_2016_4258_LEVL_3.geojson") -# n3 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# eurostat_geodata_60_2016 <- rbind(n0,n1,n2,n3) -# eurostat_geodata_60_2016$geo <- eurostat_geodata_60_2016$NUTS_ID -# save(eurostat_geodata_60_2016, file = "./data/eurostat_geodata_60_2016.rda") - - -## -- 2013 -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2013_4258_LEVL_0.geojson") -# n0 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2013_4258_LEVL_1.geojson") -# n1 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2013_4258_LEVL_2.geojson") -# n2 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2013_4258_LEVL_3.geojson") -# n3 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# eurostat_geodata_60_2013 <- rbind(n0,n1,n2,n3) -# save(eurostat_geodata_60_2013, file = "./data/eurostat_geodata_60_2013.rda") - -## --2010 -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2010_4258_LEVL_0.geojson") -# n0 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2010_4258_LEVL_1.geojson") -# n1 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2010_4258_LEVL_2.geojson") -# n2 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2010_4258_LEVL_3.geojson") -# n3 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# eurostat_geodata_60_2010 <- rbind(n0,n1,n2,n3) -# save(eurostat_geodata_60_2010, file = "./data/eurostat_geodata_60_2010.rda") - -## --2006 -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2006_4258_LEVL_0.geojson") -# n0 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2006_4258_LEVL_1.geojson") -# n1 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2006_4258_LEVL_2.geojson") -# n2 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# resp <- GET("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v1/geojson/NUTS_RG_60M_2006_4258_LEVL_3.geojson") -# n3 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) -# eurostat_geodata_60_2006 <- rbind(n0,n1,n2,n3) -# save(eurostat_geodata_60_2006, file = "./data/eurostat_geodata_60_2006.rda") +#' +NULL diff --git a/R/deprecated_regions_functions.R b/R/deprecated_regions_functions.R new file mode 100755 index 00000000..533e00f7 --- /dev/null +++ b/R/deprecated_regions_functions.R @@ -0,0 +1,198 @@ +#' DEPRECATED FUNCTIONS FOR BACKWARD COMPATIBILITY +#' FUNCTIONS GIVE WARNING AND CALL APPROPRIATE regions FUNCTIONS + +#' @title Add the statistical aggregation level to data frame +#' @description Eurostat regional statistics contain country, and various +#' regional level information. In many cases, for example, when mapping, +#' it is useful to filter out national level data from NUTS2 level regional +#' data, for example. +#' +#' This function will be deprecated. Use the more comprehensive +#' `[regions::validate_nuts_regions()]` instead. +#' +#' @param dat A data frame or tibble returned by [get_eurostat()]. +#' @param geo_labels A geographical label, defaults to `geo`. +#' @author Daniel Antal +#' @return a new numeric variable nuts_level with the numeric value of +#' NUTS level 0 (country), 1 (greater region), +#' 2 (region), 3 (small region). +#' @keywords internal +#' @family regions functions +#' @seealso [regions::validate_nuts_regions()] +#' @examples +#' +#' dat <- data.frame( +#' geo = c("FR", "IE04", "DEB1C"), +#' values = c(1000, 23, 12) +#' ) +#' +#' add_nuts_level(dat) +#' @importFrom dplyr mutate case_when +#' +#' @export + +add_nuts_level <- function(dat, geo_labels = "geo") { + message("This function will be deprecated. Use regions::validate_nuts_regions() instead.") + + if (any(c("character", "factor") %in% class(dat))) { + input <- "label" + geo <- dat + dat <- data.frame( + geo = as.character(geo), + stringsAsFactors = FALSE + ) + } else { + input <- "not_label" + } + + + if ("data.frame" %in% class(dat)) { + if (!geo_labels %in% names(dat)) { + stop( + "Regional labelling variable '", + geo_labels, + "' is not found in the data frame." + ) + } + + dat <- regions::validate_nuts_regions(dat) + dat$nuts_level <- unlist(lapply(dat$typology, switch, + country = 0, + nuts_level_1 = 1, + nuts_level_2 = 2, + nuts_level_3 = 3 + )) + } + + if (input == "label") { + as.numeric(dat$nuts_level) + } else { + dat + } +} + +#' @title Harmonize NUTS region codes that changed with the `NUTS2016` +#' definition +#' @description Eurostat mixes `NUTS2013` and `NUTS2016` geographic +#' label codes in the `'geo'` column, which creates time-wise comparativity +#' issues. This deprecated function checked if you data is affected by this +#' problem and gives information on what to do. +#' +#' This function is deprecated, and a more general function was moved to +#' [regions::validate_nuts_regions()]. +#' +#' @param dat A Eurostat data frame downloaded with [get_eurostat()] +#' @author Daniel Antal +#' @return An augmented data frame that explains potential problems and +#' possible solutions. +#' @importFrom regions validate_nuts_regions +#' @examples +#' dat <- eurostat::tgs00026 +#' regions::validate_nuts_regions(dat) +#' @family regions functions +#' @keywords internal +#' @seealso [regions::validate_nuts_regions()] +#' @export + +harmonize_geo_code <- function(dat) { + warning("The 'harmonize_geo_code' function is deprecated. Use instead regions::validate_nuts_regions(dat, nuts_year = 2016)") + + regions::validate_nuts_regions(dat, nuts_year = 2016) +} + + +#' @title Recode geo labels and rename regions from NUTS2013 to NUTS2016 +#' @description Eurostat mixes NUTS2013 and NUTS2016 geographic label codes +#' in the `'geo'` column, which creates time-wise comparativity issues. +#' +#' This function is deprecated, and a more general function was moved to +#' `[regions::recode_nuts()]`. +#' +#' @param dat A Eurostat data frame downloaded with +#' [get_eurostat()]. +#' @author Daniel Antal +#' @return An augmented and potentially relabelled data frame which +#' contains all formerly `'NUTS2013'` definition geo labels in the +#' `'NUTS2016'` vocabulary when only the code changed, but the +#' boundary did not. It also contains some information on other geo labels +#' that cannot be brought to the current `'NUTS2016'` definition. +#' Furthermore, when the official name of the region changed, it will use +#' the new name (if the otherwise the region boundary did not change.) +#' If not called before, the function will use the helper function +#' [harmonize_geo_code()] +#' @examples +#' test_regional_codes <- data.frame( +#' geo = c("FRB", "FRE", "UKN02", "IE022", "FR243", "FRB03"), +#' time = c(rep(as.Date("2014-01-01"), 5), as.Date("2015-01-01")), +#' values = c(1:6), +#' control = c( +#' "Changed from NUTS2 to NUTS1", +#' "New region NUTS2016 only", +#' "Discontinued region NUTS2013", +#' "Boundary shift NUTS2013", +#' "Recoded in NUTS2013", +#' "Recoded in NUTS2016" +#' ) +#' ) +#' +#' recode_to_nuts_2016(test_regional_codes) +#' @importFrom regions recode_nuts +#' @family regions functions +#' @seealso [regions::recode_nuts()] +#' @keywords internal +#' @export + +recode_to_nuts_2016 <- function(dat) { + warning("The 'recode_to_nuts_2013' function is deprecated. Use instead regions::recode_nuts(dat, nuts_year = 2016)") + + regions::recode_nuts(dat, nuts_year = 2016) +} + + + +#' @title Recode geo labels and rename regions from NUTS2016 to NUTS2013 +#' @description Eurostat mixes NUTS2013 and NUTS2016 geographic label codes +#' in the `'geo'` column, which creates time-wise comparativity issues. +#' +#' This function is deprecated, and a more general function was moved to +#' `[regions::recode_nuts()]`. +#' +#' @param dat A Eurostat data frame downloaded with +#' [get_eurostat()]. +#' @author Daniel Antal +#' @return An augmented and potentially relabelled data frame which +#' contains all formerly `'NUTS2013'` definition geo labels in the +#' `'NUTS2016'` vocabulary when only the code changed, but the +#' boundary did not. It also contains some information on other geo labels +#' that cannot be brought to the current `'NUTS2013'` definition. +#' Furthermore, when the official name of the region changed, it will use +#' the new name (if the otherwise the region boundary did not change.) +#' If not called before, the function will use the helper function +#' [harmonize_geo_code()] +#' @examples +#' test_regional_codes <- data.frame( +#' geo = c("FRB", "FRE", "UKN02", "IE022", "FR243", "FRB03"), +#' time = c(rep(as.Date("2014-01-01"), 5), as.Date("2015-01-01")), +#' values = c(1:6), +#' control = c( +#' "Changed from NUTS2 to NUTS1", +#' "New region NUTS2016 only", +#' "Discontinued region NUTS2013", +#' "Boundary shift NUTS2013", +#' "Recoded in NUTS2013", +#' "Recoded in NUTS2016" +#' ) +#' ) +#' +#' recode_to_nuts_2013(test_regional_codes) +#' @importFrom regions recode_nuts +#' @family regions functions +#' @seealso [regions::recode_nuts()] +#' @keywords internal +#' @export + +recode_to_nuts_2013 <- function(dat) { + warning("The 'recode_to_nuts_2013' function is deprecated. Use instead regions::recode_nuts(dat, nuts_year = 2013)") + + recode_nuts(dat, nuts_year = 2013) +} diff --git a/R/dic_order.R b/R/dic_order.R index 0171478e..0acaea6c 100755 --- a/R/dic_order.R +++ b/R/dic_order.R @@ -1,22 +1,30 @@ #' @title Order of Variable Levels from Eurostat Dictionary. #' @description Orders the factor levels. -#' @details Some variables, like classifications, have logical or conventional ordering. Eurostat data tables are nor neccessary ordered in this order. The function \code{dic_order} get the ordering from Eurostat classifications dictionaries. The function \code{\link{label_eurostat}} can also order factor levels of labels with argument \code{eu_order = TRUE}. -#' +#' @details Some variables, like classifications, have logical or conventional +#' ordering. Eurostat data tables are nor necessary ordered in this order. +#' The function [dic_order()] get the ordering from Eurostat classifications +#' dictionaries. The function [label_eurostat()] can also order factor levels +#' of labels with argument `eu_order = TRUE`. +#' #' @param x a variable (code or labelled) to get order for. -#' @param dic a name of the dictionary. Correspond a variable name in the -#' data_frame from \code{\link{get_eurostat}}. Can be also data_frame from -#' \code{\link{get_eurostat_dic}}. -#' @param type a type of the x. Could be \code{code} or \code{label}. -#' @export -#' @author Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu \email{ropengov-forum@@googlegroups.com} \url{http://github.com/ropengov/eurostat} +#' @param dic a name of the dictionary. Correspond a variable name in the +#' data_frame from [get_eurostat()]. Can be also data_frame from +#' [get_eurostat_dic()]. +#' @param type a type of the x. Could be `code` or `label`. +#' @author Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu #' @return A numeric vector of orders. +#' +#' @importFrom tibble is_tibble +#' @family helpers +#' +#' @export dic_order <- function(x, dic, type) { - if (!is_tibble(dic)) dic <- get_eurostat_dic(dic) - + if (!tibble::is_tibble(dic)) dic <- get_eurostat_dic(dic) + # code or label n_type <- match(type, c("code", "label")) if (is.na(n_type)) stop("Invalid type.") - + # get order y <- order(match(x, dic[[n_type]])) if (any(is.na(y))) stop("All orders were not found.") diff --git a/R/eurostat-defunct.R b/R/eurostat-defunct.R new file mode 100644 index 00000000..9837831c --- /dev/null +++ b/R/eurostat-defunct.R @@ -0,0 +1,26 @@ +#' @title Defunct functions in eurostat +#' +#' @description +#' This list of defunct functions is maintained to document changes to eurostat functions in a +#' transparent manner. +#' +#' @param ... Generic representation of old arguments +#' +#' @name eurostat-defunct +#' @aliases eurostat-defunct +#' +#' @details +#' The following functions are defunct: +#' +#' \itemize{ +#' \item \code{\link{grepEurostatTOC}}: Use \code{search_eurostat} instead +#' } +#' +#' +NULL + +#' @rdname eurostat-defunct +#' @export +grepEurostatTOC <- function(...) { + .Defunct(new = "search_eurostat") +} \ No newline at end of file diff --git a/R/eurostat-package.R b/R/eurostat-package.R index 27c24013..7f231408 100755 --- a/R/eurostat-package.R +++ b/R/eurostat-package.R @@ -1,25 +1,342 @@ -#' Brief summary of the eurostat package -#' -#' \tabular{ll}{ -#' Package: \tab eurostat\cr -#' Type: \tab Package\cr -#' Version: \tab See sessionInfo() or DESCRIPTION file\cr -#' Date: \tab 2014-2017\cr -#' License: \tab BSD_2_clause + LICENSE\cr -#' LazyLoad: \tab yes\cr -#' } +#' @title R Tools for Eurostat open data +#' +#' @description +#' Tools to download data from the Eurostat database +#' together with search and manipulation +#' utilities. +#' +#' @details #' -#' R Tools for Eurostat Open Data +#' | | | +#' | :--- | :-- | +#' | **Package** | eurostat | +#' | **Type** | Package | +#' | **Version** | `r packageVersion("eurostat")` | +#' | **Date** | 2014-2023 | +#' | **License** | `r as.character(utils::packageDescription("eurostat")["License"])`| +#' | **LazyLoad** | yes | #' #' @name eurostat-package #' @aliases eurostat #' @docType package -#' @title R Tools for Eurostat open data #' -#' @author Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek \email{ropengov-forum@@googlegroups.com} \url{https://ropengov.github.io/eurostat} +#' @author Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek #' @references -#' See citation("eurostat") -#' \url{https://ropengov.github.io/eurostat} +#' See `citation("eurostat")`: +#' +#' ```{r, echo=FALSE, comment="" } +#' citation("eurostat") +#' ``` +#' +#' When citing data downloaded from Eurostat, see section "Citing Eurostat data" +#' in [get_eurostat()] documentation. +#' +#' @details +#' # Eurostat +#' Eurostat website: \url{https://ec.europa.eu/eurostat} +#' Eurostat database: \url{https://ec.europa.eu/eurostat/web/main/data/database} +#' +#' Information about the data update schedule from Eurostat: +#' "Eurostat datasets are updated twice a day at 11:00 and 23:00 CET, if newer +#' data is available or for structural changes, for example for the +#' dimensions in the dataset. +#' +#' The Eurostat database always contains the latest version of the datasets, +#' meaning that there is no versioning or documentation of past versions of +#' the data." +#' +#' # Data source: Eurostat SDMX 2.1 Dissemination API +#' +#' Data is downloaded from Eurostat SDMX 2.1 API endpoint +#' as compressed TSV files that are transformed into tabular format. +#' See Eurostat documentation for more information: +#' \url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+data+query} +#' +#' The new dissemination API replaces the old bulk download facility that was +#' used by Eurostat before October 2023 and by the eurostat R package versions +#' before 4.0.0. +#' See Eurostat documentation about the transition from Bulk Download to API +#' for more information about the differences between the old bulk download +#' facility and the data provided by the new API connection: +#' \url{https://wikis.ec.europa.eu/display/EUROSTATHELP/Transition+-+from+Eurostat+Bulk+Download+to+API} +#' +#' See especially the document Migrating_to_API_TSV.pdf that describes the +#' changes in TSV file format in new applications. +#' +#' For more information about SDMX 2.1, see SDMX standards: Section 7: +#' Guidelines for the use of web services, Version 2.1: +#' \url{https://sdmx.org/wp-content/uploads/SDMX_2-1_SECTION_7_WebServicesGuidelines.pdf} +#' +#' # Disclaimer: Availability of filtering functionalities +#' +#' Currently it only possible to download filtered data through API Statistics +#' (JSON API) when using `eurostat` package, although technically filtering +#' datasets downloaded through the SDMX Dissemination API is also supported by +#' Eurostat. We may support this feature in the future. In the meantime, if you +#' are interested in filtering Dissemination API data queries manually, please +#' consult the following Eurostat documentation: +#' \url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+data+filtering} +#' +#' # Data source: Eurostat API Statistics (JSON API) +#' +#' Data is downloaded from Eurostat API Statistics. See Eurostat documentation +#' for more information about data queries in API Statistics +#' \url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query} +#' +#' This replaces the old JSON Web Services that was used by Eurostat before +#' February 2023 and by the eurostat R package versions before 3.7.13. +#' See Eurostat documentation about the migration from JSON web service to API +#' Statistics for more information about the differences between the old and +#' the new service: +#' \url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+migrating+from+JSON+web+service+to+API+Statistics} +#' +#' For easily viewing which filtering options are available - in addition to +#' the default ones, time and language - Eurostat Web services Query builder +#' tool may be useful: +#' \url{https://ec.europa.eu/eurostat/web/query-builder} +#' +#' # Filtering datasets +#' +#' When using Eurostat API Statistics (JSON API), datasets can be filtered +#' before they are downloaded and saved in local memory. The general format +#' for filter parameters is `=`. +#' +#' Filter parameters are optional but the used dimension codes must be present +#' in the data product that is being queried. Dimension codes can +#' vary between different data products so it may be useful to examine new +#' datasets in Eurostat data browser beforehand. However, most if not all +#' Eurostat datasets concern European countries and contain information that +#' was gathered at some point in time, so `geo` and `time` dimension codes +#' can usually be used. +#' +#' `` and `` are case-insensitive and they can be written +#' in lowercase or uppercase in the query. +#' +#' Parameters are passed onto the `eurostat` package functions [get_eurostat()] +#' and [get_eurostat_json()] as a list item. If an individual item contains +#' multiple items, as it often can be in the case of `geo` parameters and +#' other optional items, they must be in the form of a vector: `c("FI", "SE")`. +#' For examples on how to use these parameters, see function examples below. +#' +#' ## Time parameters +#' +#' `time` and `time_period` address the same `TIME_PERIOD` dimension in the +#' dataset and can be used interchangeably. In the Eurostat documentation +#' it is stated that "Using more than one Time parameter in the same query +#' is not accepted", but practice has shown that actually Eurostat API allows +#' multiple `time` parameters in the same query. This makes it possible to +#' use R colon operator when writing queries, so `time = c(2015:2018)` +#' translates to `&time=2015&time=2016&time=2017&time=2018`. +#' +#' The only exception +#' to this is when the queried dataset contains e.g. quarterly data and +#' `TIME_PERIOD` is saved as `2015-Q1`, `2015-Q2` etc. Then it is possible +#' to use `time=2015-Q1&time=2015-Q2` style in the query URL, but this makes it +#' unfeasible to use the colon operator and requires a lot of manual typing. +#' +#' Because of this, it is useful to know about other time parameters as well: +#' * `untilTimePeriod`: return dataset items from the oldest record up until the +#' set time, for example "all data until 2000": `untilTimePeriod = 2000` +#' * `sinceTimePeriod`: return dataset items starting from set time, for example +#' "all datastarting from 2008": `sinceTimePeriod = 2008` +#' * `lastTimePeriod`: starting from the most recent time period, how many +#' preceding time periods should be returned? For example 10 most +#' recent observations: `lastTimePeriod = 10` +#' +#' Using both `untilTimePeriod` and `sinceTimePeriod` parameters in the same +#' query is allowed, making the usage of the R colon operator unnecessary. +#' In the case of quarterly data, using `untilTimePeriod` and `sinceTimePeriod` +#' parameters also works, as opposed to the colon operator, so it is generally +#' safer to use them as well. +#' +#' ## Other dimensions +#' +#' In [get_eurostat_json()] examples `nama_10_gdp` dataset is filtered with +#' two additional filter parameters: +#' * `na_item = "B1GQ"` +#' * `unit = "CLV_I10"` +#' +#' Filters like these are most likely unique to the `nama_10_gdp` dataset +#' (or other datasets within the same domain) and should +#' not be used with others dataset without user discretion. +#' By using [label_eurostat()] we know that `"B1GQ"` stands for +#' "Gross domestic product at market prices" and +#' `"CLV_I10"` means "Chain linked volumes, index 2010=100". +#' +#' Different dimension codes can be translated to a natural language by using +#' the [get_eurostat_dic()] function, which returns labels for individual +#' dimension items such as `na_item` and `unit`, as opposed to +#' [label_eurostat()] which does it for whole datasets. For example, the +#' parameter `na_item` stands for "National accounts indicator (ESA 2010)" and +#' `unit` stands for "Unit of measure". +#' +#' ## Language +#' +#' All datasets have metadata available in English, French and German. If no +#' parameter is given, the labels are returned in English. +#' +#' Example: +#' * `lang = "fr"` +#' +#' ## More information +#' +#' For more information about data filtering see Eurostat documentation +#' on API Statistics: +#' \url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query#APIStatisticsdataquery-TheparametersdefinedintheRESTrequest} +#' +#' # Data source: Eurostat Table of Contents +#' +#' The Eurostat Table of Contents (TOC) is downloaded from +#' \url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=en} +#' (default) or from French or German language variants: +#' \url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=fr} +#' \url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=de} +#' +#' See Eurostat documentation on TOC items: +#' \url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+-+Detailed+guidelines+-+Catalogue+API+-+TOC} +#' +#' # Data source: GISCO - General Copyright +#' +#' "Eurostat's general copyright notice and licence policy is applicable and +#' can be consulted here: +#' +#' +#' Please also be aware of the European Commission's general conditions: +#' +#' +#' Moreover, there are specific provisions applicable to some of the following +#' datasets available for downloading. The download and usage of these data +#' is subject to their acceptance: +#' +#' * Administrative Units / Statistical Units +#' * Population distribution / Demography +#' * Transport Networks +#' * Land Cover +#' * Elevation (DEM)" +#' +#' Of the abovementioned datasets, Administrative Units / Statistical Units +#' is applicable if the user wants to draw maps with borders provided by +#' GISCO / EuroGeographics. +#' +#' # Data source: GISCO - Administrative Units / Statistical Units +#' +#' The following copyright notice is provided for end user convenience. +#' Please check up-to-date copyright information from the GISCO website: +#' [GISCO: Geographical information and maps - Administrative units/statistical units](https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units) +#' +#' "In addition to the [general copyright and licence policy](https://ec.europa.eu/eurostat/web/main/about/policies/copyright) applicable to the whole Eurostat website, the following +#' specific provisions apply to the datasets you are downloading. The download +#' and usage of these data is subject to the acceptance of the following +#' clauses: +#' +#' 1. The Commission agrees to grant the non-exclusive and not transferable +#' right to use and process the Eurostat/GISCO geographical data downloaded +#' from this page (the "data"). +#' +#' 1. The permission to use the data is granted on condition that: +#' 1. the data will not be used for commercial purposes; +#' 2. the source will be acknowledged. A copyright notice, as specified +#' below, will have to be visible on any printed or electronic publication +#' using the data downloaded from this page." +#' +#' ## Copyright notice +#' +#' When data downloaded from this page is used in any printed or electronic +#' publication, in addition to any other provisions applicable to the whole +#' Eurostat website, data source will have to be acknowledged in the legend of +#' the map and in the introductory page of the publication with the following +#' copyright notice: +#' +#' EN: © EuroGeographics for the administrative boundaries +#' +#' FR: © EuroGeographics pour les limites administratives +#' +#' DE: © EuroGeographics bezüglich der Verwaltungsgrenzen +#' +#' For publications in languages other than English, French or German, the +#' translation of the copyright notice in the language of the publication shall +#' be used. +#' +#' If you intend to use the data commercially, please contact EuroGeographics +#' for information regarding their licence agreements." +#' +#' # Eurostat: Copyright notice and free re-use of data +#' +#' The following copyright notice is provided for end user convenience. +#' Please check up-to-date copyright information from the eurostat website: +#' +#' +#' "(c) European Union, 1995 - today +#' +#' Eurostat has a policy of encouraging free re-use of its data, both for +#' non-commercial and commercial purposes. All statistical data, metadata, +#' content of web pages or other dissemination tools, official publications +#' and other documents published on its website, with the exceptions listed +#' below, can be reused without any payment or written licence provided that: +#' +#' * the source is indicated as Eurostat; +#' * when re-use involves modifications to the data or text, this must be +#' stated clearly to the end user of the information." +#' +#' For exceptions to the abovementioned principles see +#' [Eurostat website](https://ec.europa.eu/eurostat/about-us/policies/copyright) +#' +#' # Citing Eurostat data +#' +#' For citing datasets, use [get_bibentry()] to build a bibliography that +#' is suitable for your reference manager of choice. +#' +#' When using Eurostat data in other contexts than academic publications that +#' in-text citations or footnotes/endnotes, the following guidelines may be +#' helpful: +#' +#' * The origin of the data should always be mentioned as "Source: Eurostat". +#' +#' * The online dataset codes(s) should also be provided in order to ensure +#' transparency and facilitate access to the Eurostat data and related +#' methodological information. For example: +#' "Source: Eurostat (online data code: namq_10_gdp)" +#' +#' * Online publications (e.g. web pages, PDF) should include a clickable +#' link to the dataset using the bookmark functionality available in the +#' Eurostat data browser. +#' +#' It should be avoided to associate different entities (e.g. Eurostat, +#' National Statistical Offices, other data providers) to the same dataset or +#' indicator without specifying the role of each of them in the treatment of +#' data. +#' +#' See also section "Eurostat: Copyright notice and free re-use of data" +#' in [get_eurostat()] documentation. +#' +#' # Strategies for handling large datasets more efficiently +#' +#' Most Eurostat datasets are relatively manageable, at least on a machine +#' with 16 GB of RAM. The largest dataset in Eurostat database, at the time +#' of writing this, had 148362539 (148 million) values, which results in an +#' object with 148 million rows in tidy data (long) format. The test machine +#' with 16 GB of RAM was able to handle the second largest dataset in the +#' database with 91 million values (rows). +#' +#' There are still some methods to make data fetching +#' functions perform faster: +#' +#' * turn caching off: `get_eurostat(cache = FALSE)` +#' * turn cache compression off (may result in rather large cache files!): +#' `get_eurostat(compress_file = FALSE)` +#' * if you want faster caching with manageable file sizes, use stringsAsFactors: +#' `get_eurostat(cache = TRUE, compress_file = TRUE, stringsAsFactors = TRUE)` +#' * Use faster data.table functions: `get_eurostat(use.data.table = TRUE)` +#' * Keep column processing to a minimum: +#' `get_eurostat(time_format = "raw", type = "code")` etc. +#' * Read `get_eurostat()` function documentation carefully so you understand +#' what different arguments do +#' * Filter the dataset so that you fetch only the parts you need! +#' #' @examples library(eurostat) +#' @section regions functions: +#' For working with sub-national statistics the basic functions of the +#' regions package are imported . #' @keywords package +#' @seealso `help("regions")`, NULL diff --git a/R/eurotime2date.R b/R/eurotime2date.R index 0d0efe87..a59a030e 100755 --- a/R/eurotime2date.R +++ b/R/eurotime2date.R @@ -1,77 +1,154 @@ -#' @title Date Conversion from Eurostat Time Format -#' @description Date conversion from Eurostat time format. A function to -#' convert Eurostat time values to objects of class \code{\link{Date}} +#' @title Date Conversion from New Eurostat Time Format +#' @description +#' Date conversion from Eurostat time format. A function to +#' convert Eurostat time values to objects of class [Date()] #' representing calendar dates. +#' @details +#' Available patterns are YYYY (year), YYYY-SN (semester), YYYY-QN (quarter), +#' YYYY-MM (month), YYYY-WNN (week) and YYYY-MM-DD (day). #' @param x a charter string with time information in Eurostat time format. -#' @param last a logical. If \code{FALsE} (default) the date is -#' the first date of the period (month, quarter or year). If \code{TRUE} +#' @param last a logical. If `FALSE` (default) the date is +#' the first date of the period (month, quarter or year). If `TRUE` #' the date is the last date of the period. +#' @references +#' See `citation("eurostat")`: +#' +#' ```{r, echo=FALSE, comment="#" } +#' citation("eurostat") +#' ``` +#' +#' @return an object of class [Date()]. +#' @author Janne Huovari +#' @family helpers +#' @seealso [lubridate::ymd()] +#' @examplesIf check_access_to_data() +#' \donttest{ +#' na_q <- get_eurostat("namq_10_pc", time_format = "raw") +#' na_q$TIME_PERIOD <- eurotime2date(x = na_q$TIME_PERIOD) +#' unique(na_q$TIME_PERIOD) +#' } +#' +#' \dontrun{ +#' # Test for weekly data +#' get_eurostat( +#' id = "lfsi_abs_w", +#' select_time = c("W"), +#' time_format = "date" +#' ) +#' } +#' +#' @importFrom lubridate ymd +#' @importFrom ISOweek ISOweek2date +#' @importFrom dplyr inner_join +#' #' @export -#' @return an object of class \code{\link{Date}}. -#' @author Janne Huovari \email{janne.huovari@@ptt.fi} -#' @examples \dontrun{ -#' na_q <- get_eurostat("namq_10_pc", time_format = "raw") -#' na_q$time <- eurotime2date(x = na_q$time) -#' -#' un <- get_eurostat("une_rt_m", time_format = "raw") -#' un$time <- eurotime2date(x = un$time) -#' -#' na_a <- get_eurostat("nama_10_pc", time_format = "raw") -#' na_a$time <- eurotime2date(x = na_a$time) -#' -#' eur_d <- get_eurostat("ert_bil_eur_d", time_format = "raw") -#' eur_d$time <- eurotime2date(x = eur_d$time) -#' } -eurotime2date <- function(x, last = FALSE){ +eurotime2date <- function(x, last = FALSE) { if (!is.factor(x)) x <- factor(x) - times <- levels(x) - year <- substr(times, 1, 4) - subyear <- substr(times, 5, 7) - tcode <- substr(subyear[1], 1, 1) # type of time data - if (tcode != "_" && nchar(times[1]) > 7){ - days <- substr(times, 8, 10) - tcode <- substr(days[1], 1, 1) # type of time data if daily + unique_times <- levels(x) + year <- substr(unique_times, 1, 4) + # 5th character is always "-", if there is one + #from "YYYY-QN" the following line would extract "QN", from "YYYY-WNN" "WNN" + subyear <- substr(unique_times, 6, 8) + # the first character tells the type of the date + tcode <- substr(subyear[1], 1, 1) + if (tcode != "_" && nchar(unique_times[1]) > 8) { + days <- substr(unique_times, 8, 10) #extract -DD from YYYY-MM-DD + tcode <- substr(days[1], 1, 1) # tcode for daily data is "-" } - + if (tcode == "") tcode <- "Y" - - day <- "01" # default for day - - # for yearly data + + day <- "01" # default for day + if (tcode == "Y") { + # for yearly data period <- "01" - # for bi-annual } else if (tcode == "S") { + # for bi-annual ("semester") data lookup <- c(S1 = "01", S2 = "07") - period <- lookup[subyear] - # for quarterly + period <- lookup[subyear] } else if (tcode == "Q") { + # quarterly data lookup <- c(Q1 = "01", Q2 = "04", Q3 = "07", Q4 = "10") - period <- lookup[subyear] - # for montly - } else if (tcode == "M") { + period <- lookup[subyear] + } else if (tcode == "0" || tcode == "1") { + # monthly data period <- gsub("M", "", subyear) - # for daily - } else if (tcode == "D") { + } else if (tcode == "W") { + # weekly data + # We need period to be of format "WNN", e.g. W01 for 1st week of the year + period <- subyear + } else if (tcode == "-") { + # daily data period <- gsub("M", "", subyear) day <- gsub("D", "", days) - # for year intervals - } else if (tcode == "_") { - warning("Time format is a year interval. No date conversion was made.") - return(x) - # for unkown } else { - warning("Unknown time code, ", tcode, ". No date conversion was made.\n - Please fill bug report at https://github.com/rOpenGov/eurostat/issues.") + # nocov start + warning( + "Unknown time code, ", tcode, ". No date conversion was made.\n + Please fill bug report at ", + "https://github.com/rOpenGov/eurostat/issues." + ) return(x) + # nocov end } - - levels(x) <- paste0(year, "-", period, "-", day) - + # The date as the last date of the period - if (last == TRUE) { - shift <- c("Y" = 367, "S" = 186, "Q" = 96, "M" = 32, "D" = 0)[tcode] - levels(x) <- lubridate::ymd(cut(lubridate::ymd(levels(x)) + shift, "month")) - 1 + if (tcode == "W") { + # in some datasets week number can be larger than 53, e.g. 99 + # ISOweek2date does not support such week numbers -> they are coerced + # to W01-1 or W01-7 + if (any(substr(period, 2, 3) > 53)) { + warning(paste("Some TIME_PERIOD fields have invalid week values (> 53).", + "Coercing invalid fields to format YYYY-W01", + "(the first week of the year). If you wish to handle", + "weeks with invalid values manually please use", + "parameter time_format = 'raw' in get_eurostat.")) + invalid_w_numbers <- which(substr(period, 2, 3) > 53) + period[invalid_w_numbers] <- "W01" + } + + # Range is 1-7 here, not 01-07. 1 = Monday, 2 = Tuesday etc. + day <- ifelse(last == TRUE, 7, 1) + levels(x) <- paste0(year, "-", period, "-", day) + unique_dates <- unique(x) + column_names <- c("orig", "date") + d <- data.frame( + matrix( + nrow = length(unique_dates), + ncol = length(column_names) + ) + ) + colnames(d) <- column_names + d$orig <- unique_dates + d$date <- ISOweek::ISOweek2date(unique_dates) + + # NEW CODE: data.table + # d <- as.data.table(d) + # x <- as.data.table(x) + + x <- as.data.frame(x) + colnames(x) <- "orig" + + # NEW CODE: data.table + # y <- x[d, on = "orig"]$date + + y <- dplyr::inner_join(x, d, by = "orig") + y <- y$date + return(y) + } + + levels(x) <- paste0(year, "-", period, "-", day) + + # For times other than weeks + if (last == TRUE && tcode != "W") { + shift <- c( + "Y" = 367, "S" = 186, "Q" = 96, "0" = 32, "1" = 32, "D" = 0 + )[tcode] + + levels(x) <- lubridate::ymd( + cut(lubridate::ymd(levels(x)) + shift, "month") + ) - 1 } y <- lubridate::ymd(x) y diff --git a/R/eurotime2num.R b/R/eurotime2num.R index b474d58e..2e5fcb8d 100755 --- a/R/eurotime2num.R +++ b/R/eurotime2num.R @@ -1,60 +1,74 @@ #' @title Conversion of Eurostat Time Format to Numeric #' @description A conversion of a Eurostat time format to numeric. -#' @details Bi-annual, quarterly and monthly data is presented as fraction of -#' the year in beginning of the period. Conversion of daily data is not -#' supported. +#' @details +#' Bi-annual (semester), quarterly, monthly and weekly data can be presented as +#' a fraction of the year in beginning of the period. Conversion of daily data +#' is not supported. #' @param x a charter string with time information in Eurostat time format. +#' @return see [as.numeric()]. +#' @author Janne Huovari , Pyry Kantanen +#' @family helpers +#' @examplesIf check_access_to_data() +#' \donttest{ +#' na_q <- get_eurostat("namq_10_pc", time_format = "raw") +#' na_q$TIME_PERIOD <- eurotime2num(x = na_q$TIME_PERIOD) +#' +#' unique(na_q$TIME_PERIOD) +#' } +#' #' @export -#' @return see \code{\link{as.numeric}}. -#' @author Janne Huovari \email{janne.huovari@@ptt.fi} -#' @examples \dontrun{ -#' na_q <- get_eurostat("namq_10_pc", time_format = "raw") -#' na_q$time <- eurotime2num(x = na_q$time) -#' -#' un <- get_eurostat("une_rt_m", time_format = "raw") -#' un$time <- eurotime2num(x = un$time) -#' -#' na_a <- get_eurostat("nama_10_pc", time_format = "raw") -#' na_a$time <- eurotime2num(x = na_a$time) -#' } -eurotime2num <- function(x){ - +eurotime2num <- function(x) { x <- as.factor(x) times <- levels(x) - if (nchar(times[1]) > 7){ - tcode <- substr(times[1], 8, 8) # daily + if (nchar(times[1]) > 8) { + # Finds the only format that is longer than YYYY-WNN (weeks, 8 chars) + # Day/date notation: YYYY-MM-DD, 10 chars + # tcode <- substr(times[1], 8, 8) + tcode <- "D" } else { - tcode <- substr(times[1], 5, 5) # type of time data - if (tcode == "") tcode <- "Y" + # Possible tcodes: S, Q, 0 or 1 (months), W + # tcode: type of time data + tcode <- substr(times[1], 6, 6) + # if tcode is empty, the data is probably annual + if (tcode == "0" || tcode == "1") { + tcode <- "M" + } else if (tcode == "") { + tcode <- "A" + } } - - # check input type - if (!(tcode %in% c("Y", "S", "Q", "M", "_"))) { + # check input type + if (!(tcode %in% c("A", "S", "Q", "M", "W"))) { # for daily - if (tcode == "D"){ + if (tcode == "D") { warning("Time format is daily data. No numeric conversion was made.") - # for year intervals - } else if (tcode == "_") { - warning("Time format is a year interval. No numeric conversion was made.") - # for unkown } else { - warning("Unknown time code, ", tcode, ". No numeric conversion was made.\n - Please fill bug report at https://github.com/rOpenGov/eurostat/issues.") + # nocov start + warning( + paste0( + "Unknown time code, ", tcode, ". No numeric conversion was made.\n", + "Please fill bug report at ", + "https://github.com/rOpenGov/eurostat/issues." + ) + ) + # nocov end } - - return(x) + + return(x) } - + year <- substr(times, 1, 4) - subyear <- substr(times, 6, 7) + subyear <- substr(times, 6, 8) + # The only characters that can be present are S, Q and W + subyear <- gsub("[SQW]", "", subyear) + subyear[subyear == ""] <- 1 - - levels(x) <- as.numeric(year) + - (as.numeric(subyear) - 1) * 1/c(Y = 1, S = 2, Q = 4, M = 12)[tcode] + levels(x) <- as.numeric(year) + + (as.numeric(subyear) - 1) * + 1 / c(A = 1, S = 2, Q = 4, M = 12, W = 53)[tcode] y <- as.numeric(as.character(x)) y } diff --git a/R/firstlib.R b/R/firstlib.R index 3b11db96..12f2f9e4 100755 --- a/R/firstlib.R +++ b/R/firstlib.R @@ -1,32 +1 @@ -#' @import methods -#' @importFrom broom tidy -#' @importFrom classInt classIntervals -#' @importFrom dplyr %>% -#' @importFrom dplyr filter -#' @importFrom dplyr mutate -#' @importFrom dplyr select -#' @importFrom dplyr filter -#' @importFrom dplyr left_join -#' @importFrom httr build_url -#' @importFrom httr content -#' @importFrom httr GET -#' @importFrom httr status_code -#' @importFrom jsonlite fromJSON -#' @importFrom RColorBrewer brewer.pal -#' @importFrom readr read_tsv -#' @importFrom readr col_character -#' @importFrom sp merge -#' @importFrom stringi stri_match_first_regex -#' @importFrom stringr str_replace_all -#' @importFrom sf st_as_sf -#' @importFrom sf as_Spatial -#' @importFrom sf st_read -#' @importFrom tibble data_frame -#' @importFrom tibble is_tibble -#' @importFrom tibble as_data_frame -#' @importFrom tidyr separate -#' @importFrom tidyr gather_ -#' @importFrom utils download.file -#' @importFrom methods as -#' @importFrom utils data .EurostatEnv <- new.env() diff --git a/R/get_bibentry.R b/R/get_bibentry.R new file mode 100755 index 00000000..0a2fc95e --- /dev/null +++ b/R/get_bibentry.R @@ -0,0 +1,157 @@ +#' @title Create A Data Bibliography +#' @description Creates a bibliography from selected Eurostat data files, +#' including last Eurostat update, URL access data, and optional keywords +#' set by the user. +#' @param code A Eurostat data code or a vector of Eurostat data codes as +#' character or factor. +#' @param keywords A list of keywords to be added to the entries. Defaults +#' to `NULL`. +#' @param format Default is `'Biblatex'`, alternatives are `'bibentry'` +#' or `'Bibtex'` (not case sensitive) +#' @inheritParams get_eurostat +#' @author Daniel Antal, Przemyslaw Biecek +#' @inheritSection eurostat-package Citing Eurostat data +#' +#' @return a bibentry, Bibtex or Biblatex object. +#' +#' @seealso [utils::bibentry] [RefManageR::toBiblatex] +#' +#' @examplesIf check_access_to_data() +#' \dontrun{ +#' my_bibliography <- get_bibentry( +#' code = c("tran_hv_frtra", "tec00001"), +#' keywords = list( +#' c("transport", "freight", "multimodal data", "GDP"), +#' c("economy and finance", "annual", "national accounts", "GDP") +#' ), +#' format = "Biblatex" +#' ) +#' my_bibliography +#' } +#' +#' @importFrom lubridate dmy year month day +#' @importFrom utils toBibtex person +#' @importFrom RefManageR BibEntry toBiblatex +#' @importFrom stringr str_glue +#' +#' @export +get_bibentry <- function(code, + keywords = NULL, + format = "Biblatex", + lang = "en") { + if (!any(class(code) %in% c("character", "factor"))) { + stop("The code(s) must be added as character vector") + } + if (!is.null(keywords) && !inherits(keywords, "list")) { + stop("If keyword(s) are added, they must be added as a list.") + } + + code <- as.character(code) + format <- tolower(as.character(format)) + + if (!format %in% c("bibentry", "bibtex", "biblatex")) { + warning("The ", format, " is not recognized, will return Biblatex as + default.") + format <- "biblatex" + } + + toc <- get_eurostat_toc(lang = lang) + # Remove hierarchy column to make duplicated() check more viable + toc <- toc[, !names(toc) %in% c("hierarchy")] + toc <- toc[toc$code %in% code, ] + toc <- toc[!duplicated(toc), ] + + urldate <- as.character(Sys.Date()) + + lang <- check_lang(lang) + + lang_long <- switch(lang, + en = "english", + fr = "french", + de = "german") + + if (nrow(toc) == 0) { + warning(paste( + "None of the codes were found in the Eurostat table of contents.\n", + "Please check the 'code' argument in get_bibentry() for errors:\n", + paste(code, collapse = ", ") + ) + ) + return() + } + + not_found <- NULL + not_found <- !(code %in% toc$code) + not_found <- code[not_found] + if (is.character(not_found) && length(not_found) != 0) { + warning(paste( + "The following codes were not found in the Eurostat table of contents.\n", + "Bibliography object returned without the following items:\n", + paste(not_found, collapse = ", ") + )) + } + + for (i in seq_len(nrow(toc))) { + last_update_date <- lubridate::dmy(toc[["last.update.of.data"]][[i]]) + last_update_year <- lubridate::year(last_update_date) + last_update_month <- lubridate::month(last_update_date) + last_update_day <- lubridate::day(last_update_date) + + dataset_key <- paste0( + toc$code[i], "-", last_update_year, "-", last_update_month, "-", + last_update_day + ) + # replace troublesome _ with - + dataset_key <- gsub("_", "-", dataset_key) + + dataset_id <- toc$code[i] + # replace troublesome _ with \_ for bibtex and biblatex references + if (format %in% c("bibtex", "biblatex")) { + dataset_id <- gsub("_", "\\\\_", dataset_id) + } + + if (!is.null(keywords)) { # if user entered keywords + if (length(keywords) < i) { # last keyword not entered + keyword_entry <- NULL + } else if (nchar(keywords)[i] > 0) { # not empty keyword entry + keyword_entry <- paste(keywords[[i]], collapse = ", ") + } + } else { + keyword_entry <- NULL + } + + entry <- RefManageR::BibEntry( + bibtype = "misc", + key = dataset_key, + title = paste0(toc$title[i], " (", dataset_id, ")"), + url = paste0("https://ec.europa.eu/eurostat/web/products-datasets/product?code=", + toc$code[i]), + language = lang_long, + # date = last_update_date, + year = last_update_year, + author = c( + utils::person(given = "Eurostat") + ), + keywords = keyword_entry, + urldate = urldate, + type = "Dataset", + note = stringr::str_glue( + paste("Accessed {as.Date(urldate)},", + "dataset last updated {as.Date(last_update_date)}") + ) + ) + + if (i > 1) { + entries <- c(entries, entry) + } else { + entries <- entry + } + } + + if (format == "bibtex") { + entries <- utils::toBibtex(entries) + } else if (format == "biblatex") { + entries <- RefManageR::toBiblatex(entries) + } + entries +} diff --git a/R/get_eurostat.R b/R/get_eurostat.R index 4ef13d79..013fea38 100755 --- a/R/get_eurostat.R +++ b/R/get_eurostat.R @@ -1,197 +1,786 @@ -#' @title Read Eurostat Data -#' @description Download data sets from Eurostat \url{ec.europa.eu/eurostat}. -#' @param id A code name for the dataset of interest. -#' See \code{\link{search_eurostat}} or details for how to get code. -#' @param filters a "none" (default) to get a whole dataset or a named list of -#' filters to get just part of the table. Names of list objects are -#' Eurostat variable codes and values are vectors of observation codes. -#' If \code{NULL} the whole -#' dataset is returned via API. More on details. See more on filters and -#' limitations per query via API from for -#' \code{\link{get_eurostat_json}}. -#' @param time_format a string giving a type of the conversion of the time -#' column from the eurostat format. A "date" (default) convers to -#' a \code{\link{Date}} with a first date of the period. -#' A "date_last" convers to a \code{\link{Date}} with -#' a last date of the period. A "num" convers to a numeric and "raw" -#' does not do conversion. See \code{\link{eurotime2date}} and -#' \code{\link{eurotime2num}}. -#' @param type A type of variables, "code" (default) or "label". -#' @param select_time a character symbol for a time frequence or NULL, -#' which is used by default as most datasets have just one time -#' frequency. For datasets with multiple time -#' frequencies, select the desired time format with: -#' Y = annual, S = semi-annual, Q = quarterly, M = monthly. -#' For all frequencies in same data frame \code{time_format = "raw"} -#' should be used. -#' @param cache a logical whether to do caching. Default is \code{TRUE}. Affects -#' only queries from the bulk download facility. -#' @param update_cache a locigal whether to update cache. Can be set also with -#' options(eurostat_update = TRUE) -#' @param cache_dir a path to a cache directory. The directory have to exist. -#' The \code{NULL} (default) uses and creates -#' 'eurostat' directory in the temporary directory from -#' \code{\link{tempdir}}. Directory can also be set with -#' \code{option} eurostat_cache_dir. -#' @param compress_file a logical whether to compress the -#' RDS-file in caching. Default is \code{TRUE}. -#' @param stringsAsFactors if \code{TRUE} (the default) variables are -#' converted to factors in original Eurostat order. If \code{FALSE} -#' they are returned as a character. -#' @param keepFlags a logical whether the flags (e.g. "confidential", -#' "provisional") should be kept in a separate column or if they -#' can be removed. Default is \code{FALSE}. For flag values see: -#' \url{http://ec.europa.eu/eurostat/data/database/information}. -#' Also possible non-real zero "0n" is indicated in flags column. -#' Flags are not available for eurostat API, so \code{keepFlags} -#' can not be used with a \code{filters}. -#' @param ... further argument for \code{\link{get_eurostat_json}}. -#' @export -#' @author Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu \email{ropengov-forum@@googlegroups.com} \url{http://github.com/ropengov/eurostat} -#' @details Data sets are downloaded from -#' \href{http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing}{the Eurostat bulk download facility} or from The Eurostat Web Services -#' \href{http://ec.europa.eu/eurostat/web/json-and-unicode-web-services}{JSON API}. -#' If only the table \code{id} is given, the whole table is downloaded from the -#' bulk download facility. If also \code{filters} are defined the JSON API is -#' used. +#' @title Get Eurostat Data +#' +#' @description +#' Download data sets from Eurostat \url{https://ec.europa.eu/eurostat} +#' +#' @param id +#' A unique identifier / code for the dataset of interest. If code is not +#' known [search_eurostat()] function can be used to search Eurostat table +#' of contents. +#' @param filters +#' A named list of filters. Names of list objects are Eurostat +#' variable codes and values are vectors of observation codes. If `NULL` +#' (default) the whole dataset is returned. See details for more information +#' on filters and limitations per query. +#' @param time_format +#' a string giving a type of the conversion of the time column from the +#' eurostat format. The default argument "`date`" converts to a [Date()] class +#' with the date being the first day of the period. A "`date_last`" argument +#' converts the dataset date to a [Date()] class object with the difference +#' that the exact date is the last date of the period. Period can be year, +#' semester (half year), quarter, month, or week (See [eurotime2date()] for +#' more information). +#' Argument "`num`" converts the date into a numeric (integer) meaning that +#' the first day of the year 2000 is close to 2000.01 and the last day of the +#' year is close to 2000.99 (see [eurotime2num()] for more information). +#' Using the argument "`raw`" preserves the dates as they were in the original +#' Eurostat data. +#' @param type +#' A type of variables, "`code`" (default), "`label`" or "`both`". +#' The parameter "`both`" will return a data_frame with named vectors, +#' labels as values and codes as names. +#' @param select_time +#' a character symbol for a time frequency or `NULL`, +#' which is used by default as most datasets have just one time +#' frequency. For datasets with multiple time +#' frequencies, select one or more of the desired frequencies with: +#' "Y" (or "A") = annual, "S" = semi-annual / semester, "Q" = quarterly, +#' "M" = monthly, "W" = weekly. For all frequencies in same data +#' frame `time_format = "raw"` should be used. +#' @param lang 2-letter language code, default is "`en`" (English), other +#' options are "`fr`" (French) and "`de`" (German). Used for labeling datasets. +#' @param cache +#' a logical whether to do caching. Default is `TRUE`. +#' @param update_cache +#' a logical whether to update cache. Can be set also with +#' `options(eurostat_update = TRUE)` +#' @param cache_dir +#' a path to a cache directory. `NULL` (default) uses and creates +#' 'eurostat' directory in the temporary directory defined by base R +#' [tempdir()] function. The user can set the cache directory to an existing +#' directory by using this argument. The cache directory can also be set with +#' [set_eurostat_cache_dir()] function. +#' @param compress_file +#' a logical whether to compress the RDS-file in caching. Default is `TRUE`. +#' @param stringsAsFactors +#' if `TRUE` (the default) variables are converted to factors in the original +#' Eurostat order. If `FALSE` they are returned as strings. +#' @param keepFlags +#' a logical whether the flags (e.g. "confidential", +#' "provisional") should be kept in a separate column or if they +#' can be removed. Default is `FALSE`. For flag values see: +#' . +#' Also possible non-real zero "0n" is indicated in flags column. +#' Flags are not available for eurostat API, so `keepFlags` +#' can not be used with a `filters`. +#' @param use.data.table Use faster data.table functions? Default is FALSE. +#' On Windows requires that RTools is installed. +#' @inheritDotParams get_eurostat_json +#' +#' @inherit eurostat-package references +#' +#' @inheritSection eurostat-package Eurostat: Copyright notice and free re-use of data +#' @inheritSection eurostat-package Filtering datasets +#' @inheritSection eurostat-package Citing Eurostat data +#' @inheritSection eurostat-package Disclaimer: Availability of filtering functionalities +#' @inheritSection eurostat-package Strategies for handling large datasets more efficiently +#' +#' @author +#' Przemyslaw Biecek, Leo Lahti, Janne Huovari, Markus Kainu and Pyry Kantanen #' +#' @details +#' Datasets are downloaded from +#' [the Eurostat SDMX 2.1 API](https://wikis.ec.europa.eu/display/EUROSTATHELP/Transition+-+from+Eurostat+Bulk+Download+to+API) +#' in TSV format or from The Eurostat +#' [API Statistics JSON API](https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query). +#' If only the table `id` is given, the whole table is downloaded from the +#' SDMX API. If any `filters` are given JSON API is used instead. +#' #' The bulk download facility is the fastest method to download whole datasets. -#' It is also often the only way as the JSON API has limitation of maximum -#' 50 sub-indicators at time and whole datasets usually exceeds that. Also, -#' it seems that multi frequency datasets can only be retrived via -#' bulk download facility and the \code{select_time} is not available for +#' It is also often the only way as the JSON API has limitation of maximum +#' 50 sub-indicators at time and whole datasets usually exceeds that. Also, +#' it seems that multi frequency datasets can only be retrieved via +#' bulk download facility and the `select_time` is not available for #' JSON API method. -#' -#' If your connection is thru a proxy, you probably have to set proxy parameters -#' to use JSON API, see \code{\link{get_eurostat_json}}. -#' -#' By default datasets from the bulk download facility are cached as they are -#' often rather large. Caching is not (currently) possible for datasets from -#' JSON API. +#' +#' If your connection is through a proxy, you may have to set proxy parameters +#' to use JSON API, see [get_eurostat_json()]. +#' +#' By default datasets are cached to reduce load on Eurostat services and +#' because some datasets can be quite large. #' Cache files are stored in a temporary directory by default or in -#' a named directory if cache_dir or option eurostat_cache_dir is defined. -#' The cache can be emptied with \code{\link{clean_eurostat_cache}}. -#' -#' The \code{id}, a code, for the dataset can be searched with -#' the \code{\link{search_eurostat}} or from the Eurostat database -#' \url{http://ec.europa.eu/eurostat/data/database}. The Eurostat +#' a named directory (See [set_eurostat_cache_dir()]). +#' The cache can be emptied with [clean_eurostat_cache()]. +#' +#' The `id`, a code, for the dataset can be searched with +#' the [search_eurostat()] or from the Eurostat database +#' . The Eurostat #' database gives codes in the Data Navigation Tree after every dataset #' in parenthesis. -#' @return a tibble. One column for each dimension in the data, -#' the time column for a time dimension and -#' the values column for numerical values. -#' Eurostat data does not include all missing values and a treatment of -#' missing values depend on source. In bulk download -#' facility missing values are dropped if all dimensions are missing -#' on particular time. In JSON API missing values are dropped -#' only if all dimensions are missing on all times. The data from -#' bulk download facility can be completed for example with -#' \code{\link[tidyr]{complete}}. -#' @seealso \code{\link{search_eurostat}}, \code{\link{label_eurostat}} -#' @examples +#' +#' @return +#' a tibble. +#' +#' One column for each dimension in the data, the time column for a time +#' dimension and the values column for numerical values. Eurostat data does +#' not include all missing values and a treatment of missing values depend +#' on source. In bulk download facility missing values are dropped if all +#' dimensions are missing on particular time. In JSON API missing values are +#' dropped only if all dimensions are missing on all times. The data from +#' bulk download facility can be completed for example with [tidyr::complete()]. +#' +#' @seealso +#' [search_eurostat()], [label_eurostat()] +#' +#' @examplesIf check_access_to_data() #' \dontrun{ #' k <- get_eurostat("nama_10_lp_ulc") #' k <- get_eurostat("nama_10_lp_ulc", time_format = "num") #' k <- get_eurostat("nama_10_lp_ulc", update_cache = TRUE) -#' dir.create(file.path(tempdir(), "r_cache")) -#' k <- get_eurostat("nama_10_lp_ulc", -#' cache_dir = file.path(tempdir(), "r_cache")) +#' +#' k <- get_eurostat("nama_10_lp_ulc", +#' cache_dir = file.path(tempdir(), "r_cache") +#' ) #' options(eurostat_update = TRUE) #' k <- get_eurostat("nama_10_lp_ulc") #' options(eurostat_update = FALSE) -#' options(eurostat_cache_dir = file.path(tempdir(), "r_cache")) +#' +#' set_eurostat_cache_dir(file.path(tempdir(), "r_cache2")) #' k <- get_eurostat("nama_10_lp_ulc") #' k <- get_eurostat("nama_10_lp_ulc", cache = FALSE) #' k <- get_eurostat("avia_gonc", select_time = "Y", cache = FALSE) -#' -#' dd <- get_eurostat("nama_10_gdp", -#' filters = list(geo = "FI", -#' na_item = "B1GQ", -#' unit = "CLV_I10")) +#' +#' dd <- get_eurostat("nama_10_gdp", +#' filters = list( +#' geo = "FI", +#' na_item = "B1GQ", +#' unit = "CLV_I10" +#' ) +#' ) +#' +#' # A dataset with multiple time series in one +#' dd2 <- get_eurostat("AVIA_GOR_ME", +#' select_time = c("A", "M", "Q"), +#' time_format = "date_last" +#' ) +#' +#' # An example of downloading whole dataset from JSON API +#' dd3 <- get_eurostat("AVIA_GOR_ME", +#' filters = list() +#' ) +#' +#' # Filtering a dataset from a local file +#' dd3_filter <- get_eurostat("AVIA_GOR_ME", +#' filters = list( +#' tra_meas = "FRM_BRD" +#' ) +#' ) +#' #' } -get_eurostat <- function(id, time_format = "date", filters = "none", +#' +#' @importFrom digest digest +#' @importFrom jsonlite toJSON fromJSON +#' @importFrom dplyr filter +#' @importFrom rlang !! sym +#' @export +get_eurostat <- function(id, + time_format = "date", + filters = NULL, type = "code", select_time = NULL, - cache = TRUE, update_cache = FALSE, cache_dir = NULL, + lang = "en", + cache = TRUE, + update_cache = FALSE, + cache_dir = NULL, compress_file = TRUE, - stringsAsFactors = default.stringsAsFactors(), - keepFlags = FALSE, ...){ - #Warning for flags with filter - if (keepFlags & !is.character(filters) && filters != "none") { - warning("keepFlags can be used only without filters. No Flags returned.") - } + stringsAsFactors = FALSE, + keepFlags = FALSE, + use.data.table = FALSE, + ...) { + + # Check if you have access to ec.europe.eu. + # If dataset is cached, access to ec.europe.eu is not needed + # Therefore this is a warning, not a stop + if (!check_access_to_data()) { + # nocov start + warning("You have no access to ec.europe.eu. + Please check your connection and/or review your proxy settings") + # nocov end + } - # No cache for json - if (is.null(filters) || filters != "none") { - cache <- FALSE + # For better code clarity, use only NULL in code + if (is.character(filters) && identical(tolower(filters), "none")) { + filters <- NULL + } else if (is.character(filters) && !identical(tolower(filters), "null")) { + message("Non-standard filters argument. Using argument 'filters = NULL'") + filters <- NULL + } + + # Inform user with message if keepFlags == TRUE cannot be delivered + if (keepFlags && !is.null(filters)) { + message("The keepFlags argument of the get_eurostat function + can be used only without filters. No Flags returned.") + keepFlags <- FALSE } - if (cache){ + # Sanity check + type <- tolower(type) + time_format <- tolower(time_format) + lang <- check_lang(lang) + + if (cache) { + # check option for update update_cache <- update_cache | getOption("eurostat_update", FALSE) # get cache directory - if (is.null(cache_dir)){ - cache_dir <- getOption("eurostat_cache_dir", NULL) - if (is.null(cache_dir)){ - cache_dir <- file.path(tempdir(), "eurostat") - if (!file.exists(cache_dir)) dir.create(cache_dir) + cache_dir <- eur_helper_cachedir(cache_dir) + + cache_list <- file.path( + cache_dir, + paste0( + "cache_list", + ".json" + ) + ) + + # cache file connection + cache_list_conn <- file(cache_list, "a") + + # Sort filters alphabetically #### + # Subsetting with NULL turns list("") (List of 1) to list() (List of 0) + # This needs to be looked into, even if it's useful, it's unexpected + # if structure below prevents subsetting when names(filters) is NULL + if (!is.null(names(filters))) { + filters <- filters[sort(names(filters), decreasing = FALSE)] + } + + # Sort items inside each individual filter to alphabetical order + for (i in seq_along(filters)) { + if (length(filters[i]) > 1) { + filters[i] <- sort(filters[i]) } + } + + # Turn query into lists with predefined order + # Order is defined by the order of arguments in function documentation + query <- list( + list( + id = id, + time_format = time_format, + filters = filters, + type = type, + select_time = select_time, + stringsAsFactors = stringsAsFactors, + keepFlags = keepFlags, + source = ifelse(is.null(filters), "bulk", "json"), + download_date = Sys.Date() + ) + ) + + if (is.null(filters)) { + query_unfiltered <- list( + list( + id = id, + time_format = time_format, + filters = NULL, + type = type, + select_time = select_time, + stringsAsFactors = stringsAsFactors, + keepFlags = keepFlags, + source = "bulk", + download_date = Sys.Date() + ) + ) } else { - if (!file.exists(cache_dir)) { - stop("The folder ", cache_dir, " does not exist") + # This is to make it possible to filter local dataset + # The only way to return a whole dataset from JSON API is to use + # empty list with filters argument + query_unfiltered <- list( + list( + id = id, + time_format = time_format, + filters = list(), + type = type, + select_time = select_time, + stringsAsFactors = stringsAsFactors, + keepFlags = keepFlags, + source = "json", + download_date = Sys.Date() + ) + ) + } + + query_hash <- digest::digest(query, algo = "md5") + query_hash_unfiltered <- digest::digest(query_unfiltered, algo = "md5") + names(query) <- digest::digest(query, algo = "md5") + cache_list_current <- readLines(cache_list) + + if (any(grepl(query_hash, cache_list_current))) { + # Check if the same query has already been made + message("Dataset query already saved in cache_list.json...") + } else if (!any(grepl(query_hash, cache_list_current))) { + # If query was not made, append cache_list with new json query + if (length(cache_list_current) == 0) { + # close previous connection with mode "a" + close(cache_list_conn) + cache_list_conn <- file(cache_list, "w") + + json_query <- jsonlite::toJSON( + query, + pretty = TRUE, + null = "null" + ) + + writeLines(text = json_query, + con = cache_list_conn, + sep = "\n") + + } else if (length(cache_list_current) != 0) { + # read previously saved queries and save them + cache_list_history <- jsonlite::fromJSON(cache_list) + # close previous connection with mode "a" + close(cache_list_conn) + # open a new connection with mode "w" that wipes the file + cache_list_conn <- file(cache_list, "w") + cache_list_history_and_query <- c(cache_list_history, query) + + json_query <- jsonlite::toJSON( + cache_list_history_and_query, + pretty = TRUE, + null = "null" + ) + + writeLines( + text = paste0(json_query), + con = cache_list_conn, + sep = "\n" + ) } } + close(cache_list_conn) + + # If query_hash and query_hash_unfiltered are not identical + # it means that it makes sense to see if a superset of the data exists + data_superset_exists <- FALSE + + # Re-read cache_list file if there were any changes + cache_list_current <- readLines(cache_list) + + if (!identical(query_hash, query_hash_unfiltered) && + any(grepl(query_hash_unfiltered, cache_list_current))) { + message("Dataset query not in cache_list.json but the whole dataset is") + data_superset_exists <- TRUE + } + # cache filename - cache_file <- file.path(cache_dir, - paste0(id, "_", time_format, - "_", type, select_time, "_", - strtrim(stringsAsFactors, 1), - strtrim(keepFlags, 1), - ".rds")) + cache_file <- file.path( + cache_dir, + paste0( + query_hash, + ".rds" + ) + ) + + # bulk cache filename + cache_file_bulk <- file.path( + cache_dir, + paste0( + query_hash_unfiltered, + ".rds" + ) + ) } - # if cache = FALSE or update or new: dowload else read from cache - if (!cache || update_cache || !file.exists(cache_file)){ - - if (is.null(filters) || is.list(filters)){ - # api download - y <- get_eurostat_json(id, filters, type = type, - stringsAsFactors = stringsAsFactors, ...) + # Download always files from server if any of the following is TRUE + # cache = FALSE + # -> no caching, always downloading + # update_cache = TRUE + # -> want to update a cache -> redownload + # suitable cache_file AND cache_file_bulk do not exist + # -> no cache files -> no reading from cache -> download + if (!cache || update_cache || + (!file.exists(cache_file) && !file.exists(cache_file_bulk))) { + if (is.list(filters)) { + # If filters value is some type of a list + # -> Download from Eurostat Web Service (replaces "JSON API") + + y <- get_eurostat_json( + id, + filters, + type, + lang, + stringsAsFactors, + ... + ) y$time <- convert_time_col(factor(y$time), time_format = time_format) - # bulk download - } else if (filters == "none") { - y_raw <- get_eurostat_raw(id) - y <- tidy_eurostat(y_raw, time_format, select_time, - stringsAsFactors = stringsAsFactors, - keepFlags = keepFlags) - if (type == "code") { - y <- y - } else if (type == "label") { - y <- label_eurostat(y) - } else if (type == "both"){ - stop("type = \"both\" can be only used with JSON API. Set filters argument") + } else if (is.null(filters)) { + # If filters value is NULL + # -> Download from SDMX 2.1 REST API (replaces old "Bulk download") + + y <- try(get_eurostat_raw(id, use.data.table = use.data.table), silent = TRUE) + if ("try-error" %in% class(y)) { + stop(paste("get_eurostat_raw fails with the id", id)) + } + + # If download from SDMX 2.1 REST API is successful + # -> tidy the dataset with tidy_eurostat function + + y <- tidy_eurostat( + y, + time_format, + select_time, + stringsAsFactors = stringsAsFactors, + keepFlags = keepFlags, use.data.table = use.data.table + ) + + if (identical(type, "code")) { + # do nothing + # y <- y + } else if (identical(type, "label")) { + y <- label_eurostat(y, lang) + } else if (identical(type, "both")) { + stop(paste("type = \"both\" can be only used with JSON API.", + "Set filters argument")) } else { stop("Invalid type.") } - } - - } else { + } else if (file.exists(cache_file_bulk) && data_superset_exists) { + # Somewhat redundant as data_superset_exists checks cache_list.json + # which lists files downloaded and saved to cache but maybe in some + # situations the cached file could go missing? Not very likely though + + message(paste("Reading cache file", cache_file_bulk, "and filtering it")) + y <- readRDS(cache_file_bulk) + for (i in seq_along(filters)) { + y <- dplyr::filter(y, + !!rlang::sym(names(filters)[i]) == filters[i]) + } + # y <- y_raw + } else if (file.exists(cache_file)) { cf <- path.expand(cache_file) message(paste("Reading cache file", cf)) y <- readRDS(cache_file) message(paste("Table ", id, " read from cache file: ", cf)) } - # if update or new: save - if (cache && (update_cache || !file.exists(cache_file))){ + # if update_cache = TRUE or cache file does not yet exist + # -> save cache file to cache directory + if (cache && (update_cache || !file.exists(cache_file))) { saveRDS(y, file = cache_file, compress = compress_file) message("Table ", id, " cached at ", path.expand(cache_file)) } y +} + +#' @title Get all datasets in a folder +#' @description +#' Loops over all files in a Eurostat database folder, downloads the data and +#' assigns the datasets to environment. +#' @details +#' The datasets are assigned into .EurostatEnv by default, using dataset codes +#' as object names. The datasets are downloaded from SDMX API as TSV files, +#' meaning that they are returned without filtering. No filters can be +#' provided using this function. +#' +#' Please do not attempt to download too many datasets or the whole database +#' at once. The number of datasets that can be downloaded at once is hardcoded +#' to 20. The function also asks the user for confirmation if the number of +#' datasets in a folder is more than 10. This is by design to discourage +#' straining Eurostat API. +#' @param code Folder code from Eurostat Table of Contents. +#' @param env Name of the environment where downloaded datasets are assigned. +#' Default is .EurostatEnv. If NULL, datasets are returned as a list object. +#' +#' @inheritSection eurostat-package Data source: Eurostat Table of Contents +#' @inheritSection eurostat-package Data source: Eurostat SDMX 2.1 Dissemination API +#' +#' @author Pyry Kantanen +#' +#' @inherit set_eurostat_toc seealso +#' +#' @importFrom stringr str_glue +#' @importFrom utils menu +#' +#' +#' @export +get_eurostat_folder <- function(code, env = .EurostatEnv) { + + # Limit after which the function prompts the user whether they really want + # to proceed + soft_limit <- 10 + # Limit that cannot be crossed with this function + hard_limit <- 20 + + toc <- get_eurostat_toc() + if (toc[["type"]][which(toc[["code"]] == code)] != "folder") { + warning("The code you provided is not a folder.") + return(invisible()) + } + children <- toc_list_children(code) + # Filter out potential subfolders + children <- children[which(children$type %in% c("dataset", "table")), ] + + if (nrow(children) == 0) { + warning("The folder code you provided did not have any items.") + return(invisible()) + } + if (nrow(children) > hard_limit) { + warning(stringr::str_glue( + "The number of datasets in folder ({nrow(children)}) is too large. ", + "Please use some other method for retrieving datasets." + )) + return(invisible()) + } + + if (nrow(children) > soft_limit) { + title_msg <- stringr::str_glue( + "The number of items in the folder is more than {soft_limit}. ", + "Do you wish to proceed?") + switch(menu(c("Yes", "No"), title = title_msg) + 1, + cat("Nothing done\n"), + message("Proceeding to download datasets in folder..."), + return(invisible())) + } + + if (!is.null(env)) { + for (i in seq_len(nrow(children))) { + dataset <- get_eurostat(children$code[i], cache = TRUE) + assign(children$code[i], dataset, envir = env) + message( + stringr::str_glue( + "Dataset {i} / {nrow(children)} assigned to environment\n\n") + ) + } + return(invisible()) + } else { + list_of_datasets <- list() + for (i in seq_len(nrow(children))) { + dataset <- get_eurostat(children$code[i], cache = TRUE) + list_of_datasets[[i]] <- dataset + # names(list_of_datasets[i]) <- children$code[i] + message( + stringr::str_glue( + "Dataset {i} / {nrow(children)} assigned to a list\n\n") + ) + } + names(list_of_datasets) <- children[["code"]] + return(list_of_datasets) + } +} + +#' @title Get Eurostat data interactive +#' @description +#' A simple interactive helper function to go through the steps of downloading +#' and/or finding suitable eurostat datasets. +#' +#' @details +#' This function is intended to enable easy exploration of different eurostat +#' package functionalities and functions. In order to not drown the end user +#' in endless menus this function does not allow for setting +#' all possible [get_eurostat()] function arguments. It is possible to set +#' `time_format`, `type`, `lang`, `stringsAsFactors`, `keepFlags`, and +#' `use.data.table` in the interactive menus. +#' +#' In some datasets setting these parameters may result in a +#' "Error in label_eurostat" error, for example: +#' "labels for XXXXXX includes duplicated labels in the Eurostat dictionary". +#' In these cases, and with other more complex queries, please +#' use [get_eurostat()] function directly. +#' +#' @param code +#' A unique identifier / code for the dataset of interest. If code is not +#' known [search_eurostat()] function can be used to search Eurostat table +#' of contents. +#' +#' @seealso [get_eurostat()] +#' @importFrom stringr str_glue +#' @importFrom utils capture.output +#' @export +get_eurostat_interactive <- function(code = NULL) { + # Interactive function, not feasible to test + # nocov start + lang_selection <- switch( + menu(c("English", "French", "German"), title = "Select language") + 1, + return(invisible()), + "en", + "fr", + "de" + ) + + if (is.null(code)) { + + search_term <- readline(prompt = "Enter search term for data: ") + results <- search_eurostat(pattern = search_term, lang = lang_selection) + code_and_title <- paste0("[", results$code, "] ", results$title) + if (nrow(results) > 0) { + choice <- menu(choices = code_and_title, title = "Which dataset would you like to download?") + if (choice == 0) { + # message("Nothing done\n") + return(invisible()) + } + } else { + stop(paste("No data found with given search term:", search_term)) + } + code <- results$code[choice] + } + + download_selection <- switch( + menu(choices = c("Yes", "No"), + title = "Download the dataset?") + 1, + return(invisible()), + TRUE, + FALSE + ) + # Set manual_selection to FALSE here to make it possible to print code + # for downloading dataset later + manual_selection <- FALSE + + if (download_selection) { + manual_selection <- switch( + menu(choices = c("Default", "Manually selected"), + title = "Would you like to use default download arguments or set them manually?") + 1, + return(invisible()), + FALSE, + TRUE + ) + + if (manual_selection) { + time_format_selection <- switch( + menu(choices = c("Convert to date, first day of the period (2000-04-01) (default)", + "Convert to date, last day of the period (2000-06-30)", + "Convert to numeric (2000.25)", + "Use raw data (2000-Q2)")) + 1, + "date", + "date", + "date_last", + "num", + "raw" + ) + + type_selection <- switch( + menu(choices = c("Return categorical variables as short codes (default)", + "Return categorical variables in labeled (long) format"), + title = "Note: The option 'both' is supported only when using the JSON API.") + 1, + "code", + "code", + "label" + ) + + stringsAsFactors_selection <- switch( + menu(choices = c("Return categorical data as characters (default)", + "Convert categorical data into factors"), + title = "Converting text data into factors may help reduce time used in data processing, reduce RAM usage and cache file size") + 1, + FALSE, + FALSE, + TRUE + ) + + keepFlags_selection <- switch( + menu(choices = c("Do not return flags, just remove them (default)", + "Return flags in separate column")) + 1, + FALSE, + FALSE, + TRUE + ) + + use.data.table_selection <- switch( + menu(choices = c("Do not use data.table functions (default", + "Use data.table functions"), + title = "Using data.table functions may help reduce time used in data processing and reduce RAM usage. It is advisable especially when dealing with large datasets.") + 1, + FALSE, + FALSE, + TRUE + ) + eurostat_data <- tryCatch({ + get_eurostat(id = code, + time_format = time_format_selection, + type = type_selection, + lang = lang_selection, + stringsAsFactors = stringsAsFactors_selection, + keepFlags = keepFlags_selection, + use.data.table = use.data.table_selection) + }, error=function(error_message) { + message("\nEncountered the following error message while attempting to modify the downloaded data:\n") + message(error_message) + message("\nUsing default options for downloading data...\n") + # Use scoping assignment here to influence code print below + time_format_selection <<- "date" + type_selection <<- "code" + lang_selection <<- lang_selection + stringsAsFactors_selection <<- FALSE + keepFlags_selection <<- FALSE + use.data.table_selection <<- FALSE + get_eurostat(id = code, + time_format = time_format_selection, + type = type_selection, + lang = lang_selection, + stringsAsFactors = stringsAsFactors_selection, + keepFlags = keepFlags_selection, + use.data.table = use.data.table_selection) + }) + } else if (!manual_selection) { + eurostat_data <- get_eurostat(id = code) + } + } + + tempfile_for_sinking <- tempfile() + + # eurostat_data <- get_eurostat(id = code) + print_citation <- switch( + menu(choices = c("Yes", "No"), + title = "Print dataset citation?") + 1, + return(invisible()), + TRUE, + FALSE + ) + + if (print_citation) { + citation <- get_bibentry(code, lang = lang_selection) + capture.output(cat("##### DATASET CITATION:\n\n"), file = tempfile_for_sinking, append = TRUE) + capture.output(print(citation), file = tempfile_for_sinking, append = TRUE) + capture.output(cat("\n"), file = tempfile_for_sinking, append = TRUE) + } + + print_code <- switch( + menu(choices = c("Yes", "No"), + title = "Print code for downloading dataset?") + 1, + return(invisible()), + TRUE, + FALSE + ) + + if (print_code == TRUE && manual_selection == TRUE) { + capture.output(cat("##### DOWNLOAD PARAMETERS:\n\n")) + capture.output(print(stringr::str_glue(paste0("get_eurostat(id = '{code}', time_format = '{time_format_selection}', ", + "type = '{type_selection}', lang = '{lang_selection}', ", + "stringsAsFactors = {stringsAsFactors_selection}, ", + "keepFlags = {keepFlags_selection}, ", + "use.data.table = {use.data.table_selection})"))), file = tempfile_for_sinking, append = TRUE) + capture.output(cat("\n")) + } else if (print_code == TRUE && manual_selection == FALSE) { + capture.output(cat("##### DOWNLOAD PARAMETERS:\n\n"), file = tempfile_for_sinking, append = TRUE) + capture.output(print(stringr::str_glue("get_eurostat(id = '{code}')")), file = tempfile_for_sinking, append = TRUE) + capture.output(cat("\n"), file = tempfile_for_sinking, append = TRUE) + } + + if (exists("eurostat_data")) { + print_code <- switch( + menu(choices = c("Yes", "No"), + title = "Print dataset fixity checksum?") + 1, + return(invisible()), + TRUE, + FALSE + ) + + if (print_code) { + capture.output(cat("##### FIXITY CHECKSUM:\n\n"), file = tempfile_for_sinking, append = TRUE) + capture.output(print(stringr::str_glue("Fixity checksum (md5) for dataset {code}: {eurostat:::fixity_checksum(eurostat_data, algorithm = 'md5')}")), file = tempfile_for_sinking, append = TRUE) + capture.output(cat("\n"), file = tempfile_for_sinking, append = TRUE) + } + } + + if (exists("eurostat_data")) { + cat(readLines(tempfile_for_sinking), sep = "\n") + return(eurostat_data) + } else { + cat(readLines(tempfile_for_sinking), sep = "\n") + return(invisible()) + } + # nocov end } diff --git a/R/get_eurostat_dic.R b/R/get_eurostat_dic.R index dded4a47..b4236869 100755 --- a/R/get_eurostat_dic.R +++ b/R/get_eurostat_dic.R @@ -1,35 +1,58 @@ #' @title Download Eurostat Dictionary #' @description Download a Eurostat dictionary. -#' @details For given coded variable from Eurostat \url{ec.europa.eu/eurostat}. -#' The dictionaries link codes with human-readable labels. -#' To translate codes to labels, use \code{\link{label_eurostat}}. +#' @details For given coded variable from Eurostat +#' . The dictionaries link codes with +#' human-readable labels. To translate codes to labels, use +#' [label_eurostat()]. #' @param dictname A character, dictionary for the variable to be downloaded. -#' @param lang A character, language code. Options: "en" (default) / "fr" / "de". +#' @param lang A character, language code. Options: "en" (default), "fr", "de". #' @return tibble with two columns: code names and full names. #' @export -#' @seealso \code{\link{label_eurostat}}, \code{\link{get_eurostat}}, -#' \code{\link{search_eurostat}}. -#' @references See citation("eurostat"). -#' @author Przemyslaw Biecek and Leo Lahti \email{leo.lahti@@iki.fi}. Thanks to Wietse Dol for contributions. -#' @examples \dontrun{ -#' tmp <- get_eurostat_dic("crop_pro") -#' head(tmp) -#' tmp <- get_eurostat_dic("crop_pro", lang = "fr") -#' } +#' @seealso [label_eurostat()], [get_eurostat()], +#' [search_eurostat()]. +#' @references +#' See `citation("eurostat")`: +#' +#' ```{r, echo=FALSE, comment="#" } +#' citation("eurostat") +#' ``` +#' +#' @author Przemyslaw Biecek and Leo Lahti . Thanks to +#' Wietse Dol for contributions. Updated by Pyry Kantanen to support XML +#' codelists. +#' @examplesIf check_access_to_data() +#' \donttest{ +#' get_eurostat_dic("crop_pro") +#' +#' # Try another language +#' get_eurostat_dic("crop_pro", lang = "fr") +#' } +#' +#' @importFrom readr read_tsv cols col_character +#' #' @keywords utilities database get_eurostat_dic <- function(dictname, lang = "en") { + + lang <- check_lang(lang) + dictlang <- paste0(dictname, "_", lang) if (!exists(dictlang, envir = .EurostatEnv)) { url <- getOption("eurostat_url") - tname <- paste0(url, - "estat-navtree-portlet-prod/BulkDownloadListing?file=dic%2F", lang, "%2F", - dictname, ".dic") - dict <- readr::read_tsv(url(tname), col_names = c("code_name", "full_name"), - col_types = readr::cols(.default = readr::col_character())) - dict$full_name <- gsub(pattern="\u0092", replacement="'", dict$full_name) + tname <- paste0( + url, + "api/dissemination/sdmx/2.1/codelist/ESTAT/", dictname, + "?format=TSV&lang=", lang + ) + dict <- readr::read_tsv(url(tname), + col_names = c("code_name", "full_name"), + col_types = readr::cols(.default = readr::col_character()) + ) + dict$full_name <- gsub( + pattern = "\u0092", replacement = "'", + dict$full_name + ) assign(dictlang, dict, envir = .EurostatEnv) } get(dictlang, envir = .EurostatEnv) } - diff --git a/R/get_eurostat_geospatial.R b/R/get_eurostat_geospatial.R index 5f309a0a..6f7525c6 100755 --- a/R/get_eurostat_geospatial.R +++ b/R/get_eurostat_geospatial.R @@ -1,229 +1,285 @@ -#' @title Download Geospatial Data from GISGO -#' @description Downloads either a simple features (sf), SpatialPolygonDataFrame or a data_frame preprocessed using -#' \code{broom::tidy()}. -#' @param output_class A string. Class of object returned, -#' either \code{sf} \code{simple features}, \code{df} (\code{data_frame}) or -#' \code{spdf} (\code{SpatialPolygonDataFrame}) +#' @title Download Geospatial Data from GISCO +#' +#' @description Downloads either a simple features (sf) or a data_frame +#' of NUTS regions. This function is a wrapper of [giscoR::gisco_get_nuts()]. +#' This function requires to have installed the packages \CRANpkg{sf} and +#' \CRANpkg{giscoR}. +#' +#' @seealso [giscoR::gisco_get_nuts()] +#' @param output_class Class of object returned, +#' either `sf` `simple features` or `df` (`data_frame`). `spdf` output has +#' been soft-deprecated, the function would switch to `sf`. #' @param resolution Resolution of the geospatial data. One of -#' "60" (1:60million), "20" (1:20million), "10" (1:10million), "03" (1:3million) or "01" (1:1million). +#' * "60" (1:60million), +#' * "20" (1:20million) +#' * "10" (1:10million) +#' * "03" (1:3million) or +#' * "01" (1:1million). #' @param nuts_level Level of NUTS classification of the geospatial data. One of #' "0", "1", "2", "3" or "all" (mimics the original behaviour) #' @param year NUTS release year. One of -#' "2003", "2006", "2010", "2013" or "2016" -#' @param cache a logical whether to do caching. Default is \code{TRUE}. Affects -#' only queries from the bulk download facility. -#' @param update_cache a locigal whether to update cache. Can be set also with -#' options(eurostat_update = TRUE) -#' @param cache_dir a path to a cache directory. The directory have to exist. -#' The \code{NULL} (default) uses and creates -#' 'eurostat' directory in the temporary directory from -#' \code{\link{tempdir}}. Directory can also be set with -#' \code{option} eurostat_cache_dir. -#' @export -#' @details The data source URL is \url{http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units}. -#' @author Markus Kainu -#' @return a sf, data_frame or SpatialPolygonDataFrame. +#' "2003", "2006", "2010", "2013", "2016" or "2021" +#' @param cache a logical whether to do caching. Default is `TRUE`. +#' @param update_cache a logical whether to update cache. Can be set also with +#' `options(eurostat_update = TRUE)` +#' @param cache_dir a path to a cache directory. See +#' [set_eurostat_cache_dir()]. If `NULL` and the cache dir has not been set +#' globally the file would be stored in the [tempdir()]. +#' @param crs projection of the map: 4-digit +#' [EPSG code](https://spatialreference.org/ref/epsg/). One of: +#' * "4326" - WGS84 +#' * "3035" - ETRS89 / ETRS-LAEA +#' * "3857" - Pseudo-Mercator +#' +#' @param make_valid Deprecated +#' +#' @inheritDotParams giscoR::gisco_get_nuts -epsg +#' +#' @details +#' The objects downloaded from GISCO should contain all or some of the +#' following variable columns: +#' * **id**: JSON id code, the same as **NUTS_ID**. See **NUTS_ID** below for +#' further clarification. +#' * **LEVL_CODE**: NUTS level code: 0 (national level), 1 (major +#' socio-economic regions), 2 (basic regions for the application of regional +#' policies) or 3 (small regions). +#' * **NUTS_ID**: NUTS ID code, consisting of country code and numbers (1 for +#' NUTS 1, 2 for NUTS 2 and 3 for NUTS 3) +#' * **CNTR_CODE**: Country code: two-letter ISO code (ISO 3166 alpha-2), except +#' in the case of Greece (EL). +#' * **NAME_LATN**: NUTS name in local language, transliterated to Latin script +#' * **NUTS_NAME**: NUTS name in local language, in local script. +#' * **MOUNT_TYPE**: Mountain typology for NUTS 3 regions. +#' * 1: "where more than 50 % of the surface is covered by topographic +#' mountain areas" +#' * 2: "in which more than 50 % of the regional population lives in +#' topographic mountain areas" +#' * 3: "where more than 50 % of the surface is covered by topographic +#' mountain areas and where more than 50 % of the regional population lives +#' in these mountain areas" +#' * 4: non-mountain region / other region +#' * 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 and +#' non-EU countries) +#' * **URBN_TYPE**: Urban-rural typology for NUTS 3 regions. +#' * 1: predominantly urban region +#' * 2: intermediate region +#' * 3: predominantly rural region +#' * 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 +#' regions) +#' * **COAST_TYPE**: Coastal typology for NUTS 3 regions. +#' * 1: coastal (on coast) +#' * 2: coastal (>= 50% of population living within 50km of the coastline) +#' * 3: non-coastal region +#' * 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 +#' regions) +#' * **FID**: Same as NUTS_ID. +#' * **geo**: Same as NUTS_ID, added for for easier joins with dplyr. Consider +#' the status of this column "questioning" and use other columns for joins +#' when possible. +#' * **geometry**: geospatial information. +#' +#' @author +#' Markus Kainu , Diego Hernangomez +#' +#' @return a sf or data_frame +#' +#' @source +#' Data source: Eurostat +#' +#' © EuroGeographics for the administrative boundaries +#' +#' Data downloaded using \pkg{giscoR} +#' +#' @inheritSection eurostat-package Eurostat: Copyright notice and free re-use of data +#' @inheritSection eurostat-package Data source: GISCO - General Copyright +#' @inheritSection eurostat-package Data source: GISCO - Administrative Units / Statistical Units +#' +#' @family geospatial #' @examples -#' \dontrun{ -#' lp <- get_eurostat_geospatial(output_class = "sf", resolution = "60", nuts_level = "all") -#' lp %>% select(NUTS_ID) %>% plot() -#' lp <- get_eurostat_geospatial(output_class = "spdf", resolution = "60", nuts_level = "all") -#' spplot(lp, "STAT_LEVL_") -#' # or -#' lp <- get_eurostat_geospatial(output_class = "df", resolution = "60", nuts_level = "all") -#' ggplot(lp, aes(x=long,y=lat,group=group,fill=STAT_LEVL_),color="white") + geom_polygon() -#' } -#' -get_eurostat_geospatial <- function(output_class="sf",resolution="60", nuts_level = "all", year = "2016", - cache = TRUE, update_cache = FALSE, cache_dir = NULL){ - - eurostat_geodata_60_2016 <- NULL - LEVL_CODE <- NULL - # output_class <- NULL - - data("eurostat_geodata_60_2016", envir = environment(), package = "eurostat") - - # Check resolution is of correct format - resolution <- as.character(resolution) - resolution <- gsub("^0+", "", resolution) - if (!as.numeric(resolution) %in% c(1, 3, 10, 20, 60)) { - stop("Resolution should be one of 01, 1, 03, 3, 10, 20, 60") +#' \donttest{ +#' # Uses cached dataset +#' sf <- get_eurostat_geospatial( +#' output_class = "sf", +#' resolution = "60", +#' nuts_level = "all" +#' ) +#' # Downloads dataset from server +#' sf2 <- get_eurostat_geospatial( +#' output_class = "sf", +#' resolution = "20", +#' nuts_level = "all" +#' ) +#' df <- get_eurostat_geospatial( +#' output_class = "df", +#' nuts_level = "0" +#' ) +#' } +#' +#' @export +get_eurostat_geospatial <- function(output_class = "sf", + resolution = "60", + nuts_level = "all", year = "2016", + cache = TRUE, update_cache = FALSE, + cache_dir = NULL, crs = "4326", + make_valid = "DEPRECATED", ...) { + # nocov start + if (!requireNamespace("sf")) { + message("'sf' package is required for geospatial functionalities") + return(invisible()) } - resolution <- gsub("^1$", "01", resolution) - resolution <- gsub("^3$", "03", resolution) - - # Check year is of correct format - year <- as.character(year) - if (!as.numeric(year) %in% c(2003, 2006, 2010, 2013, 2016)) { - stop("Year should be one of 2003, 2006, 2010, 2013 or 2016") + # nocov end + # Simplified and leaving most of the heavy-lifting to giscoR + + # Deprecation messages + stopifnot(length(output_class) == 1L) + if (output_class == "spdf") { + message("'spdf' output deprecated. Switching to sf output") + output_class <- "sf" } - - if (as.numeric(year) == 2003 & as.numeric(resolution) == 60) { - stop("NUTS 2003 is not provided at 1:60 million resolution. Try 1:1 million, 1:3 million, 1:10 million or 1:20 million") + + if (!identical(make_valid, "DEPRECATED")) { + message("'make_valid' argument has been deprecated") } - message(" -COPYRIGHT NOTICE - -When data downloaded from this page - -is used in any printed or electronic publication, -in addition to any other provisions -applicable to the whole Eurostat website, -data source will have to be acknowledged -in the legend of the map and -in the introductory page of the publication -with the following copyright notice: - -- EN: (C) EuroGeographics for the administrative boundaries -- FR: (C) EuroGeographics pour les limites administratives -- DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen - -For publications in languages other than -English, French or German, -the translation of the copyright notice -in the language of the publication shall be used. - -If you intend to use the data commercially, -please contact EuroGeographics for -information regarding their licence agreements. - ") - - if (resolution == "60" & year == 2016){ - - if (nuts_level %in% c("all")){ - shp <- eurostat_geodata_60_2016 - } - if (nuts_level == "0") shp <- filter(eurostat_geodata_60_2016, LEVL_CODE == 0) - if (nuts_level == "1") shp <- filter(eurostat_geodata_60_2016, LEVL_CODE == 1) - if (nuts_level == "2") shp <- filter(eurostat_geodata_60_2016, LEVL_CODE == 2) - if (nuts_level == "3") shp <- filter(eurostat_geodata_60_2016, LEVL_CODE == 3) - - if (output_class == "df"){ - nuts_sp <- as(shp, "Spatial") - nuts_sp$id <- row.names(nuts_sp) - nuts_ff <- broom::tidy(nuts_sp) - shp <- left_join(nuts_ff,nuts_sp@data) - } - if (output_class == "spdf"){ - shp <- as(shp, "Spatial") + # Leaving only specific validations - rest of call would be handled by giscoR + output_class <- match.arg(as.character(output_class), c("sf", "df")) + + # Sanity check for nuts_level + stopifnot(length(nuts_level) == 1L) + nuts_level <- regmatches(nuts_level, regexpr("^(all|[0-9]+)", nuts_level)) + nuts_level <- match.arg(nuts_level, c("all", 0:3)) + + # Performance - If df requested resolution and crs are meaningless. Switching + # to 60 and 4326 for speed (except for 2003, no available) + if (output_class == "df") { + resolution <- "60" + crs <- "4326" + + if (as.integer(year) == 2003) resolution <- "20" + } + + + # If cache file requested get the info from the internal dataset + capture_dots <- list(...) + + # Check if the pre-set call to the function has been modified on + # relevant parameters + use_local <- all( + as.character(resolution) == "60", + as.character(year) == "2016", + isFALSE(update_cache), + as.character(crs) == "4326", + # Check dots are empty + length(capture_dots) == 0 + ) + + if (use_local) { + # Not modified - using dataset included with eurostat package + message("Extracting data from eurostat::eurostat_geodata_60_2016") + shp <- eurostat::eurostat_geodata_60_2016 + if (nuts_level != "all") { + shp <- shp[shp$LEVL_CODE == nuts_level, ] } - } else { + # Check if package "giscoR" is installed + # nocov start + if (!requireNamespace("giscoR")) { + message("'giscoR' package is required for geospatial functionalities") + return(invisible()) + } + # nocov end + + message(paste0( + "Extracting data using giscoR package, please report issues", + " on https://github.com/rOpenGov/giscoR/issues" + )) - # if (resolution != "60"){ - # if (resolution != "60" & year != 2013){ - - if (cache){ - # check option for update - update_cache <- update_cache | getOption("eurostat_update", FALSE) - - # get cache directory - if (is.null(cache_dir)){ - cache_dir <- getOption("eurostat_cache_dir", NULL) - if (is.null(cache_dir)){ - cache_dir <- file.path(tempdir(), "eurostat") - if (!file.exists(cache_dir)) dir.create(cache_dir) - } + # Manage cache: Priority is eurostat cache (if set) + # If not make use of giscoR default options + detect_eurostat_cache <- eur_helper_detect_cache_dir() + if (!is.null(cache_dir)) { + # Already set by the user, no need message + cache_dir <- eur_helper_cachedir(cache_dir) + } else if (identical( + detect_eurostat_cache, + file.path(tempdir(), "eurostat") + )) { + # eurostat not set, using default giscoR cache management + message("Cache management as per giscoR. see 'giscoR::gisco_get_nuts()'") } else { - if (!file.exists(cache_dir)) { - stop("The folder ", cache_dir, " does not exist") - } + cache_dir <- eur_helper_cachedir(cache_dir) } - - # cache filename - cache_file <- file.path(cache_dir, - paste0( - output_class, resolution, nuts_level, year, ".RData") + + # giscoR call with parameters + # on input errors giscoR would show warnings, etc + shp <- giscoR::gisco_get_nuts( + resolution = resolution, + nuts_level = nuts_level, year = year, + cache = cache, update_cache = update_cache, + cache_dir = cache_dir, epsg = crs, + ... ) } - - # if cache = FALSE or update or new: dowload else read from cache - if (!cache || update_cache || !file.exists(cache_file)){ - - - if (nuts_level %in% c("0","all")){ - resp <- GET(paste0("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/geojson/NUTS_RG_",resolution,"M_",year,"_4258_LEVL_0.geojson")) - nuts0 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) - } - if (nuts_level %in% c("1","all")){ - resp <- GET(paste0("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/geojson/NUTS_RG_",resolution,"M_",year,"_4258_LEVL_1.geojson")) - nuts1 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) - } - if (nuts_level %in% c("2","all")){ - resp <- GET(paste0("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/geojson/NUTS_RG_",resolution,"M_",year,"_4258_LEVL_2.geojson")) - nuts2 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) - } - if (nuts_level %in% c("3","all")){ - resp <- GET(paste0("http://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/geojson/NUTS_RG_",resolution,"M_",year,"_4258_LEVL_3.geojson")) - nuts3 <- st_read(content(resp, as="text"), stringsAsFactors = FALSE, quiet = TRUE) - } - if (nuts_level %in% c("all")){ - shp <- rbind(nuts0,nuts1,nuts2,nuts3) - } - if (nuts_level == "0") shp <- nuts0 - if (nuts_level == "1") shp <- nuts1 - if (nuts_level == "2") shp <- nuts2 - if (nuts_level == "3") shp <- nuts3 - - - if (output_class == "df"){ - nuts_sp <- as(shp, "Spatial") - nuts_sp$id <- row.names(nuts_sp) - nuts_ff <- broom::tidy(nuts_sp) - shp <- left_join(nuts_ff,nuts_sp@data) - } - if (output_class == "spdf"){ - shp <- as(shp, "Spatial") - } - - } - } - if (resolution != "60" & year != 2016){ - if (cache & file.exists(cache_file)) { - cf <- path.expand(cache_file) - message(paste("Reading cache file", cf)) - # y <- readRDS(cache_file) - load(file = cache_file) - if (output_class == "sf") message(paste("sf at resolution 1:", resolution, " from year ",year," read from cache file: ", cf)) - if (output_class == "df") message(paste("data_frame at resolution 1:", resolution, " from year ",year," read from cache file: ", cf)) - if (output_class == "spdf") message(paste("SpatialPolygonDataFrame at resolution 1:", resolution, " from year ",year," read from cache file: ", cf)) - } - - # if update or new: save - if (cache && (update_cache || !file.exists(cache_file))){ - save(shp, file = cache_file) - if (output_class == "sf") message(paste("sf at resolution 1:", resolution, " cached at: ", path.expand(cache_file))) - if (output_class == "df") message(paste("data_frame at resolution 1:", resolution, " cached at: ", path.expand(cache_file))) - if (output_class == "spdf") message(paste("SpatialPolygonDataFrame at resolution 1:", resolution, " cached at: ", path.expand(cache_file))) - } + # Just to capture potential NULL outputs from giscoR - this can happen + # on some errors + if (is.null(shp)) { + return(NULL) } - - if (resolution == "60" & year == 2016){ - if (output_class == "sf") message(paste("sf at resolution 1:60 read from local file")) - if (output_class == "df") message(paste("data_frame at resolution 1:60 read from local file")) - if (output_class == "spdf") message(paste("SpatialPolygonDataFrame at resolution 1:60 read from local file")) - + + # Post-data treatments + # Manage col names + shp <- geo_names(shp) + + # to df + if (output_class == "df") { + # Remove geometry + shp <- sf::st_drop_geometry(shp) } - - message(" -# -------------------------- -HEADS UP!! - -Function now returns the data in 'sf'-class (simple features) -by default which is different -from previous behaviour's 'SpatialPolygonDataFrame'. - -If you prefer either 'SpatialPolygonDataFrame' or -fortified 'data_frame' (for ggplot2::geom_polygon), -please specify it explicitly to 'output_class'-argument! - -# -------------------------- - ") - - # Adding a `geo` column for easier joins with dplyr - shp$geo <- shp$NUTS_ID + return(shp) +} + + +# Helper function, add names and reorder +geo_names <- function(x) { + # Case for border (BN), there are no NUTS_ID , do nothing + if (!"NUTS_ID" %in% names(x)) { + return(x) + } + + + # Add `id` and `geo` column for easier joins with dplyr + x$geo <- x$NUTS_ID + x$id <- x$geo + + # Arrange names in proper order + the_geom <- sf::st_geometry(x) + the_df <- sf::st_drop_geometry(x) + rest <- c( + "id", "LEVL_CODE", "NUTS_ID", "CNTR_CODE", "NAME_LATN", "NUTS_NAME", + "MOUNT_TYPE", "URBN_TYPE", "COAST_TYPE", "FID", "geo" + ) + + # Check what needed columns are not present in the source file + miss_cols <- setdiff(unique(rest), names(the_df)) + extra_cols <- setdiff(names(the_df), rest) + + + # Add missing cols with NAs + list_df <- lapply(miss_cols, function(x) { + template_df <- data.frame(somecol = NA) + names(template_df) <- x + template_df + }) + new_df <- dplyr::bind_cols(c(list(the_df), list_df)) + + # Final column order + order_cols <- unique(c(rest, extra_cols)) + xend <- new_df[, order_cols] + + # Back to sf + final_sf <- sf::st_sf(xend, geometry = the_geom) + final_sf } diff --git a/R/get_eurostat_json.R b/R/get_eurostat_json.R index eb76ca34..3549e327 100755 --- a/R/get_eurostat_json.R +++ b/R/get_eurostat_json.R @@ -1,120 +1,337 @@ #' @title Get Data from Eurostat API in JSON #' @description Retrieve data from Eurostat API in JSON format. -#' @details +#' @details #' Data to retrieve from -#' \href{http://ec.europa.eu/eurostat/web/json-and-unicode-web-services}{The -#' Eurostat Web Services} can be specified with filters. Normally, it is -#' better to use JSON query through \code{\link{get_eurostat}}, than to use -#' \code{\link{get_eurostat_json}} directly. -#' +#' [The Eurostat Web Services](https://ec.europa.eu/eurostat/web/main/data/web-services) +#' can be specified with filters. Normally, it is +#' better to use JSON query through [get_eurostat()], than to use +#' [get_eurostat_json()] directly. +#' #' Queries are limited to 50 sub-indicators at a time. A time can be #' filtered with fixed "time" filter or with "sinceTimePeriod" and -#' "lastTimePeriod" filters. A \code{sinceTimePeriod = 2000} returns -#' observations from 2000 to a last available. A \code{lastTimePeriod = 10} -#' returns a 10 last observations. -#' -#' To use a proxy to connect, a \code{\link[httr]{use_proxy}} can be -#' passed to \code{\link[httr]{GET}}. For example -#' \code{get_eurostat_json(id, filters, -#' config = httr::use_proxy(url, port, username, password))}. -#' -#' @param id A code name for the dataset of interested. See the table of -#' contents of eurostat datasets for more details. -#' @param filters A named list of filters. Names of list objects are Eurostat -#' variable codes and values are vectors of observation codes. If \code{NULL} -#' (default) the whole dataset is returned. See details for more on filters -#' and limitations per query. -#' @param lang A language used for metadata (en/fr/de). -#' @param type A type of variables, "code" (default), "label" or "both". The -#' "both" will return a data_frame with named vectors, labels as values and -#' codes as names. -#' @param stringsAsFactors if \code{TRUE} (the default) variables are converted -#' to factors in original Eurostat order. If \code{FALSE} they are returned as -#' a character. -#' @param ... Other arguments passed on to \code{\link[httr]{GET}}. For example -#' a proxy parameters, see details. -#' . +#' "lastTimePeriod" filters. A `sinceTimePeriod = 2000` returns +#' observations from 2000 to a last available. A `lastTimePeriod = 10` +#' returns a 10 last observations. See "Filtering datasets" section below +#' for more detailed information about filters. #' -#' @return A dataset as a data_frame. -#' @export -#' @author Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu -#' \email{ropengov-forum@@googlegroups.com} -#' \url{http://github.com/ropengov/eurostat} +#' To use a proxy to connect, proxy arguments can be +#' passed to [httr2::req_perform()] via [httr2::req_proxy()] - see latter +#' function documentation for parameter names that can be passed with `...`. +#' A non-functional example: +#' `get_eurostat_json(id, filters, proxy = TRUE, url = "127.0.0.1", port = 80)`. +#' +#' When retrieving data from Eurostat JSON API the user may encounter errors. +#' For end user convenience, we have provided a ready-made internal dataset +#' `sdmx_http_errors` that contains descriptive labels and descriptions about +#' the possible interpretation or cause of each error. These messages are +#' returned if the API returns a status indicating a HTTP error +#' (400 or greater). +#' +#' The Eurostat implementation seems to be based on SDMX 2.1, which is the +#' reason we've used SDMX Standards guidelines as a supplementary source +#' that we have included in the dataset. What this means in practice is that +#' the dataset contains error codes and their mappings that are not mentioned +#' in the Eurostat website. We hope you never encounter them. +#' +#' @param proxy Use proxy, TRUE or FALSE (default). +#' @inheritParams get_eurostat +#' @inheritDotParams httr2::req_proxy +#' @inherit get_eurostat references +#' +#' @return A dataset as an object of `data.frame` class. +#' @author +#' Przemyslaw Biecek, Leo Lahti, Janne Huovari Markus Kainu and Pyry Kantanen + #' @examples -#' \dontrun{ -#' tmp <- get_eurostat_json("cdh_e_fos") -#' yy <- get_eurostat_json(id = "nama_gdp_c", filters = list(geo=c("EU28", "FI"), -#' unit="EUR_HAB", -#' indic_na="B1GM")) +#' \dontrun{ +#' # Generally speaking these queries would be done through get_eurostat +#' tmp <- get_eurostat_json("nama_10_gdp") +#' yy <- get_eurostat_json("nama_10_gdp", filters = list( +#' geo = c("FI", "SE", "EU28"), +#' time = c(2015:2023), +#' lang = "FR", +#' na_item = "B1GQ", +#' unit = "CLV_I10" +#' )) +#' +#' # TIME_PERIOD filter works also with the new JSON API +#' yy2 <- get_eurostat_json("nama_10_gdp", filters = list( +#' geo = c("FI", "SE", "EU28"), +#' TIME_PERIOD = c(2015:2023), +#' lang = "FR", +#' na_item = "B1GQ", +#' unit = "CLV_I10" +#' )) +#' +#' # An example from get_eurostat +#' dd <- get_eurostat("nama_10_gdp", +#' filters = list( +#' geo = "FI", +#' na_item = "B1GQ", +#' unit = "CLV_I10" +#' )) #' } +#' @importFrom httr2 request req_user_agent req_retry req_perform req_proxy +#' @importFrom httr2 resp_body_json resp_content_type resp_is_error req_error +#' @importFrom jsonlite fromJSON +#' @importFrom tibble as_tibble +#' @importFrom stringr str_glue +#' @importFrom httr2 %>% +#' +#' @inheritSection eurostat-package Data source: Eurostat API Statistics (JSON API) +#' @inheritSection eurostat-package Filtering datasets +#' @inheritSection eurostat-package Eurostat: Copyright notice and free re-use of data +#' @inheritSection eurostat-package Citing Eurostat data +#' @inheritSection eurostat-package Disclaimer: Availability of filtering functionalities +#' +#' @seealso +#' [httr2::req_proxy()] +#' #' @keywords utilities database -get_eurostat_json <- function(id, filters = NULL, - type = c("code", "label", "both"), - lang = c("en", "fr", "de"), - stringsAsFactors = default.stringsAsFactors(), - ...){ - - #get response - url <- eurostat_json_url(id = id, filters = filters, lang = lang) - resp <- httr::GET(url, ...) - status <- httr::status_code(resp) - - # check status and get json - msg <- ". Some datasets are not accessible via the eurostat interface. You can try to search the data manually from the comext database at http://epp.eurostat.ec.europa.eu/newxtweb/ or bulk download facility at http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing or annual Excel files http://ec.europa.eu/eurostat/web/prodcom/data/excel-files-nace-rev.2" - if (status == 200){ - jdat <- jsonlite::fromJSON(url) - } else if (status == 400){ - stop("Failure to get data. Probably invalid dataset id. Status code: ", - status, msg) - } else if (status == 500){ - stop("Failure to get data. Probably filters did not return any data - or data exceeded query size limitation. Status code: ", status, msg) - } else { - stop("Failure to get data. Status code: ", status, msg) +#' @export +get_eurostat_json <- function(id, + filters = NULL, + type = "code", + lang = "en", + stringsAsFactors = FALSE, + proxy = FALSE, + ...) { + + ## Special products that must be built to matrix + ## User is prompted to halt and use iotables::iotables_download() + user_want_stop <- special_id_values(id) + if (user_want_stop) { + return(NULL) + } + + # Check if you have access to ec.europe.eu. + if (!check_access_to_data()) { + # nocov start + message("You have no access to ec.europe.eu. + Please check your connection and/or review your proxy settings") + # nocov end } + + # construct url + url <- eurostat_json_url(id = id, filters = filters, lang = lang) + # set user agent with version + # ua <- httr::user_agent(paste0("eurostat_", + # packageDescription("eurostat", fields = "Version"))) + # ua <- httr::user_agent("https://github.com/rOpenGov/eurostat") - # get json data - dims <- jdat[[1]]$dimension - ids <- dims$id + if (proxy == TRUE) { + # Check if "..." has arguments needed for proxy + args <- list(...) + dot_url <- dot_port <- dot_username <- dot_password <- NULL + if("url" %in% names(args)) dot_url <- args$url + if("port" %in% names(args)) dot_port <- as.numeric(args$port) + if("username" %in% names(args)) dot_username <- args$username + if("password" %in% names(args)) dot_port <- args$password + if("auth" %in% names(args)) { + dot_auth <- args$auth + } else { + dot_auth <- "basic" + } - dims_list <- lapply(dims[rev(ids)], function(x){ + resp <- httr2::request(url) %>% + httr2::req_user_agent(string = "https://github.com/rOpenGov/eurostat") %>% + httr2::req_proxy(url = dot_url, + port = dot_port, + username = dot_username, + password = dot_password, + auth = dot_auth) %>% + httr2::req_retry(max_tries = 3, max_seconds = 60) %>% + httr2::req_error(is_error = function(resp) FALSE) %>% + httr2::req_perform() + } + resp <- httr2::request(url) %>% + httr2::req_user_agent(string = "https://github.com/rOpenGov/eurostat") %>% + httr2::req_retry(max_tries = 3, max_seconds = 60) %>% + httr2::req_error(is_error = function(resp) FALSE) %>% + httr2::req_perform() + + # RETRY GET 3 times + # resp <- httr::RETRY(verb = "GET", + # url = url, + # times = 3, + # terminate_on = c(404), + # ua) + + # Source: httr vignette "Best practices for API packages" [httr_vignette] + if (httr2::resp_content_type(resp) != "application/json") { + stop("API did not return json", call. = FALSE) + } + + # parse JSON data into R object (as per [httr_vignette]) + # assume that content encoding is utf8 + result <- httr2::resp_body_json( + resp = resp, + simplifyVector = TRUE) + + if (httr2::resp_is_error(resp)) { + + # These objects are only needed if there is an error + json_data_frame <- as.data.frame(result) + status <- json_data_frame$error.status + id <- json_data_frame$error.id + label <- json_data_frame$error.label + + # sdmx_http_errors is stored in R/sysdata.rda + # same data in extdata/sdmx_to_http_error_mapping.csv file + faultstring <- sdmx_http_errors$sdmx.faultstring[which(sdmx_http_errors$sdmx.faultcode == id & sdmx_http_errors$http.status_code == status)] + status_code_label <- sdmx_http_errors$http.status_code.label[grep(status, sdmx_http_errors$http.status_code)][1] + + if (status == 416) { + stop(stringr::str_glue("Too many categories have been requested. Maximum is 50.\n", + "HTTP status: {status} ({status_code_label})\n", + " Error id: {id} ({faultstring})\n", + " Error label from API: {label}") + ) + } else { + stop(stringr::str_glue("\n", + "HTTP status: {status} ({status_code_label})\n", + " Error id: {id} ({faultstring})\n", + " Error label from API: {label}") + ) + } + } + + if (resp$status_code == 200 && length(result$value) == 0) { + + # nocov start + msg <- paste(" Please also note that some datasets are not accessible via", + "the eurostat API interface. You can try to search the data", + "manually from the comext database at", + "https://ec.europa.eu/eurostat/comext/newxtweb/ or explore", + "data at https://ec.europa.eu/eurostat/web/main/data/database") + # nocov end + + stop(paste("HTTP status: 200, but the dataset didn't have any values.\n", + " Editing the query parameters may resolve the issue.\n", + msg) + ) + } + + #status <- httr::status_code(resp) + + # check status and get json + jdat <- result + # no need to download same data twice + # jdat <- jsonlite::fromJSON(url, simplifyVector = TRUE) + + # dims <- jdat[[1]]$dimension # Was called like this in API v1.1 + dims <- jdat$dimension # Switched to this with API v2.1 + # ids <- dims$id # v1.1 + ids <- jdat$id # v2.1 + + dims_list <- lapply(dims[rev(ids)], function(x) { y <- x$category$label - if (type[1] == "label") { + if (type == "label") { y <- unlist(y, use.names = FALSE) - } else if (type[1] == "code"){ + } else if (type == "code") { y <- names(unlist(y)) - } else if (type[1] == "both"){ + } else if (type == "both") { y <- unlist(y) } else { stop("Invalid type ", type) } }) - - variables <- expand.grid(dims_list, KEEP.OUT.ATTRS = FALSE, - stringsAsFactors = stringsAsFactors) - - dat <- data.frame(variables[rev(names(variables))], values = jdat[[1]]$value) - - as_data_frame(dat) - -} + variables <- expand.grid(dims_list, + KEEP.OUT.ATTRS = FALSE, + stringsAsFactors = stringsAsFactors + ) + + # dat <- data.frame(variables[rev(names(variables))], values = jdat[[1]]$value) # v1.1 + dat <- as.data.frame(variables[rev(names(variables))]) + vals <- unlist(jdat$value, use.names = FALSE) + dat$values <- rep(NA, nrow(dat)) + inds <- 1 + as.numeric(names(jdat$value)) # 0-indexed + if (!length(vals) == length(inds)) { + stop("Complex indexing not implemented.") + } + dat$values[inds] <- vals + + tibble::as_tibble(dat) +} -# Internal function to build json url -eurostat_json_url <- function(id, filters, lang){ +#' @title Internal function to build json url +#' @description Internal function to build json url +#' @importFrom utils hasName +#' @importFrom httr2 url_parse url_build +#' @noRd +#' @keywords internal utilities +eurostat_json_url <- function(id, filters = NULL, lang = NULL) { # prepare filters for query filters2 <- as.list(unlist(filters)) + # Give items names without numbers names(filters2) <- rep(names(filters), lapply(filters, length)) - - # prepare url - url_list <- list(scheme = "http", - hostname = "ec.europa.eu", - path = file.path("eurostat/wdds/rest/data/v1.1/json", - lang[1], id), - query = filters2) - class(url_list) <- "url" - url <- httr::build_url(url_list) + + # The “format” parameter’s only possible value is "JSON". + if (!hasName(filters2, "format")) { + filters2$format <- "JSON" + } + + filters2$lang <- check_lang(lang) + + host_url <- "https://ec.europa.eu/eurostat/api/dissemination/" + service <- "statistics/" + version <- "1.0/" + response_type <- "data/" + datasetCode <- id + + # Parse host_url and add relevant information in the standard list + url_list <- httr2::url_parse(host_url) + # Paste "statistics/1.0/data/{id}" at the end of the fixed part of the URL + url_list$path <- paste0(url_list$path, + service, + version, + response_type, + datasetCode) + url_list$query <- filters2 + + url <- httr2::url_build(url_list) url -} \ No newline at end of file +} + +# Internal function to give warning if symmetric input-output tables need to download into strict matirx formats. +special_id_values <- function(id) { + siot_id_codes <- c( + "naio_10_cp1700", "naio_10_pyp1700", + "naio_10_cp1750", "naio_10_pyp1750", + "naio_10_cp15", "naio_10_cp16", + "naio_10_cp1610", "naio_10_pyp1610", + "naio_10_cp1620", "naio_10_pyp1620", + "naio_10_cp1630", "naio_10_pyp1630" + ) + if (id %in% siot_id_codes) { + message( + "The requested product id is a special input-output matrix.", + "\nTo keep the matrix structure for further use, download it with iotables::iotables_download().\nThe iotables package is an extension for such cases to the eurostat package." + ) + answer <- readline(prompt = "Do you want to stop downloading now? [y/n] ") + if (tolower(answer) == "y") TRUE else FALSE + } else { + # By default evaluates to FALSE and no interruption happens + FALSE + } +} + +# Internal function to give warning if sub-national geo codes need validation +is_regional_nuts_present <- function(dat, geo) { + potentional_regional_codes <- unique(dat$geo)[nchar(unique(dat$geo)) > 2] + + potentional_regional_codes <- potentional_regional_codes[!substr(potentional_regional_codes, 1, 2) %in% c("EU", "EA")] + + if (length(potentional_regional_codes) > 0) { + types_found <- paste(sort(unique(validate_geo_code(potentional_regional_codes, nuts_year = 2021))), collapse = ", ") + message( + "The following sub-national geographical codes present in the dataset:\n", types_found, + "\nRegional and metropolitian area boundaries, codes and names are changing frequently.", + "\nSee ?validate_geo_code, ?validate_nuts_regions and ?recode_nuts or the", + "\n'Mapping Regional Data, Mapping Metadata Problems' vignette for a tutorial." + ) + } +} diff --git a/R/get_eurostat_raw.R b/R/get_eurostat_raw.R index dd180531..edb01459 100755 --- a/R/get_eurostat_raw.R +++ b/R/get_eurostat_raw.R @@ -1,50 +1,103 @@ -#' @title Download Data from Eurostat Database -#' @description Download data from the eurostat database. -#' @param id A code name for the dataset of interested. See the table of -#' contents of eurostat datasets for more details. -#' @return A dataset in tibble format. First column contains comma -#' separated codes of cases. Other columns usually corresponds to -#' years and column names are years with preceding X. Data is in -#' character format as it contains values together with eurostat -#' flags for data. -#' @seealso \code{\link{get_eurostat}}. -#' @details Data is downloaded from -#' \url{http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing} and transformed into tabular format. -#' @references see citation("eurostat") -#' @author Przemyslaw Biecek, Leo Lahti and Janne Huovari \email{ropengov-forum@@googlegroups.com} -#' @examples \dontrun{ -#' tmp <- eurostat:::get_eurostat_raw("educ_iste") -#' head(tmp) -#' } +#' @title Download Data from Eurostat Dissemination API +#' @description Download data from the eurostat database through the new +#' dissemination API. +#' @inheritParams get_eurostat +#' @return A dataset in tibble format. First column contains comma +#' separated codes of cases. Other columns usually corresponds to +#' years and column names are years with preceding X. Data is in +#' character format as it contains values together with eurostat +#' flags for data. +#' @seealso +#' [get_eurostat()] +#' +#' @inheritSection eurostat-package Data source: Eurostat SDMX 2.1 Dissemination API +#' @inheritSection eurostat-package Eurostat: Copyright notice and free re-use of data +#' @inheritSection eurostat-package Citing Eurostat data +#' @inheritSection eurostat-package Disclaimer: Availability of filtering functionalities +#' @references +#' See `citation("eurostat")`: +#' +#' ```{r, echo=FALSE, comment="#" } +#' citation("eurostat") +#' ``` +#' +#' @author Przemyslaw Biecek, Leo Lahti, Janne Huovari and Pyry Kantanen +#' @examplesIf check_access_to_data() +#' \donttest{ +#' eurostat:::get_eurostat_raw("educ_iste") +#' } +#' +#' @importFrom readr read_tsv cols col_character +#' @importFrom utils download.file +#' @importFrom tibble as_tibble +#' @importFrom curl curl_download +#' @importFrom data.table fread +#' #' @keywords utilities database -get_eurostat_raw <- function(id) { - +get_eurostat_raw <- function(id, use.data.table = FALSE) { base <- getOption("eurostat_url") - url <- paste0(base, - "estat-navtree-portlet-prod/BulkDownloadListing?sort=1&file=data%2F", - id, ".tsv.gz") + url <- paste0( + base, + "api/dissemination/sdmx/2.1/data/", + id, + "?format=TSV&compressed=true" + ) tfile <- tempfile() on.exit(unlink(tfile)) + + if (.Platform$OS.type == "windows") { + curl::curl_download(url = url, destfile = tfile) + } else { + # R Packages (2e): Restore state with base::on.exit() + # timeout = 120 should in most cases be enough for even the biggest datasets + op <- options(timeout = 120) + on.exit(options(op), add = TRUE) + utils::download.file(url, tfile) + } - # download and read file - utils::download.file(url, tfile) - - dat <- readr::read_tsv(gzfile(tfile), na = ":", - col_types = readr::cols(.default = readr::col_character())) - + if (!use.data.table) { + # OLD CODE + dat <- readr::read_tsv(gzfile(tfile), + na = ":", + progress = TRUE, + col_types = readr::cols(.default = readr::col_character()) + ) + } else if (use.data.table) { + # NEW CODE: data.table + dat <- data.table::fread(cmd = paste("gzip -dc", tfile), + na.strings = ":", + header = TRUE, + colClasses = "character") + + # OLD CODE + # data.table object does not need to be converted into a tibble at this + # point as it will handled by data.table functions in tidy_eurostat. + # dat <- tibble::as_tibble(dat) + } + # check validity - - if (ncol(dat) < 2 | nrow(dat) < 1) { - msg <- ". Some datasets are not accessible via the eurostat interface. You can try to search the data manually from the comext database at http://epp.eurostat.ec.europa.eu/newxtweb/ or bulk download facility at http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing or annual Excel files http://ec.europa.eu/eurostat/web/prodcom/data/excel-files-nace-rev.2" + if (ncol(dat) < 2 || nrow(dat) < 1) { + # nocov start + msg <- paste0( + ". Some datasets (for instance the comext type) are not ", + "accessible via the eurostat interface. You can try to ", + "search the data manually from the comext database at ", + "http://epp.eurostat.ec.europa.eu/newxtweb/ ", + "or bulk download facility at ", + "http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing ", + "or annual Excel files at ", + "http://ec.europa.eu/eurostat/web/prodcom/data/excel-files-nace-rev.2" + ) + # nocov end + if (grepl("does not exist or is not readable", dat[1])) { - stop(id, " does not exist or is not readable", msg) - } else { + } else { stop(paste("Could not download ", id, msg)) } } - + dat } diff --git a/R/get_eurostat_toc.R b/R/get_eurostat_toc.R index 1e93d923..3c1d13bf 100755 --- a/R/get_eurostat_toc.R +++ b/R/get_eurostat_toc.R @@ -1,30 +1,82 @@ #' @title Download Table of Contents of Eurostat Data Sets -#' @description Download table of contents (TOC) of eurostat datasets. -#' @return A tibble with eight columns -#' \itemize{ -#' \item{title}{The name of dataset of theme} -#' \item{code}{The codename of dataset of theme, will be used by the eurostat and get_eurostat_raw functions.} -#' \item{type}{Is it a dataset, folder or table.} -#' \item{last.update.of.data, last.table.structure.change, data.start, data.end}{Dates.} -#' } -#' @export -#' @seealso \code{\link{get_eurostat}}, \code{\link{search_eurostat}}. -#' @details The TOC is downloaded from \url{http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&file=table_of_contents_en.txt}. The values in column 'code' should be used to download a selected dataset. -#' @references See citation("eurostat"). -#' @author Przemyslaw Biecek and Leo Lahti \email{ropengov-forum@@googlegroups.com} -#' @examples \dontrun{tmp <- get_eurostat_toc(); head(tmp)} +#' @description Download table of contents (TOC) of eurostat datasets. +#' @details In the downloaded Eurostat Table of Contents the 'code' column +#' values are refer to the function 'id' that is used as an argument in certain +#' functions when downloading datasets. +#' @return A tibble with nine columns: +#' \describe{ +#' \item{title}{Dataset title in English (default)} +#' \item{code}{ Each item (dataset, table and folder) of the TOC has a +#' unique code which allows it to be identified in the API. Used in the +#' [get_eurostat()] and [get_eurostat_raw()] functions to retrieve datasets.} +#' \item{type}{dataset, folder or table} +#' \item{last.update.of.data}{Date, indicates the last time the +#' dataset/table was updated (format `DD.MM.YYYY` or `%d.%m.%Y`)} +#' \item{last.table.structure.change}{Date, indicates the last time the +#' dataset/table structure was modified (format `DD.MM.YYYY` or `%d.%m.%Y`)} +#' \item{data.start}{Date of the oldest value included in the dataset +#' (if available) (format usually `YYYY` or `%Y` but can also be `YYYY-MM`, +#' `YYYY-MM-DD`, `YYYY-SN`, `YYYY-QN` etc.)} +#' \item{data.end}{Date of the most recent value included in the dataset +#' (if available) (format usually `YYYY` or `%Y` but can also be `YYYY-MM`, +#' `YYYY-MM-DD`, `YYYY-SN`, `YYYY-QN` etc.)} +#' \item{values}{Number of actual values included in the dataset} +#' \item{hierarchy}{Hierarchy of the data navigation tree, represented +#' in the original txt file by a 4-spaces indentation prefix in the title} +#' } +#' +#' @seealso [get_eurostat()], [search_eurostat()] +#' @inheritSection eurostat-package Data source: Eurostat Table of Contents +#' @inherit get_eurostat references +#' @inheritParams get_eurostat +#' +#' @author +#' Przemyslaw Biecek, Leo Lahti and Pyry Kantanen +#' +#' @examplesIf check_access_to_data() +#' \donttest{ +#' tmp <- get_eurostat_toc() +#' head(tmp) +#' +#' # Convert columns containing dates as character into Date class +#' # Last update of data +#' tmp[[4]] <- as.Date(tmp[[4]], format = c("%d.%m.%Y")) +#' # Last table structure change +#' tmp[[5]] <- as.Date(tmp[[5]], format = c("%d.%m.%Y")) +#' # Data start, contains several formats (date, week, month quarter, semester) +#' # Unfortunately semesters are not directly supported so they need to be +#' # changed into quarters +#' tmp$data.start <- gsub("S2", "Q3", tmp$data.start) +#' tmp$data.start <- lubridate::as_date( +#' x = tmp$data.start, +#' format = c("%Y", "%Y-Q%q", "%Y-W%W", "%Y-S%q", "%Y-%m-%d", "%Y-%m") +#' ) +#' # Data end, same as data start +#' tmp$data.end <- gsub("S2", "Q3", tmp$data.end) +#' tmp$data.end <- lubridate::as_date( +#' x = tmp$data.end, +#' format = c("%Y", "%Y-Q%q", "%Y-W%W", "%Y-S%q", "%Y-%m-%d", "%Y-%m") +#' ) +#' } +#' #' @keywords utilities database -get_eurostat_toc <- function() { - set_eurostat_toc() - invisible(get(".eurostatTOC", envir = .EurostatEnv)) +#' @export +get_eurostat_toc <- function(lang = "en") { + + lang <- check_lang(lang) + + language_version <- switch(lang, + en = ".eurostatTOC", + fr = ".eurostatTOC_fr", + de = ".eurostatTOC_de") + + set_eurostat_toc(lang = lang) + + invisible(get(language_version, envir = .EurostatEnv)) } -# @describeIn get_eurostat_toc Old deprecated version -# @export -#getEurostatTOC <- function() { -# .Deprecated("get_eurostat_toc") -# get_eurostat_toc() -#} - - - +clean_eurostat_toc <- function() { + objects_in_env <- objects(envir = .EurostatEnv, all.names = TRUE) + toc_objects_in_env <- objects_in_env[grep(".eurostatTOC", objects_in_env)] + remove(list = toc_objects_in_env, envir = .EurostatEnv) +} diff --git a/R/harmonize_country_code.R b/R/harmonize_country_code.R index 97426e2d..55a6a1aa 100755 --- a/R/harmonize_country_code.R +++ b/R/harmonize_country_code.R @@ -4,23 +4,23 @@ #' represent Greece, and UK (not GB) is used to represent the #' United Kingdom. This function turns country codes into to ISO #' 3166-1 alpha-2. -#' @param x A character or a factor vector of eurostat countycodes. +#' @param x A character or a factor vector of eurostat countycodes. #' @export -#' @author Janne Huovari \email{janne.huovari@@ptt.fi} +#' @author Janne Huovari #' @return a vector. -#' @examples -#' \dontrun{ -#' lp <- get_eurostat("nama_aux_lp") -#' lp$geo <- harmonize_country_code(lp$geo) -#' } - -harmonize_country_code <- function (x) { - - if (is.factor(x)){ +#' @family helpers +#' @examplesIf check_access_to_data() +#' \donttest{ +#' lp <- get_eurostat("nama_10_lp_ulc") +#' lp$geo <- harmonize_country_code(lp$geo) +#' } +#' +harmonize_country_code <- function(x) { + if (is.factor(x)) { levels(x) <- harmonize_country_code(levels(x)) } else { x <- gsub("EL", "GR", x) - x <- gsub("UK", "GB", x) + x <- gsub("UK", "GB", x) } - x -} \ No newline at end of file + x +} diff --git a/R/label_eurostat.R b/R/label_eurostat.R index 9b478a6f..bf2ad8bf 100755 --- a/R/label_eurostat.R +++ b/R/label_eurostat.R @@ -1,63 +1,87 @@ -#' @title Get Eurostat Codes +#' @title Get Eurostat Codes for data downloaded from new dissemination API #' @description Get definitions for Eurostat codes from Eurostat dictionaries. -#' @details A character or a factor vector of codes returns a corresponding -#' vector of definitions. \code{label_eurostat} labels also data_frames from -#' \code{\link{get_eurostat}}. For vectors a dictionary name have to be -#' supplied. For data_frames dictonary names are taken from column names. -#' "time" and "values" columns are returned as they were, so you can supply -#' data_frame from \code{\link{get_eurostat}} and get data_frame with +#' @details A character or a factor vector of codes returns a corresponding +#' vector of definitions. [label_eurostat()] labels also data_frames from +#' [get_eurostat()]. For vectors a dictionary name have to be +#' supplied. For data_frames dictionary names are taken from column names. +#' "time" and "values" columns are returned as they were, so you can supply +#' data_frame from [get_eurostat()] and get data_frame with #' definitions instead of codes. -#' -#' Some Eurostat dictionaries includes dublicated labels. By default -#' dublicated labels cause an error, but they can be fixed automatically -#' with \code{fix_duplicated = TRUE}. -#' +#' +#' Some Eurostat dictionaries includes duplicated labels. By default +#' duplicated labels cause an error, but they can be fixed automatically +#' with `fix_duplicated = TRUE`. +#' #' @param x A character or a factor vector or a data_frame. #' @param dic A string (vector) naming eurostat dictionary or dictionaries. -#' If \code{NULL} (default) dictionry names taken from column names of +#' If `NULL` (default) dictionary names taken from column names of #' the data_frame. #' @param code For data_frames names of the column for which also code columns #' should be retained. The suffix "_code" is added to code column names. #' @param eu_order Logical. Should Eurostat ordering used for label levels. #' Affects only factors. -#' @param lang A character, code for language. Available are "en" (default), -#' "fr" and "de". -#' @param countrycode A \code{NULL} or a name of the coding scheme for -#' the \code{\link[countrycode]{countrycode}} +#' @param countrycode A `NULL` or a name of the coding scheme for +#' the [countrycode::countrycode()] #' to label "geo" variable with countrycode-package. It can be used to #' convert to short and long country names in many different languages. -#' If \code{NULL} (default) eurostat dictionary is used instead. -#' @param countrycode_nomatch What to do when using the countrycode to label a "geo" and -#' countrycode fails to find a match, for example other than country codes like EU28. -#' the original code is used with -#' a \code{NULL} (default), eurostat dictionary label is used with "eurostat", -#' and \code{NA} is used with NA. -#' @param custom_dic a named vector or named list of named vectors to give an own dictionary -#' for (part of) codes. Names of the vector should be codes and values labels. List -#' can be used to spesify dictonaries and then list names should be dictionary codes. +#' If `NULL` (default) eurostat dictionary is used instead. +#' @param countrycode_nomatch What to do when using the countrycode to label +#' a "geo" and countrycode fails to find a match, for example other than +#' country codes like EU28. The original code is used with +#' a `NULL` (default), eurostat dictionary label is used with "eurostat", +#' and `NA` is used with NA. +#' @param custom_dic a named vector or named list of named vectors to give an +#' own dictionary for (part of) codes. Names of the vector should be codes +#' and values labels. List can be used to specify dictionaries and then +#' list names should be dictionary codes. #' @param fix_duplicated A logical. If TRUE, the code is added to the -#' duplicated label values. If FALSE (default) error is given if -#' labelling produce duplicates. -#' @export -#' @author Janne Huovari \email{janne.huovari@@ptt.fi} +#' duplicated label values. If FALSE (default) error is given if +#' labeling produce duplicates. +#' @inheritParams get_eurostat +#' @author Janne Huovari +#' @family helpers +#' @seealso [countrycode::countrycode()] #' @return a vector or a data_frame. #' @examples -#' \dontrun{ -#' lp <- get_eurostat("nama_10_lp_ulc") -#' lpl <- label_eurostat(lp) -#' str(lpl) -#' lpl_order <- label_eurostat(lp, eu_order = TRUE) -#' lpl_code <- label_eurostat(lp, code = "unit") -#' label_eurostat_vars(names(lp)) -#' label_eurostat_tables("nama_10_lp_ulc") -#' label_eurostat(c("FI", "DE", "EU28"), dic = "geo") -#' label_eurostat(c("FI", "DE", "EU28"), dic = "geo", custom_dic = c(DE = "Germany")) -#' label_eurostat(c("FI", "DE", "EU28"), dic = "geo", countrycode = "country.name", -#' custom_dic = c(EU28 = "EU")) -#' label_eurostat(c("FI", "DE", "EU28"), dic = "geo", countrycode = "country.name") -#' # In Finnish -#' label_eurostat(c("FI", "DE", "EU28"), dic = "geo", countrycode = "cldr.short.fi") -#' } +#' \dontrun{ +#' lp <- get_eurostat("nama_10_lp_ulc") +#' lpl <- label_eurostat(lp) +#' str(lpl) +#' lpl_order <- label_eurostat(lp, eu_order = TRUE) +#' lpl_code <- label_eurostat(lp, code = "unit") +#' # Note that the dataset id must be provided in label_eurostat_vars +#' label_eurostat_vars(id = "nama_10_lp_ulc", x = "geo", lang = "en") +#' label_eurostat_tables("nama_10_lp_ulc") +#' label_eurostat(c("FI", "DE", "EU28"), dic = "geo") +#' label_eurostat( +#' c("FI", "DE", "EU28"), +#' dic = "geo", +#' custom_dic = c(DE = "Germany") +#' ) +#' label_eurostat( +#' c("FI", "DE", "EU28"), +#' dic = "geo", countrycode = "country.name", +#' custom_dic = c(EU28 = "EU") +#' ) +#' label_eurostat( +#' c("FI", "DE", "EU28"), +#' dic = "geo", +#' countrycode = "country.name" +#' ) +#' # In Finnish +#' label_eurostat( +#' c("FI", "DE", "EU28"), +#' dic = "geo", +#' countrycode = "cldr.short.fi" +#' ) +#' } +#' +#' @importFrom countrycode countrycode +#' @importFrom tibble as_tibble +#' @importFrom dplyr filter coalesce +#' @importFrom stringr str_glue +#' +#' @export label_eurostat <- function(x, dic = NULL, @@ -69,16 +93,26 @@ label_eurostat <- custom_dic = NULL, fix_duplicated = FALSE) { - # Avoid warnings - code_name <- NULL - - if (is.data.frame(x)) { - y <- x - - mynams <- names(y)[!(names(y) %in% c("time", "values", "flags"))] - - for (i in names(y)[!(names(y) %in% c("time", "values", "flags"))]) { - y[[i]] <- label_eurostat(y[[i]], i, + # Check if you have access to ec.europe.eu. + if (!check_access_to_data()) { + message(paste("You have no access to ec.europe.eu. Please check your", + "connection and/or review your proxy settings")) + } else { + + # Avoid warnings + code_name <- NULL + + if (is.data.frame(x)) { + y <- x + + mynams <- names(y)[!(names(y) %in% + c("TIME_PERIOD", "time", "values", "flags"))] + + for (i in names(y)[!(names(y) %in% + c("TIME_PERIOD", "time", "values", "flags"))]) { + y[[i]] <- label_eurostat( + y[[i]], + i, eu_order = eu_order, lang = lang, countrycode = countrycode, @@ -86,135 +120,284 @@ label_eurostat <- custom_dic = custom_dic, fix_duplicated = fix_duplicated ) - } - - # Fix the first two variables - #nams <- names(y) - #y <- cbind(as.vector(unlist(x[[1]])), - # as.vector(unlist(x[[2]])), - # x[, 3:ncol(x)]) - #colnames(y) <- nams - - # Codes added if asked - if (!is.null(code)) { - code_in <- code %in% names(y) - if (!all(code_in)) - stop("code column name(s) ", shQuote(code[!code_in]), " not found on x") - y_code <- x[, code, drop = FALSE] - names(y_code) <- paste0(names(y_code), "_code") - y <- cbind(y_code, y) - } - #return data.frame - return(as_data_frame(y)) - - } else { - if (is.null(dic)) - stop("Dictionary information is missing") - - dic_df <- get_eurostat_dic(dic, lang = lang) - - if (is.factor(x)) { - if (eu_order) { - ord <- dic_order(levels(x), dic_df, "code") - } else { - ord <- seq_along(levels(x)) } - - y <- factor(x, levels(x)[ord], - labels = label_eurostat(levels(x), dic = dic, - eu_order = eu_order, - lang = lang, - countrycode = countrycode, - countrycode_nomatch = countrycode_nomatch, - custom_dic = custom_dic, - fix_duplicated = fix_duplicated)[ord] - ) - - #return factor - return(y) - - } else if (dic == "geo" & !is.null(countrycode)){ - if (is.null(countrycode_nomatch)){ - y <- countrycode::countrycode(x, origin = "eurostat", destination = countrycode, nomatch = NULL) - } else if (is.na(countrycode_nomatch)) { - y <- countrycode::countrycode(x, origin = "eurostat", destination = countrycode, nomatch = NA) - } else if (countrycode_nomatch == "eurostat") { - y <- countrycode::countrycode(x, origin = "eurostat", destination = countrycode, nomatch = NA) - y[is.na(y)] <- label_eurostat(x[is.na(y)], dic = "geo", lang = lang, fix_duplicated = fix_duplicated) - } else { - stop("unknown argument ", countrycode_nomatch, " for countrycode_nomatch") + + # Fix the first two variables + # nams <- names(y) + # y <- cbind(as.vector(unlist(x[[1]])), + # as.vector(unlist(x[[2]])), + # x[, 3:ncol(x)]) + # colnames(y) <- nams + + # Codes added if asked + if (!is.null(code)) { + code_in <- code %in% names(y) + if (!all(code_in)) { + stop( + stringr::str_glue( + "The following code column name(s) not found on x:\n", + "{code[!code_in]}") + ) + } + y_code <- x[, code, drop = FALSE] + names(y_code) <- paste0(names(y_code), "_code") + y <- cbind(y_code, y) } + + return(tibble::as_tibble(y)) } else { - # dics are in upper case, change if x is not - test_n <- min(length(x), 5) - if (!all(toupper(x[1:test_n]) == x[1:test_n])) - x <- toupper(x) - # test duplicates - dic_sel <- dplyr::filter(dic_df, code_name %in% x) - - if (anyDuplicated(dic_sel$full_name)) { - if (fix_duplicated) { - dup_labels <- - dic_sel$full_name %in% dic_sel$full_name[duplicated(dic_sel$full_name)] - modif_dup_labels <- paste(dic_sel$code_name[dup_labels], - dic_sel$full_name[dup_labels], - sep = " ") - dic_sel$full_name[dup_labels] <- modif_dup_labels - message( - "Labels for ", - dic, - " includes duplicated labels in the Eurostat dictionary. ", - "Codes have been added as a prefix for dublicated.\n", - "Modified labels are: ", - paste0(modif_dup_labels, collapse = ", ") - ) + if (is.null(dic)) { + stop("Dictionary information is missing") + } + + dic_df <- get_eurostat_dic(dic, lang = lang) + + if (is.factor(x)) { + if (eu_order) { + ord <- dic_order(levels(x), dic_df, "code") + } else { + ord <- seq_along(levels(x)) + } + + y <- factor( + x, + levels(x)[ord], + labels = + label_eurostat( + levels(x), + dic = dic, + eu_order = eu_order, + lang = lang, + countrycode = countrycode, + countrycode_nomatch = countrycode_nomatch, + custom_dic = custom_dic, + fix_duplicated = fix_duplicated + )[ord] + ) + + # return factor + return(y) + } else if (dic == "geo" && !is.null(countrycode)) { + if (is.null(countrycode_nomatch)) { + y <- countrycode::countrycode(x, + origin = "eurostat", + destination = countrycode, + nomatch = NULL) + } else if (is.na(countrycode_nomatch)) { + y <- countrycode::countrycode(x, + origin = "eurostat", + destination = countrycode, + nomatch = NA) + } else if (countrycode_nomatch == "eurostat") { + y <- countrycode::countrycode(x, + origin = "eurostat", + destination = countrycode, + nomatch = NA) + y[is.na(y)] <- label_eurostat(x[is.na(y)], + dic = "geo", + lang = lang, + fix_duplicated = fix_duplicated) } else { stop( - "Labels for ", - dic, - " includes duplicated labels in the Eurostat dictionary. ", - "Use fix_duplicated = TRUE with label_eurostat() to fix duplicated - labels automatically." - ) + stringr::str_glue( + "unknown argument {countrycode_nomatch} for countrycode_nomatch" + ) + ) } + } else { + # dics are in upper case, change if x is not + test_n <- min(length(x), 5) + if (!all(toupper(x[1:test_n]) == x[1:test_n])) { + x <- toupper(x) + } + # test duplicates + dic_sel <- dplyr::filter(dic_df, code_name %in% x) + + if (anyDuplicated(dic_sel$full_name)) { + if (fix_duplicated) { + dup_labels <- dic_sel$full_name %in% + dic_sel$full_name[duplicated(dic_sel$full_name)] + modif_dup_labels <- paste( + dic_sel$code_name[dup_labels], + dic_sel$full_name[dup_labels], + sep = " " + ) + dic_sel$full_name[dup_labels] <- modif_dup_labels + message( + "Labels for ", + dic, + " includes duplicated labels in the Eurostat dictionary. ", + "Codes have been added as a prefix for duplicated.\n", + "Modified labels are: ", + paste0(modif_dup_labels, collapse = ", ") + ) + } else { + stop( + "Labels for ", + dic, + " includes duplicated labels in the Eurostat dictionary. ", + "Use fix_duplicated = TRUE with label_eurostat() to fix ", + "duplicated labels automatically." + ) + } + } + # mapvalues + y <- dic_sel[[2]][match(x, dic_sel[[1]])] + } + + # apply custom_dic + if (!is.null(custom_dic)) { + if (is.list(custom_dic)) custom_dic <- custom_dic[[dic]] + y <- dplyr::coalesce(unname(custom_dic[x]), y) + } + + if (any(is.na(y))) { + warning("All labels for ", dic, " were not found.") } - # mapvalues - y <- dic_sel[[2]][match(x, dic_sel[[1]])] - - } - - # apply custom_dic - if (!is.null(custom_dic)){ - if (is.list(custom_dic)) custom_dic <- custom_dic[[dic]] - y <- dplyr::coalesce(unname(custom_dic[x]), y) } - - if (any(is.na(y))) - warning("All labels for ", dic, " were not found.") - - } - - - y - + y + } } -#' @describeIn label_eurostat Get definitions for variable (column) names. For -#' objects other than characters or factors definitions are get for names. +## OLD CODE +# @rdname eurostat-deprecated +# @inheritParams label_eurostat +# @export +# label_eurostat_vars <- function(x, lang = "en") { +# .Deprecated("label_eurostat_vars2") +# dictname <- "dimlst" +# +# if (!(is.character(x) || is.factor(x))) { +# x <- names(x) +# } +# x <- x[!grepl("values", x)] # remove values column +# +# url <- getOption("eurostat_url") +# tname <- paste0( +# url, +# "estat-navtree-portlet-prod/BulkDownloadListing?file=dic%2F", lang, "%2F", +# dictname, ".dic" +# ) +# dict <- readr::read_tsv(url(tname), +# col_names = c("code_name", "full_name"), +# col_types = readr::cols(.default = readr::col_character()) +# ) +# dict$full_name <- gsub( +# pattern = "\u0092", replacement = "'", +# dict$full_name +# ) +# +# +# y <- dict$full_name[which(dict$code_name %in% toupper(x))] +# +# if (length(y) < length(x)) { +# warning(paste( +# "The query did not return names for the following variables:\n", +# x[which(!(toupper(x) %in% dict$code_name))] +# ) +# ) +# } +# +# y +# } + + +#' @describeIn label_eurostat Get definitions for variable (column) names. +#' @inheritParams get_eurostat +#' @importFrom httr2 url_build url_parse +#' #' @export -label_eurostat_vars <- function(x, lang = "en") { - if (!(is.character(x) | is.factor(x))) - x <- names(x) - x <- x[!grepl("values", x)] # remove values column - label_eurostat(x, dic = "dimlst", lang = lang) +label_eurostat_vars <- function(x = NULL, id, lang = "en") { + + eurostat_base_url <- getOption("eurostat_url") + api_base_uri <- paste0(eurostat_base_url, "api/dissemination/sdmx/2.1") + resource <- "conceptscheme" + agencyID <- "ESTAT" + resourceID <- id + parameters <- list( + compressed = "false" + ) + + lang <- check_lang(lang) + + req_url <- paste(api_base_uri, resource, agencyID, resourceID, sep = "/") + req_url <- httr2::url_parse(req_url) + req_url$query <- parameters + req_url <- httr2::url_build(req_url) + + columns <- c("code_name", "full_name") + dict = data.frame(matrix(nrow = 0, ncol = length(columns))) + + xml_obj <- xml2::read_xml(req_url) + concepts <- xml_obj %>% + xml2::xml_find_all(xpath = stringr::str_glue(".//s:Concept")) %>% + xml2::xml_attr(attr = "id") + + for (i in seq_len(length(concepts))) { + concept <- concepts[i] + var_name <- xml2::xml_find_all( + xml_obj, + xpath = stringr::str_glue(".//s:Concept[@id='{concept}']/c:Name[@xml:lang='{lang}']") + ) %>% + xml2::xml_text() + + dict <- rbind(dict, c(concept, var_name), make.row.names = FALSE) + } + + colnames(dict) <- columns + + if (nrow(dict) == 0) { + warning("The query did not return any names for dataset columns") + return(invisible()) + } else if (nrow(dict) < length(concepts)) { + warning("The query did not return names for all concepts") + } + + if (!is.null(x)) { + filtered_dict <- dict$full_name[which(dict$code_name %in% x)] + return(filtered_dict) + } + + unfiltered_dict <- dict$full_name + unfiltered_dict } #' @describeIn label_eurostat Get definitions for table names +#' @importFrom xml2 xml_find_all xml_text read_xml +#' @importFrom httr2 url_parse url_build +#' @importFrom dplyr %>% +#' @importFrom stringr str_glue #' @export label_eurostat_tables <- function(x, lang = "en") { - if (!(is.character(x) | - is.factor(x))) + if (!(is.character(x) || is.factor(x))) { stop("x have to be a character or a factor") - label_eurostat(x, dic = "table_dic", lang = lang) + } + eurostat_base_url <- getOption("eurostat_url") + api_base_uri <- paste0(eurostat_base_url, "api/dissemination/sdmx/2.1") + resource <- "dataflow" + agencyID <- "ESTAT" + resourceID <- x + parameters <- list( + compressed = "false" + ) + + lang <- check_lang(lang) + + req_url <- paste(api_base_uri, resource, agencyID, resourceID, sep = "/") + req_url <- httr2::url_parse(req_url) + req_url$query <- parameters + req_url <- httr2::url_build(req_url) + + xml_obj <- xml2::read_xml(req_url) + table_name <- xml_obj %>% + xml2::xml_find_all(xpath = stringr::str_glue(".//c:Name[@xml:lang='{lang}']")) %>% + xml2::xml_text() + + if (length(table_name) == 0) { + warning("The query did not return a name for the dataset") + return(invisible()) + } + + table_name } diff --git a/R/regional_coding_functions.R b/R/regional_coding_functions.R new file mode 100755 index 00000000..bd7316a2 --- /dev/null +++ b/R/regional_coding_functions.R @@ -0,0 +1,109 @@ +#' @title Recode Region Codes From Source To Target NUTS Typology +#' @param dat A data frame with a 3-5 character `geo_var` variable +#' to be validated. +#' @param geo_var Defaults to `"geo"`. The variable that contains +#' the 3-5 character geo codes to be validated. +#' @param nuts_year The year of the NUTS typology to use. +#' You can select any valid +#' NUTS definition, i.e. `1999`, `2003`, `2006`, +#' `2010`, `2013`, the currently used `2016` and the +#' already announced and defined `2021`. Defaults to the current +#' typology in force, which is `2016`. +#' @importFrom regions recode_nuts +#' @return The original data frame with a `'geo_var'` column is extended +#' with a `'typology'` column that states in which typology is the `'geo_var'` +#' a valid code. For invalid codes, looks up potential reasons of invalidity +#' and adds them to the `'typology_change'` column, and at last it +#' adds a column of character vector containing the desired codes in the +#' target typology, for example, in the NUTS2013 typology. +#' @family regions functions +#' @export +#' @examples{ +#' foo <- data.frame ( +#' geo = c("FR", "DEE32", "UKI3" , +#' "HU12", "DED", +#' "FRK"), +#' values = runif(6, 0, 100 ), +#' stringsAsFactors = FALSE ) +#' +#' recode_nuts(foo, nuts_year = 2013) +#' } +regions::recode_nuts + +#' @title Validate Conformity With NUTS Geo Codes +#' @details While country codes are technically not part of the NUTS typologies, +#' Eurostat de facto uses a `NUTS0` typology to identify countries. +#' This de facto typology has three exception which are handled by the +#' [validate_nuts_countries][regions::validate_nuts_countries] function. +#' +#' NUTS typologies have different versions, therefore the conformity +#' is validated with one specific versions, which can be any of these: +#' `1999`, `2003`, `2006`, `2010`, +#' `2013`, the currently used `2016` and the already +#' announced and defined `2021`. +#' +#' The NUTS typology was codified with the `NUTS2003`, and the +#' pre-1999 NUTS typologies may confuse programmatic data processing, +#' given that some NUTS1 regions were identified with country codes +#' in smaller countries that had no `NUTS1` divisions. +#' +#' Currently the `2016` is used by Eurostat, but many datasets +#' still contain `2013` and sometimes earlier metadata. +#' +#' @param dat A data frame with a 3-5 character `geo_var` +#' variable to be validated. +#' @param geo_var Defaults to `"geo"`. The variable that contains +#' the 3-5 character geo codes to be validated. +#' @param nuts_year The year of the NUTS typology to use. +#' Defaults to `2016`. You can select any valid +#' NUTS definition, i.e. `1999`, `2003`, `2006`, +#' `2010`, `2013`, the currently used `2016` and the +#' already announced and defined `2021`. +#' @importFrom regions validate_nuts_regions +#' @return Returns the original `dat` data frame with a column +#' that specifies the comformity with the NUTS definition of the year +#' `nuts_year`. +#' @family regions functions +#' @export +#' @examples +#' \donttest{ +#' my_reg_data <- data.frame( +#' geo = c( +#' "BE1", "HU102", "FR1", +#' "DED", "FR7", "TR", "DED2", +#' "EL", "XK", "GB" +#' ), +#' values = runif(10) +#' ) +#' +#' validate_nuts_regions(my_reg_data) +#' +#' validate_nuts_regions(my_reg_data, nuts_year = 2013) +#' +#' validate_nuts_regions(my_reg_data, nuts_year = 2003) +#' } +regions::validate_nuts_regions + + +#' @title Validate Conformity with NUTS Geo Codes (vector) +#' @param geo A vector of geographical code to validate. +#' @param nuts_year A valid NUTS edition year. +#' @importFrom regions validate_geo_code +#' @family regions functions +#' @return A character list with the valid typology, or 'invalid' in the cases +#' when the geo coding is not valid. +#' @export +#' @examples +#' \donttest{ +#' my_reg_data <- data.frame( +#' geo = c( +#' "BE1", "HU102", "FR1", +#' "DED", "FR7", "TR", "DED2", +#' "EL", "XK", "GB" +#' ), +#' values = runif(10) +#' ) +#' +#' validate_geo_code(my_reg_data$geo) +#' } +regions::validate_geo_code diff --git a/R/search_eurostat.R b/R/search_eurostat.R index 6b34b918..ae439dbc 100755 --- a/R/search_eurostat.R +++ b/R/search_eurostat.R @@ -1,46 +1,85 @@ #' @title Grep Datasets Titles from Eurostat -#' @description Lists names of dataset from eurostat with the particular -#' pattern in the description. +#' @description Lists datasets from eurostat table of contents with the +#' particular pattern in item titles. #' @details Downloads list of all datasets available on #' eurostat and return list of names of datasets that contains particular #' pattern in the dataset description. E.g. all datasets related to -#' education of teaching. -#' @param pattern Character, datasets, folder or tables with this pattern in -#' the description will be returned (depending on the 'type' argument) -#' @param type Grep the Eurostat table of contents either for -#' 'dataset' (default), 'folder', 'table' or "all" (for all types). -#' @param fixed logical. If TRUE, pattern is a string to be matched as is. -#' Change to FALSE if more complex regex matching is needed. -#' @return A tibble with eight columns -#' \itemize{ -#' \item{title}{The name of dataset of theme} -#' \item{code}{The codename of dataset of theme, will be used by the get_eurostat and get_eurostat_raw functions.} -#' \item{type}{Is it a dataset, folder or table.} -#' \item{last.update.of.data, last.table.structure.change, data.start, data.end}{Dates.} -#' } -#' @export -#' @seealso \code{\link{get_eurostat}}, \code{\link{get_eurostat_toc}} -#' @references See citation("eurostat") -#' @author Przemyslaw Biecek and Leo Lahti \email{ropengov-forum@@googlegroups.com} -#' @examples \dontrun{ -#' tmp <- search_eurostat("education") -#' head(tmp) -#' # Use "fixed = TRUE" when pattern has characters that would need escaping. -#' # Here, parentheses would normally need to be escaped in regex -#' tmp <- search_eurostat("Live births (total) by NUTS 3 region", fixed = TRUE) +#' education of teaching. +#' +#' If you wish to perform searches on other fields than item title, +#' you can download the Eurostat Table of Contents manually using +#' `get_eurostat_toc()` function and use `grep()` function normally. The data +#' browser on Eurostat website may also return useful results. +#' @param pattern +#' Text string that is used to search from dataset, folder or table titles, +#' depending on the type argument. +#' @param type +#' Selection for types of datasets to be searched. Default is `dataset`, other +#' possible options are `table`, `folder` and `all` for all types. +#' @param column +#' Selection for the column of TOC where search is done. Default is `title`, +#' other possible option is `code`. +#' @param fixed +#' logical. If TRUE (default), pattern is a string to be matched as is. +#' See `grep()` documentation for more information. +#' @inheritParams get_eurostat +#' @inherit get_eurostat_toc return seealso +#' @inherit eurostat-package references +#' @inheritSection eurostat-package Data source: Eurostat Table of Contents +#' +#' @author Przemyslaw Biecek and Leo Lahti +#' +#' @examplesIf check_access_to_data() +#' \donttest{ +#' tmp <- search_eurostat("education") +#' head(tmp) +#' # Use "fixed = TRUE" when pattern has characters that would need escaping. +#' # Here, parentheses would normally need to be escaped in regex +#' tmp <- search_eurostat("Live births (total) by NUTS 3 region", fixed = TRUE) #' } +#' #' @keywords utilities database -search_eurostat <- function(pattern, type = "dataset", fixed = TRUE) { - set_eurostat_toc() - tmp <- get(".eurostatTOC", envir = .EurostatEnv) - if (type != "all") tmp <- tmp[ tmp$type %in% type, ] - tmp[ grep(tmp$title, pattern = pattern, fixed = fixed), ] -} +#' +#' @export +search_eurostat <- function(pattern, + type = "dataset", + column = "title", + fixed = TRUE, + lang = "en") { + # Sanity check + type <- tolower(as.character(type)) + column <- tolower(as.character(column)) + lang <- check_lang(lang) -#' @describeIn search_eurostat Old deprecated version -#' @export -grepEurostatTOC <- function(pattern, type = "dataset"){ - .Deprecated("search_eurostat") - search_eurostat(pattern = pattern, type = type) + if (!type %in% c("dataset", "table", "folder", "all")) { + warning(stringr::str_glue("The type \"{type}\" is not recognized, ", + "will return the search results using the ", + "default argument: type = \"dataset\".")) + type <- "dataset" + } + + if (!column %in% c("title", "code")) { + warning(stringr::str_glue("The column \"{column}\" is not recognized, ", + "will return the search results using the ", + "default argument: column = \"title\".")) + column <- "title" + } + + # Check if you have access to ec.europe.eu. + if (!check_access_to_data()) { + message("You have no access to ec.europe.eu. + Please check your connection and/or review your proxy settings") + return(invisible()) + } + + #set_eurostat_toc() + #tmp <- get(".eurostatTOC", envir = .EurostatEnv) + tmp <- get_eurostat_toc(lang = lang) + + + if (!identical(type, "all")) { + tmp <- tmp[tmp$type %in% type, ] + } + tmp <- tmp[grep(tmp[[column]], pattern = pattern, fixed = fixed), ] } diff --git a/R/set_eurostat_cache.R b/R/set_eurostat_cache.R new file mode 100644 index 00000000..eaf66c6e --- /dev/null +++ b/R/set_eurostat_cache.R @@ -0,0 +1,244 @@ +#' Set Eurostat Cache +#' +#' @description +#' This function will store your `cache_dir` path on your local machine +#' and would load it for future sessions. Type +#' `Sys.getenv("EUROSTAT_CACHE_DIR")` to +#' find your cached path. +#' +#' Alternatively, you can store the `cache_dir` manually with the following +#' options: +#' * Run `Sys.setenv(EUROSTAT_CACHE_DIR = "cache_dir")`. You +#' would need to run this command on each session +#' (Similar to `install = FALSE`). +#' * Set `options(eurostat_cache_dir = "cache_dir")`. Similar to +#' the previous option. This is provided for backwards compatibility +#' purposes. +#' * Write this line on your .Renviron file: +#' `EUROSTAT_CACHE_DIR = "value_for_cache_dir"` (same behavior than +#' `install = TRUE`). This would store your `cache_dir` +#' permanently. +#' +#' @param cache_dir A path to a cache directory. On missing value the function +#' would store the cached files on a temporary dir (See +#' [base::tempdir()]). +#' @param overwrite If this is set to `TRUE`, it will overwrite an existing +#' `EUROSTAT_CACHE_DIR` that you already have in local machine. +#' @param verbose Logical, displays information. Useful for debugging, +#' default is `FALSE`. +#' @param install if `TRUE`, will install the key in your local machine for +#' use in future sessions. Defaults to `FALSE`. If `cache_dir` is +#' `FALSE` this parameter is set to `FALSE` automatically. +#' +#' @family cache utilities +#' @seealso [rappdirs::user_config_dir()] +#' +#' @return An (invisible) character with the path to your `cache_dir`. +#' +#' @author Diego Hernangómez +#' @examples +#' +#' # Don't run this! It would modify your current state +#' \dontrun{ +#' set_eurostat_cache_dir(verbose = TRUE) +#' } +#' +#' Sys.getenv("EUROSTAT_CACHE_DIR") +#' @export +set_eurostat_cache_dir <- function(cache_dir, + overwrite = FALSE, + install = FALSE, + verbose = TRUE) { + + # nocov start + # Default if not provided + if (missing(cache_dir) || cache_dir == "") { + if (verbose) { + message( + "Using a temporary cache dir. ", + "Set 'cache_dir' to a value for store permanently" + ) + } + # Create a folder on tempdir + cache_dir <- file.path(tempdir(), "eurostat") + is_temp <- TRUE + install <- FALSE + } else { + is_temp <- FALSE + } + # nocov end + + cache_dir <- path.expand(cache_dir) + + + # Validate + stopifnot( + is.character(cache_dir), + is.logical(overwrite), + is.logical(install) + ) + + + # Create cache dir if it doesn't exists + if (!dir.exists(cache_dir)) { + dir.create(cache_dir, recursive = TRUE) + } + + if (verbose) { + message( + "eurostat cache dir is: ", + cache_dir + ) + } + + + # Install path on environ var. + + # nocov start + if (install) { + config_dir <- rappdirs::user_config_dir("eurostat", "R") + # Create cache dir if not presente + if (!dir.exists(config_dir)) { + dir.create(config_dir, recursive = TRUE) + } + + eurostat_file <- file.path(config_dir, "eurostat_cache_dir") + + if (!file.exists(eurostat_file) || overwrite == TRUE) { + # Create file if it doesn't exist + writeLines(cache_dir, con = eurostat_file) + } else { + stop( + "A cache_dir path already exists.\nYou can overwrite it with the ", + "argument overwrite = TRUE", + call. = FALSE + ) + } + # nocov end + } else { + if (verbose && !is_temp) { + message( + "To install your cache_dir path for use in future sessions,", + "\nrun this function with `install = TRUE`." + ) + } + } + + Sys.setenv(EUROSTAT_CACHE_DIR = cache_dir) + return(invisible(cache_dir)) +} + + +#' Detect cache dir for eurostat +#' +#' @noRd +eur_helper_detect_cache_dir <- function() { + + # Try from getenv + getvar <- Sys.getenv("EUROSTAT_CACHE_DIR") + + + # 1. Get from option - This is from backwards compatibility only + # nocov start + from_option <- getOption("eurostat_cache_dir", NULL) + + if (!is.null(from_option) && (is.null(getvar) || getvar == "")) { + cache_dir <- set_eurostat_cache_dir(from_option, install = FALSE) + return(cache_dir) + } + + + + + if (is.null(getvar) || is.na(getvar) || getvar == "") { + # Not set - tries to retrieve from cache + cache_config <- file.path( + rappdirs::user_config_dir("eurostat", "R"), + "EUROSTAT_CACHE_DIR" + ) + + if (file.exists(cache_config)) { + cached_path <- readLines(cache_config) + + # Case on empty cached path - would default + if (is.null(cached_path) || is.na(cached_path) || cached_path == "") { + cache_dir <- set_eurostat_cache_dir(overwrite = TRUE, verbose = FALSE) + return(cache_dir) + } + + # 3. Return from cached path + Sys.setenv(EUROSTAT_CACHE_DIR = cached_path) + return(cached_path) + } else { + # 4. Default cache location + + cache_dir <- set_eurostat_cache_dir( + overwrite = TRUE, + verbose = FALSE + ) + return(cache_dir) + } + } else { + return(getvar) + } + # nocov end +} + +#' Creates `cache_dir` +#' +#' +#' @noRd +eur_helper_cachedir <- function(cache_dir = NULL) { + # Check cache dir from options if not set + if (is.null(cache_dir)) { + cache_dir <- eur_helper_detect_cache_dir() + } + + # Reevaluate + if (is.null(cache_dir)) { + cache_dir <- file.path(tempdir(), "eurostat") + } + + # Create cache dir if needed + if (isFALSE(dir.exists(cache_dir))) { + dir.create(cache_dir, recursive = TRUE) + } + return(cache_dir) +} + +#' Output cache information as data.frame +#' @description +#' Parses cache_list.json file and returns a data.frame +#' @return +#' A data.frame object with 3 columns: dataset code, download date and +#' query md5 hash +#' +#' @inheritParams get_eurostat +#' @importFrom jsonlite fromJSON +#' @export +list_eurostat_cache_items <- function(cache_dir = NULL) { + if (is.null(cache_dir)) { + cache_dir <- eur_helper_detect_cache_dir() + } + + path <- paste0(cache_dir, "/cache_list.json") + + if (!file.exists(path)) { + warning("No cache_list.json file found!") + return(invisible()) + } + + json_file <- jsonlite::fromJSON(path) + # return(json_file) + columns <- c("code", "download_date", "query_md5_hash") + df <- data.frame(matrix(nrow = 0, ncol = length(columns))) + for (i in seq_len(length(json_file))) { + df_i <- data.frame( + code = json_file[[i]][["id"]], + download_date = json_file[[i]][["download_date"]], + query_md5_hash = names(json_file[i]) + ) + df <- rbind(df, df_i) + } + df +} \ No newline at end of file diff --git a/R/set_eurostat_toc.R b/R/set_eurostat_toc.R index fa9c7908..bf1b58b2 100755 --- a/R/set_eurostat_toc.R +++ b/R/set_eurostat_toc.R @@ -1,20 +1,213 @@ #' @title Set Eurostat TOC #' @description Internal function. -#' @param ... Arguments to be passed +#' @inheritParams get_eurostat #' @return Empty element #' @references see citation("eurostat") -#' @author Przemyslaw Biecek and Leo Lahti \email{ropengov-forum@@googlegroups.com} +#' @author Przemyslaw Biecek and Leo Lahti +#' +#' @importFrom readr read_tsv cols col_character +#' @importFrom stringr str_glue +#' +#' @seealso [get_eurostat_toc()] [toc_count_children()] [toc_determine_hierarchy()] +#' [toc_list_children()] [toc_count_whitespace()] +#' #' @keywords internal -set_eurostat_toc <- function(...) { - if (!exists(".eurostatTOC", envir = .EurostatEnv)) { - base <- getOption("eurostat_url") - url <- paste(base, "estat-navtree-portlet-prod/", - "BulkDownloadListing?sort=1&downfile=table_of_contents_en.txt", - sep = "") - .eurostatTOC <- readr::read_tsv(url(url), - col_types = readr::cols(.default = readr::col_character())) - - assign(".eurostatTOC", .eurostatTOC, envir = .EurostatEnv) +set_eurostat_toc <- function(lang = "en") { + lang <- check_lang(lang) + + language_version <- switch(lang, + en = ".eurostatTOC", + fr = ".eurostatTOC_fr", + de = ".eurostatTOC_de") + + if (!exists(language_version, envir = .EurostatEnv)) { + base <- getOption("eurostat_url") + url <- stringr::str_glue( + paste0(base, "api/dissemination/catalogue/toc/txt?lang={lang}") + ) + + .eurostatTOC <- readr::read_tsv( + file = url(url), + col_types = readr::cols(.default = readr::col_character()), + name_repair = make.names, + trim_ws = FALSE + ) + + .eurostatTOC$hierarchy <- toc_determine_hierarchy(.eurostatTOC$title) + .eurostatTOC$title <- trimws(.eurostatTOC$title, which = "left") + .eurostatTOC$values <- as.numeric(.eurostatTOC$values) + + assign(language_version, .eurostatTOC, envir = .EurostatEnv) } invisible(0) } + +#' @title Count white space at the start of the title +#' @description Counts the number of white space characters at the start +#' of the string. +#' @inherit toc_determine_hierarchy details +#' @inheritParams toc_determine_hierarchy +#' @return Numeric (number of white space characters) +#' @importFrom stringr str_extract +#' @examples +#' strings <- c(" abc", " cdf", "no_spaces") +#' for (string in strings) { +#' whitespace_count <- eurostat:::toc_count_whitespace(string) +#' cat("String:", string, "\tWhitespace Count:", whitespace_count, "\n") +#' } +#' +#' @inherit set_eurostat_toc seealso +#' +#' @author Pyry Kantanen +#' +#' @keywords internal +toc_count_whitespace <- function(input_string) { + # Counts white space chars \s before encountering the 1st non-ws char \S + pattern <- "^\\s*(?=\\S)" + extracted_ws <- stringr::str_extract(input_string, pattern) + + nchar(extracted_ws) +} + +#' @title Determine level in hierarchy +#' @description Divides the number of spaces before alphanumeric characters +#' with 4 and uses the result to determine hierarchy. Top level is 0. +#' @details Used in toc_determine_hierarchy function to determine hierarchy. +#' Hierarchy is defined in Eurostat .txt format TOC files by the number of +#' white space characters at intervals of four. For example, +#' " Foo" (4 white space characters) is one level higher than +#' " Bar" (8 white space characters). +#' "Database by themes" (0 white space characters before the first +#' alphanumeric character) is highest in the hierarchy. +#' +#' The function will return a warning if the input has white space in anything +#' else than as increments of 4. 0, 4, 8... are acceptable but 3, 6, 10... +#' are not. +#' @param input_string +#' A string containing Eurostat TOC titles +#' @return Numeric +#' @examples +#' strings <- c(" abc", " cdf", "no_spaces") +#' eurostat:::toc_determine_hierarchy(strings) +#' +#' @inherit set_eurostat_toc seealso +#' +#' @author Pyry Kantanen +#' +#' @keywords internal +toc_determine_hierarchy <- function(input_string) { + number_of_whitespace <- toc_count_whitespace(input_string) + + # If all x mod y calculations equal 0 everything is ok. + # If not, input is somehow mangled + # For example " General and regional statistics" (4 whitespace) returns 1 + # whereas " " (12 whitespace without any letters) returns also 1 + # Normally all dataset items are expected to have a title to determine + # their place in hierarchy. Testing for this might be a bit tricky. + if (!all((number_of_whitespace %% 4) %in% c(0))) { + warning( + paste( + "TOC indentation was not uniform in all rows or there were some", + "items that were missing a proper title. Hierarchy value set to NA", + "for problematic rows." + ) + ) + invalid_rows <- which(!(number_of_whitespace %% 4) %in% c(0)) + # return(invisible()) + hierarchy <- number_of_whitespace %/% 4 + hierarchy[invalid_rows] <- NA + return(hierarchy) + } + + # If white space is 0, it gets number 0 in hierarchy + hierarchy <- number_of_whitespace %/% 4 + hierarchy + # Or should it be 1? + # (number_of_whitespace %/% 4) + 1 +} + +#' @title Count number of children +#' @description +#' Determine how many children a certain TOC item (usually a folder) has. +#' @param code Eurostat TOC item code (folder, dataset, table) +#' +#' @importFrom stringr str_glue +#' +#' @inherit set_eurostat_toc seealso +#' +#' @author Pyry Kantanen +#' +#' @keywords internal +#' +toc_count_children <- function(code) { + toc <- get_eurostat_toc() + + # Line number for hit + initial_pos <- which(toc$code == code) + + if (length(initial_pos) > 1) { + warning(stringr::str_glue( + "Unambiguous code input: \"{code}\". ", + "TOC contains multiple items with the same code.")) + return(invisible()) + } + + initial_hierarchy <- toc$hierarchy[initial_pos] + i <- initial_pos + 1 + num_children <- 0 + + while (toc$hierarchy[i] > initial_hierarchy) { + # Check for the 1st iteration: If the next item is of the same hierarchy + # break the while loop and determine that the number of children is 0 + if (toc$hierarchy[i] == initial_hierarchy && num_children == 0) { + num_children <- 0 + break + } + num_children <- i - initial_pos + i <- i + 1 + } + num_children +} + +#' @title List children +#' @description +#' List children of a specific folder. +#' @inheritParams toc_count_children +#' +#' @importFrom stringr str_glue +#' +#' @inherit set_eurostat_toc seealso +#' +#' @author Pyry Kantanen +#' +#' @keywords internal +#' +toc_list_children <- function(code) { + toc <- get_eurostat_toc() + # Line number for hit + initial_pos <- which(toc$code == code) + + if (length(initial_pos) > 1) { + warning(stringr::str_glue( + "Unambiguous code input: \"{code}\". ", + "TOC contains multiple items with the same code.")) + return(invisible()) + } + + final_pos <- initial_pos + toc_count_children(code) + + if (final_pos == initial_pos) { + initial_pos_type <- toc$type[initial_pos] + message(stringr::str_glue( + "The TOC item with code \"{code}\" (type: {initial_pos_type}) ", + "does not have any children. Please use another code.") + ) + return(invisible()) + } + + # Add 1 to omit root folder + initial_pos <- initial_pos + 1 + + children <- toc[initial_pos:final_pos, ] + children +} diff --git a/R/sysdata.rda b/R/sysdata.rda new file mode 100644 index 00000000..689c70af Binary files /dev/null and b/R/sysdata.rda differ diff --git a/R/tidy_eurostat.R b/R/tidy_eurostat.R index 815d8d74..ee3e9aa9 100755 --- a/R/tidy_eurostat.R +++ b/R/tidy_eurostat.R @@ -1,139 +1,251 @@ #' @title Transform Data into Row-Column-Value Format -#' @description Transform raw Eurostat data table into the row-column-value -#' format (RCV). -#' @param dat a data_frame from \code{\link{get_eurostat_raw}}. -#' @param time_format a string giving a type of the conversion of the -#' time column from the eurostat format. -#' A "date" (default) convers to a \code{\link{Date}} -#' with a first date of the period. A "date_last" -#' convers to a \code{\link{Date}} with -#' a last date of the period. A "num" convers to a numeric and "raw" -#' does not do conversion. See \code{\link{eurotime2date}} and -#' \code{\link{eurotime2num}}. -#' @param select_time a character symbol for a time frequence or NULL -#' (default). -#' @param stringsAsFactors if \code{TRUE} (the default) variables are -#' converted to factors in original Eurostat order. If \code{FALSE} -#' they are returned as strings. -#' @param keepFlags a logical whether the flags (e.g. "confidential", -#' "provisional") should be kept in a separate column or if they -#' can be removed. Default is \code{FALSE} -#' @return tibble in the molten format with the last column 'values'. -#' @seealso \code{\link{get_eurostat}} -#' @references See citation("eurostat"). -#' @author Przemyslaw Biecek, Leo Lahti and Janne Huovari \email{ropengov-forum@@googlegroups.com} \url{http://github.com/ropengov/eurostat} +#' @description Transform raw Eurostat data table downloaded from the +#' API into a tidy row-column-value format (RCV). +#' @param dat +#' a data_frame from [get_eurostat_raw()]. +#' @inheritParams get_eurostat +#' @return tibble in the melted format with the last column 'values'. +#' @seealso [get_eurostat()], [convert_time_col()], [eurotime2date()] +#' @inherit eurostat-package references +#' @author Przemyslaw Biecek, Leo Lahti, Janne Huovari and Pyry Kantanen +#' +#' @importFrom stringi stri_extract_first_regex stri_replace_all_regex +#' @importFrom stringi stri_replace_all_fixed +#' @importFrom tidyr separate pivot_longer +#' @importFrom dplyr filter +#' @importFrom data.table setDT melt .SD := +#' @importFrom stats na.omit +#' +#' @examples +#' \dontrun{ +#' # Example of a dataset with multiple time series +#' get_eurostat("AVIA_GOR_ME", +#' time_format = "date_last", +#' cache = F +#' ) +#' } +#' #' @keywords internal utilities database -tidy_eurostat <- function(dat, time_format = "date", select_time = NULL, - stringsAsFactors = default.stringsAsFactors(), - keepFlags = FALSE) { +tidy_eurostat <- function(dat, + time_format = "date", + select_time = NULL, + stringsAsFactors = FALSE, + keepFlags = FALSE, + use.data.table = FALSE) { - # To avoid warnings - time <- values <- NULL + # To avoid warnings + TIME_PERIOD <- values <- NULL - # Separate codes to columns - cnames <- strsplit(colnames(dat)[1], split = "[\\,]")[[1]] - cnames1 <- cnames[-length(cnames)] # for columns - cnames2 <- cnames[length(cnames)] # for colnames - - # Separe variables from first column - dat <- tidyr::separate_(dat, col = colnames(dat)[1], - into = cnames1, - sep = ",", convert = FALSE) + # Separate codes to columns + cnames <- strsplit(colnames(dat)[1], split = "[\\,]")[[1]] + cnames1 <- cnames[-length(cnames)] # for columns + cnames2 <- cnames[length(cnames)] # for colnames + + # Separate variables from first column + # OLD CODE + dat <- tidyr::separate(dat, + col = colnames(dat)[1], + into = cnames1, + sep = ",", + convert = FALSE + ) + if (!use.data.table) { # Get variable from column names - - cnames2_quo <- as.name(cnames2) - dat <- tidyr::gather(dat, !!cnames2_quo, values, - -seq_along(cnames1), - convert = FALSE) + # OLD CODE + dat <- tidyr::pivot_longer(data = dat, + cols = -seq_along(cnames1), + names_to = cnames2, + values_to = "values") # to save memory (and backward compatibility) + # OLD CODE dat <- dplyr::filter(dat, !is.na(values)) ## separate flags into separate column - if(keepFlags == TRUE) { + if (keepFlags == TRUE) { dat$flags <- as.vector( - stringi::stri_extract_first_regex(dat$values, - c("(^0n( [A-Za-z]+)*)|[A-Za-z]+"))) + stringi::stri_extract_first_regex( + dat$values, + c("(^0n( [A-Za-z]+)*)|[A-Za-z]+") + ) + ) } # clean time and values - dat$time <- gsub("X", "", dat$time) + # OLD CODE + dat$TIME_PERIOD <- gsub("X", "", dat$TIME_PERIOD, fixed = TRUE) dat$values <- as.numeric(gsub("[^0-9.-]+", "", as.character(dat$values))) - + # variable columns - var_cols <- names(dat)[!(names(dat) %in% c("time", "values"))] + var_cols <- names(dat)[!(names(dat) %in% c("TIME_PERIOD", "values"))] # reorder to standard order - dat <- dat[c(var_cols, "time", "values")] + # OLD CODE + dat <- dat[c(var_cols, "TIME_PERIOD", "values")] # columns from var_cols are converted into factors # avoid convert = FALSE since it converts T into TRUE instead of TOTAL - if (stringsAsFactors){ - dat[,var_cols] <- lapply(dat[, var_cols, drop = FALSE], - function(x) factor(x, levels = unique(x))) + if (stringsAsFactors) { + dat[, var_cols] <- lapply( + dat[, var_cols, drop = FALSE], + function(x) factor(x, levels = unique(x)) + ) } - - # For multiple time frequency - freqs <- available_freq(dat$time) + } else if (use.data.table) { + # NEW CODE: data.table + # defining dat as data.table object is necessary to use data.table functions + # dat <- data.table::as.data.table(dat) + # Coerce data.frame to data.table by reference + data.table::setDT(dat) - if (!is.null(select_time)){ - if (length(select_time) > 1) stop( - "Only one frequency should be selected in select_time. Selected: ", - shQuote(select_time)) - - # Annual - if (select_time == "Y"){ - dat <- subset(dat, nchar(time) == 4) - # Others - } else { - dat <- subset(dat, grepl(select_time, time)) - } - # Test if data - if (nrow(dat) == 0) stop( - "No data selected with select_time:", dQuote(select_time), "\n", - "Available frequencies: ", shQuote(freqs)) - } else { - - if (length(freqs) > 1 & time_format != "raw") stop( - "Data includes several time frequencies. Select frequency with - select_time or use time_format = \"raw\". - Available frequencies: ", shQuote(freqs )) + # Use factors by default to reduce RAM use (?) + dat[, (cnames1) := lapply(.SD, as.factor), .SDcols = cnames1] + + # NEW CODE: data.table + # Use pipe to reduce RAM use (?) + dat <- data.table::melt(data = dat, + measure.vars = setdiff(names(dat), cnames1), + variable.name = cnames2, + value.name = "values") %>% + na.omit(cols = "values") + + # NEW CODE: data.table + # should be using S3 method for data.table here... + # Problem has been with "vector memory exhausted (limit reached?)" + # dat <- na.omit(object = dat, cols = "values") + + ## separate flags into separate column + # Use data.table update := for smaller RAM footprint + if (keepFlags == TRUE) { + dat[, `:=`(flags = as.vector( + stringi::stri_extract_first_regex(values, c("(^0n( [A-Za-z]+)*)|[A-Za-z]+")) + ))] + } + + # clean time and values + # NEW CODE: use stringi instead of gsub for faster execution + # Use data.table update := for smaller RAM footprint + dat[, TIME_PERIOD := stringi::stri_replace_all_fixed(TIME_PERIOD, "X", "")] + # dat$TIME_PERIOD <- stringi::stri_replace_all_fixed(dat$TIME_PERIOD, "X", "") + dat[, values := stringi::stri_replace_all_regex(values, "[^0-9.-]+", "")] + dat[, values := as.numeric(values)] + # dat$values <- as.numeric( + # stringi::stri_replace_all_regex(as.character(dat$values), "[^0-9.-]+", "") + # ) + + # variable columns + # cnames1 is the same as var_cols so cnames1 is used + # var_cols <- names(dat)[!(names(dat) %in% c("TIME_PERIOD", "values"))] + # selected_cols <- c(var_cols, "TIME_PERIOD", "values") + + # reorder to standard order + # NEW CODE: data.table + # either this way + # dat <- dat[, ..selected_cols] + # or this way + # reorder to standard order + data.table::setcolorder(dat, c(cnames1, "TIME_PERIOD", "values")) + + # Turn factors back into characters if stringsAsFactors = FALSE + # columns from cnames1 (var_cols) are converted into factors + if (!stringsAsFactors) { + dat[, (cnames1) := lapply(.SD, as.character), .SDcols = cnames1] } - # convert time column - dat$time <- convert_time_col(dat$time, - time_format = time_format) + dat <- tibble::as_tibble(dat) + } + # For multiple time frequency + freqs <- unique(dat$freq) + if (!is.null(select_time)) { + if (length(select_time) > 1) { + message( + "Selected multiple time frequencies with select_time parameter: ", + shQuote(select_time) + ) + } - dat + # Filter dataset according to select_time filter + # This only works when a single frequency is chosen + if (identical(select_time, "Y")) { + # Annual with old style notation, "Y" for annual + dat <- subset(dat, dat$freq == "A") + } else if (identical(select_time, "A")) { + # Annual with new notation, "A" for annual + dat <- subset(dat, dat$freq == "A") + } else { + # Others, subset the data with whatever choices + dat <- subset(dat, dat$freq %in% select_time) + } + # Test if filtered dataset actually contains any data + if (nrow(dat) == 0) { + stop( + "No data selected with select_time:", dQuote(select_time), "\n", + "Available frequencies: ", shQuote(freqs) + ) + } + } else { + if (length(freqs) > 1 && time_format != "raw") { + message( + "Data includes several time frequencies. Select a single frequency \n", + "with select_time or use time_format = \"raw\" to return all data \n", + "without any filtering. Available frequencies: ", shQuote(freqs), "\n", + "Returning the dataset with multiple frequencies." + ) + } + } + + if (length(select_time) > 1) { + dat_copy <- data.frame() + for (i in seq_along(select_time)) { + dat_subset <- subset(dat, dat$freq == select_time[i]) + dat_subset$TIME_PERIOD <- convert_time_col(x = dat_subset$TIME_PERIOD, + time_format = time_format) + dat_copy <- rbind(dat_copy, dat_subset) + } + dat <- dat_copy + } else { + # convert time column to Date + dat$TIME_PERIOD <- convert_time_col(x = dat$TIME_PERIOD, + time_format = time_format) + } + + # NEW CODE: data.table + # This is needed if we still want to return tibbles at the end + # dat <- tibble::as_tibble(dat) + dat } -#' @title Time Column Conversions -#' @description Internal function to convert time column. -#' @param x A time column (vector) -#' @param time_format see \code{\link{tidy_eurostat}} +#' @title +#' Time Column Conversions for data from new dissemination API +#' @description +#' Internal function to convert time column. +#' @param x +#' A time column (vector) from a downloaded dataset +#' @param time_format +#' one of the following: `date`, `date_last`, or `num`. +#' See [tidy_eurostat()] for more information. #' @keywords internal -convert_time_col <- function(x, time_format){ - - if (time_format == "raw"){ +convert_time_col <- function(x, time_format) { + if (time_format == "raw") { y <- x } else { x <- factor(x) - if (time_format == "date"){ + if (time_format == "date") { y <- eurotime2date(x, last = FALSE) - } else if (time_format == "date_last"){ + } else if (time_format == "date_last") { y <- eurotime2date(x, last = TRUE) - } else if (time_format == "num"){ + } else if (time_format == "num") { y <- eurotime2num(x) - } else if (time_format == "raw") { - } else { - stop("An unknown time_format argument: ", time_format, - " Allowed are date, date_last, num and raw") + stop( + "An unknown time_format argument: ", time_format, + " Allowed are date, date_last, num and raw" + ) } } y diff --git a/R/utils.R b/R/utils.R index e22063c7..71b323c9 100755 --- a/R/utils.R +++ b/R/utils.R @@ -2,13 +2,107 @@ # Should not be exported # Return vector of available frequencies # param x a vector of "raw" time variable -available_freq <- function(x){ +available_freq <- function(x) { if (is.factor(x)) x <- levels(x) x <- gsub("X", "", x) freq <- c() if (any(nchar(x) == 4)) freq <- c(freq, "Y") freq_char <- unique( - substr(grep("[[:alpha:]]", x, value = TRUE), 5,5)) + substr(grep("[[:alpha:]]", x, value = TRUE), 5, 5) + ) freq <- c(freq, freq_char) freq -} \ No newline at end of file +} + +## load a eurostat package data without causing binding errors +## controlled loading in functions without loading the data +## to the global environment + +load_package_data <- function(dataset) { + .new_data_environment <- new.env(parent = emptyenv()) # a new environment + + if (!exists(x = dataset, envir = .new_data_environment)) { + utils::data( + list = dataset, + package = "eurostat", + envir = .new_data_environment + ) + } + .new_data_environment[[dataset]] +} + +check_lang <- function(lang) { + if (!is.null(lang)) { + # The Language parameter (“lang”) can have only three values: + # "en" (English), "fr" (French), and "de" (German). + lang <- tolower(lang) + if (lang %in% c("en", "fr", "de")) { + return(lang) + } else { + message( + "Unsupported language code used. Using the default language: \"en\"" + ) + lang <- "en" + } + } else { + # In case the parameter isn’t specified, the default value "en" is taken. + message("Using the default language: \"en\"") + lang <- "en" + } + lang +} + +#' @title Calculate a fixity checksum for an object +#' @description +#' Uses a hash function (md5) on an object and calculates a digest of the object +#' in the form of a character string. +#' +#' @details +#' “Fixity, in the preservation sense, means the assurance that a digital file +#' has remained unchanged, i.e. fixed.” (Bailey, 2014). In practice, fixity +#' can most easily be established by calculating a checksum for the data object +#' that changes if anything in the data object has changed. What we use as a +#' checksum here is by default calculated with md5 hash algorithm. It is +#' possible to use other algorithms supported by the imported digest function, +#' see function documentation. +#' +#' In the case of big objects with millions of rows of data calculating a +#' checksum can take a bit longer and require some amount of RAM to be +#' available. Selecting another algorithm might perform faster and/or more +#' efficiently. Whichever algorithm you are using, please make sure to report +#' it transparently in your work for transparency and ensuring replicability. +#' +#' This function takes the whole data object as an input, meaning that +#' everything counts when calculating the fixity checksum. If the dataset +#' column names are labeled, if the data itself is labeled, if stringsAsFactors +#' is TRUE, if flags are removed or kept, if data is somehow edited... all these +#' affect the calculated checksum. It is advisable to calculate the checksum +#' immediately after downloading the data, before adding any labels or doing +#' other mutating operations. If you are using other arguments than the default +#' ones when downloading data, it is also good to report the exact arguments +#' used. +#' +#' This implementation fulfills the level 1 requirement of National Digital +#' Stewardship Alliance (NDSA) preservation levels by creating "fixity info +#' if it wasn’t provided with the content". In the current version of the +#' package, fixity information has to be created manually and is at the +#' responsibility of the user. +#' +#' @param data_object A dataset downloaded with some eurostat package function. +#' @param algorithm Algorithm to use when calculating a checksum for a dataset. +#' Default is 'md5', but can be any supported algorithm in digest function. +#' +#' @seealso [digest::digest()] +#' @source https://www.dpconline.org/handbook/technical-solutions-and-tools/fixity-and-checksums +#' @importFrom digest digest +#' @keywords internal utilities +fixity_checksum <- function(data_object, algorithm = "md5") { + if (!(algorithm %in% c("md5", "sha1", "crc32", "sha256", "sha512", + "xxhash32", "xxhash64", "murmur32", "spookyhash", + "blake3", "crc32c"))) { + stop("Use a valid algorithm. See digest::digest function documentation") + } + + fixity <- digest::digest(data_object, algo = algorithm) + fixity +} diff --git a/R/zzz.R b/R/zzz.R index 2fabc867..b72f1bdc 100755 --- a/R/zzz.R +++ b/R/zzz.R @@ -1,10 +1,10 @@ .onLoad <- function(libname, pkgname) { - op <- options() - op.eurostat <- list( - eurostat_url = "https://ec.europa.eu/eurostat/" - ) - # Only set options that are not already set - toset <- !(names(op.eurostat) %in% names(op)) - if (any(toset)) options(op.eurostat[toset]) - invisible() + op <- options() + op.eurostat <- list( + eurostat_url = "https://ec.europa.eu/eurostat/" + ) + # Only set options that are not already set + toset <- !(names(op.eurostat) %in% names(op)) + if (any(toset)) options(op.eurostat[toset]) + invisible() } diff --git a/README.Rmd b/README.Rmd index 94b01248..33ff09ff 100755 --- a/README.Rmd +++ b/README.Rmd @@ -1,9 +1,8 @@ --- -output: - html_document: - keep_md: yes +output: github_document --- + ```{r, echo = FALSE} knitr::opts_chunk$set( @@ -13,42 +12,87 @@ knitr::opts_chunk$set( ) ``` -
- - - - - -[![Build Status](https://travis-ci.org/rOpenGov/eurostat.svg?branch=master)](https://travis-ci.org/rOpenGov/eurostat) -[![AppVeyor Status](https://ci.appveyor.com/api/projects/status/github/rOpenGov/eurostat?branch=master&svg=true)](https://ci.appveyor.com/project/rOpenGov/eurostat) -[![codecov.io](https://codecov.io/github/rOpenGov/eurostat/coverage.svg?branch=master)](https://codecov.io/github/rOpenGov/eurostat?branch=master) + +[![rOG-badge](https://ropengov.github.io/rogtemplate/reference/figures/ropengov-badge.svg)](https://ropengov.org/) +[![R-CMD-check](https://github.com/rOpenGov/eurostat/actions/workflows/check-full.yaml/badge.svg)](https://github.com/rOpenGov/eurostat/actions/workflows/check-full.yaml) +[![R-CMD-check (standard)](https://github.com/rOpenGov/eurostat/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/rOpenGov/eurostat/actions/workflows/check-standard.yaml) +[![cran version](http://www.r-pkg.org/badges/version/eurostat)](https://CRAN.R-project.org/package=eurostat) +[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable-1) +[![DOI](https://img.shields.io/badge/DOI-10.32614/RJ--2017--019-blue)](https://doi.org/10.32614/RJ-2017-019) +[![codecov](https://codecov.io/gh/rOpenGov/eurostat/branch/master/graph/badge.svg?token=Wp2VVvpWQA)](https://app.codecov.io/gh/rOpenGov/eurostat) [![Downloads](http://cranlogs.r-pkg.org/badges/grand-total/eurostat)](https://cran.r-project.org/package=eurostat) [![Downloads](http://cranlogs.r-pkg.org/badges/eurostat)](https://cran.r-project.org/package=eurostat) -[![Gitter](https://badges.gitter.im/rOpenGov/eurostat.svg)](https://gitter.im/rOpenGov/eurostat?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) +[![Gitter](https://badges.gitter.im/rOpenGov/eurostat.svg)](https://app.gitter.im/#/room/#rOpenGov_eurostat:gitter.im?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) +[![r-universe](https://ropengov.r-universe.dev/badges/eurostat)](https://ropengov.r-universe.dev/) + [![Watch on GitHub][github-watch-badge]][github-watch] [![Star on GitHub][github-star-badge]][github-star] -[![Follow](https://img.shields.io/twitter/follow/ropengov.svg?style=social)](https://twitter.com/intent/follow?screen_name=ropengov) - +[![Follow](https://img.shields.io/twitter/follow/ropengov.svg?style=social)](https://twitter.com/ropengov) + + + + + + - - -
+ -# eurostat R package +# eurostat R package - - -R tools to access open data from [Eurostat](http://ec.europa.eu/eurostat). Data search, download, manipulation and visualization. +R tools to access open data from [Eurostat](https://ec.europa.eu/eurostat). Data search, download, manipulation and visualization. ### Installation and use -See the [package homepage](http://ropengov.github.io/eurostat) for more. +Install stable version from CRAN: + +```{r, eval = FALSE} +install.packages("eurostat") +``` + +Alternatively, install development version from GitHub: + +```{r, eval = FALSE} +# Install from GitHub +library(devtools) +devtools::install_github("ropengov/eurostat") +``` + +Development version can be also installed using the +[r-universe](https://ropengov.r-universe.dev): + +```{r, eval=FALSE} +# Enable this universe +options(repos = c( + ropengov = "https://ropengov.r-universe.dev", + CRAN = "https://cloud.r-project.org" +)) + +install.packages("eurostat") +``` + +The package provides several different ways to get datasets from Eurostat. Searching for data is one way, if you know what to look for. + +```{r example, eval = TRUE} +# Load the package +library(eurostat) + +# Perform a simple search and print a table +passengers <- search_eurostat("passenger transport") +knitr::kable(head(passengers)) +``` + + +See the [Tutorial](https://ropengov.github.io/eurostat/articles/articles/eurostat_tutorial.html) and other resources at the [package homepage](https://ropengov.github.io/eurostat/) for more information and examples. + +### Recommended packages + +It is recommended to install the `giscoR` package (https://dieghernan.github.io/giscoR/). This is another API package that provides R tools for Eurostat geographic data to support geospatial analysis and visualization. ### Contribute @@ -57,43 +101,35 @@ Contributions are very welcome: * [Use issue tracker](https://github.com/ropengov/eurostat/issues) for feedback and bug reports. * [Send pull requests](https://github.com/ropengov/eurostat/) - * [Star us on the Github page](https://github.com/ropengov/eurostat) - * [Join the discussion in Gitter](https://gitter.im/rOpenGov/eurostat) + * [Star us on the Github page](https://github.com/ropengov/eurostat/) + * [Join the discussion in Gitter](https://app.gitter.im/#/room/#rOpenGov_eurostat:gitter.im) ### Acknowledgements -**Kindly cite this work** as follows: [Leo Lahti](https://github.com/antagomir), Przemyslaw Biecek, Markus Kainu and Janne Huovari. Retrieval and analysis of Eurostat open data with the eurostat package. [R Journal 9(1):385-392, 2017](https://journal.r-project.org/archive/2017/RJ-2017-019/index.html). R package version `r sessionInfo()$otherPkgs$eurostat$Version`. URL: [http://ropengov.github.io/eurostat](http://ropengov.github.io/eurostat) +**Kindly cite this package** by citing the following R Journal article: -We are greatful to all [contributors](https://github.com/rOpenGov/eurostat/graphs/contributors), including Joona Lehtomäki, Francois Briatte, and Oliver Reiter, and for the [Eurostat](http://ec.europa.eu/eurostat/) open data portal! This project is part of [rOpenGov](http://ropengov.github.io). +[Lahti L.](https://github.com/antagomir), Huovari J., Kainu M., and Biecek P. (2017). Retrieval and analysis of Eurostat open data with the eurostat package. [The R Journal 9(1), pp. 385-392](https://journal.r-project.org/archive/2017/RJ-2017-019/index.html). doi: [10.32614/RJ-2017-019](https://doi.org/10.32614/RJ-2017-019). +In addition, please provide a citation to the specific software version used: +Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data [Computer software]. R package version 4.0.0.9003. https://github.com/rOpenGov/eurostat + +We are grateful to all [contributors](https://github.com/ropengov/eurostat/graphs/contributors), including Daniel Antal, Joona Lehtomäki, Francois Briatte, and Oliver Reiter, and for the [Eurostat](https://ec.europa.eu/eurostat/) open data portal! This project is part of [rOpenGov](https://ropengov.org). + +This project has received funding from the European Union under grant No 101095295 (OpenMUSE), the FIN-CLARIAH research infrastructure and the Strategic Research Council's YOUNG program by the Research Council of Finland (decisions 345630, 358720, 367756, 352604). ### Disclaimer This package is in no way officially related to or endorsed by Eurostat. +When using data retrieved from Eurostat database in your work, please indicate that the data source is Eurostat. If your re-use involves some kind of modification to data or text, please state this clearly to the end user. See Eurostat policy on [copyright and free re-use of data](https://ec.europa.eu/eurostat/about/policies/copyright) for more detailed information and certain exceptions. -[chat-badge]: https://img.shields.io/badge/chat-on%20gitter-46BC99.svg?style=flat-square -[chat]: https://gitter.im/ropengov/eurostat?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge -[build-badge]: https://img.shields.io/travis/ropengov/eurostat.svg?style=flat-square -[build]: https://travis-ci.org/ropengov/eurostat -[version-badge]: https://img.shields.io/npm/v/eurostat.svg?style=flat-square -[package]: https://www.npmjs.com/package/eurostat [license-badge]: https://img.shields.io/npm/l/eurostat.svg?style=flat-square [license]: https://github.com/ropengov/eurostat/blob/master/LICENSE -[prs-badge]: https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square -[prs]: http://makeapullrequest.com -[donate-badge]: https://img.shields.io/badge/$-support-green.svg?style=flat-square -[donate]: http://kcd.im/donate -[coc-badge]: https://img.shields.io/badge/code%20of-conduct-ff69b4.svg?style=flat-square -[coc]: https://github.com/ropengov/eurostat/blob/master/other/CODE_OF_CONDUCT.md -[implementations-badge]: https://img.shields.io/badge/%F0%9F%92%A1-implementations-8C8E93.svg?style=flat-square -[implementations]: https://github.com/ropengov/eurostat/blob/master/other/IMPLEMENTATIONS.md + [github-watch-badge]: https://img.shields.io/github/watchers/ropengov/eurostat.svg?style=social [github-watch]: https://github.com/ropengov/eurostat/watchers [github-star-badge]: https://img.shields.io/github/stars/ropengov/eurostat.svg?style=social [github-star]: https://github.com/ropengov/eurostat/stargazers -[twitter]: https://twitter.com/intent/tweet?text=Check%20out%20eurostat!%20%E2%9C%A8%20Recognize%20all%20contributors,%20not%20just%20the%20ones%20who%20commit%20code%20%E2%9C%A8%20https://github.com/ropengov/eurostat%20%F0%9F%A4%97 -[twitter-badge]: https://img.shields.io/twitter/url/https/github.com/ropengov/eurostat.svg?style=social -[emojis]: https://github.com/ropengov/eurostat#emoji-key -[eurostat]: https://github.com/ropengov/eurostat + +[eurostat]: https://github.com/ropengov/eurostat/ diff --git a/README.md b/README.md old mode 100755 new mode 100644 index 236549d0..db0aa2e0 --- a/README.md +++ b/README.md @@ -1,93 +1,152 @@ ---- -output: - html_document: - keep_md: yes ---- - - + + + +[![rOG-badge](https://ropengov.github.io/rogtemplate/reference/figures/ropengov-badge.svg)](https://ropengov.org/) +[![R-CMD-check](https://github.com/rOpenGov/eurostat/actions/workflows/check-full.yaml/badge.svg)](https://github.com/rOpenGov/eurostat/actions/workflows/check-full.yaml) +[![R-CMD-check +(standard)](https://github.com/rOpenGov/eurostat/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/rOpenGov/eurostat/actions/workflows/check-standard.yaml) +[![cran +version](http://www.r-pkg.org/badges/version/eurostat)](https://CRAN.R-project.org/package=eurostat) +[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable-1) +[![DOI](https://img.shields.io/badge/DOI-10.32614/RJ--2017--019-blue)](https://doi.org/10.32614/RJ-2017-019) +[![codecov](https://codecov.io/gh/rOpenGov/eurostat/branch/master/graph/badge.svg?token=Wp2VVvpWQA)](https://app.codecov.io/gh/rOpenGov/eurostat) +[![Downloads](http://cranlogs.r-pkg.org/badges/grand-total/eurostat)](https://cran.r-project.org/package=eurostat) +[![Downloads](http://cranlogs.r-pkg.org/badges/eurostat)](https://cran.r-project.org/package=eurostat) +[![Gitter](https://badges.gitter.im/rOpenGov/eurostat.svg)](https://app.gitter.im/#/room/#rOpenGov_eurostat:gitter.im?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) +[![r-universe](https://ropengov.r-universe.dev/badges/eurostat)](https://ropengov.r-universe.dev/) -
+[![Watch on +GitHub](https://img.shields.io/github/watchers/ropengov/eurostat.svg?style=social)](https://github.com/ropengov/eurostat/watchers) +[![Star on +GitHub](https://img.shields.io/github/stars/ropengov/eurostat.svg?style=social)](https://github.com/ropengov/eurostat/stargazers) +[![Follow](https://img.shields.io/twitter/follow/ropengov.svg?style=social)](https://twitter.com/ropengov) + + -[![Build Status](https://travis-ci.org/rOpenGov/eurostat.svg?branch=master)](https://travis-ci.org/rOpenGov/eurostat) -[![AppVeyor Status](https://ci.appveyor.com/api/projects/status/github/rOpenGov/eurostat?branch=master&svg=true)](https://ci.appveyor.com/project/rOpenGov/eurostat) -[![codecov.io](https://codecov.io/github/rOpenGov/eurostat/coverage.svg?branch=master)](https://codecov.io/github/rOpenGov/eurostat?branch=master) -[![Downloads](http://cranlogs.r-pkg.org/badges/grand-total/eurostat)](https://cran.r-project.org/package=eurostat) -[![Downloads](http://cranlogs.r-pkg.org/badges/eurostat)](https://cran.r-project.org/package=eurostat) -[![Gitter](https://badges.gitter.im/rOpenGov/eurostat.svg)](https://gitter.im/rOpenGov/eurostat?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) -[![Watch on GitHub][github-watch-badge]][github-watch] -[![Star on GitHub][github-star-badge]][github-star] -[![Follow](https://img.shields.io/twitter/follow/ropengov.svg?style=social)](https://twitter.com/intent/follow?screen_name=ropengov) - - - - + -
+# eurostat R package -# eurostat R package +R tools to access open data from +[Eurostat](https://ec.europa.eu/eurostat). Data search, download, +manipulation and visualization. - +### Installation and use -R tools to access open data from [Eurostat](http://ec.europa.eu/eurostat). Data search, download, manipulation and visualization. +Install stable version from CRAN: +``` r +install.packages("eurostat") +``` -### Installation and use +Alternatively, install development version from GitHub: + +``` r +# Install from GitHub +library(devtools) +devtools::install_github("ropengov/eurostat") +``` + +Development version can be also installed using the +[r-universe](https://ropengov.r-universe.dev): + +``` r +# Enable this universe +options(repos = c( + ropengov = "https://ropengov.r-universe.dev", + CRAN = "https://cloud.r-project.org" +)) + +install.packages("eurostat") +``` + +The package provides several different ways to get datasets from +Eurostat. Searching for data is one way, if you know what to look for. + +``` r +# Load the package +library(eurostat) -See the [package homepage](http://ropengov.github.io/eurostat) for more. +# Perform a simple search and print a table +passengers <- search_eurostat("passenger transport") +knitr::kable(head(passengers)) +``` +| title | code | type | last.update.of.data | last.table.structure.change | data.start | data.end | values | hierarchy | +|:---------------------------------------------------------------------------------------------|:-----------------|:--------|:--------------------|:----------------------------|:-----------|:---------|--------:|----------:| +| Air passenger transport | enps_avia_pa | dataset | 13.03.2023 | 13.03.2023 | 2005 | 2021 | 406 | 6 | +| Modal split of air, sea and inland passenger transport | tran_hv_ms_psmod | dataset | 29.06.2023 | 29.06.2023 | 2008 | 2021 | 2100 | 4 | +| Modal split of inland passenger transport | tran_hv_psmod | dataset | 29.06.2023 | 29.06.2023 | 1990 | 2021 | 4219 | 4 | +| Volume of passenger transport relative to GDP | tran_hv_pstra | dataset | 11.08.2023 | 29.06.2023 | 1990 | 2021 | 969 | 4 | +| Maritime passenger transport performed in the Exclusive Economic Zone (EEZ) of the countries | mar_tp_pa | dataset | 21.02.2023 | 21.02.2023 | 2005 | 2021 | 1752 | 4 | +| Air passenger transport by reporting country | avia_paoc | dataset | 04.12.2023 | 28.11.2023 | 1993 | 2023-Q3 | 2482969 | 5 | + +See the +[Tutorial](https://ropengov.github.io/eurostat/articles/articles/eurostat_tutorial.html) +and other resources at the [package +homepage](https://ropengov.github.io/eurostat/) for more information and +examples. + +### Recommended packages + +It is recommended to install the `giscoR` package +(). This is another API package +that provides R tools for Eurostat geographic data to support geospatial +analysis and visualization. ### Contribute Contributions are very welcome: - * [Use issue tracker](https://github.com/ropengov/eurostat/issues) for feedback and bug reports. - * [Send pull requests](https://github.com/ropengov/eurostat/) - * [Star us on the Github page](https://github.com/ropengov/eurostat) - * [Join the discussion in Gitter](https://gitter.im/rOpenGov/eurostat) +- [Use issue tracker](https://github.com/ropengov/eurostat/issues) for + feedback and bug reports. +- [Send pull requests](https://github.com/ropengov/eurostat/) +- [Star us on the Github page](https://github.com/ropengov/eurostat/) +- [Join the discussion in + Gitter](https://app.gitter.im/#/room/#rOpenGov_eurostat:gitter.im) ### Acknowledgements -**Kindly cite this work** as follows: [Leo Lahti](https://github.com/antagomir), Przemyslaw Biecek, Markus Kainu and Janne Huovari. Retrieval and analysis of Eurostat open data with the eurostat package. [R Journal 9(1):385-392, 2017](https://journal.r-project.org/archive/2017/RJ-2017-019/index.html). R package version 3.2.93. URL: [http://ropengov.github.io/eurostat](http://ropengov.github.io/eurostat) +**Kindly cite this package** by citing the following R Journal article: -We are greatful to all [contributors](https://github.com/rOpenGov/eurostat/graphs/contributors), including Joona Lehtomäki, Francois Briatte, and Oliver Reiter, and for the [Eurostat](http://ec.europa.eu/eurostat/) open data portal! This project is part of [rOpenGov](http://ropengov.github.io). +[Lahti L.](https://github.com/antagomir), Huovari J., Kainu M., and +Biecek P. (2017). Retrieval and analysis of Eurostat open data with the +eurostat package. [The R Journal 9(1), +pp. 385-392](https://journal.r-project.org/archive/2017/RJ-2017-019/index.html). +doi: [10.32614/RJ-2017-019](https://doi.org/10.32614/RJ-2017-019). +In addition, please provide a citation to the specific software version +used: +Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., +and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data +\[Computer software\]. R package version 4.0.0.9003. + + +We are grateful to all +[contributors](https://github.com/ropengov/eurostat/graphs/contributors), +including Daniel Antal, Joona Lehtomäki, Francois Briatte, and Oliver +Reiter, and for the [Eurostat](https://ec.europa.eu/eurostat/) open data +portal! This project is part of [rOpenGov](https://ropengov.org). + +This project has received funding from the European Union under grant No 101095295 (OpenMUSE), the FIN-CLARIAH research infrastructure and the Strategic Research Council's YOUNG program by the Research Council of Finland (decisions 345630, 358720, 367756, 352604). ### Disclaimer This package is in no way officially related to or endorsed by Eurostat. - -[chat-badge]: https://img.shields.io/badge/chat-on%20gitter-46BC99.svg?style=flat-square -[chat]: https://gitter.im/ropengov/eurostat?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge -[build-badge]: https://img.shields.io/travis/ropengov/eurostat.svg?style=flat-square -[build]: https://travis-ci.org/ropengov/eurostat -[version-badge]: https://img.shields.io/npm/v/eurostat.svg?style=flat-square -[package]: https://www.npmjs.com/package/eurostat -[license-badge]: https://img.shields.io/npm/l/eurostat.svg?style=flat-square -[license]: https://github.com/ropengov/eurostat/blob/master/LICENSE -[prs-badge]: https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square -[prs]: http://makeapullrequest.com -[donate-badge]: https://img.shields.io/badge/$-support-green.svg?style=flat-square -[donate]: http://kcd.im/donate -[coc-badge]: https://img.shields.io/badge/code%20of-conduct-ff69b4.svg?style=flat-square -[coc]: https://github.com/ropengov/eurostat/blob/master/other/CODE_OF_CONDUCT.md -[implementations-badge]: https://img.shields.io/badge/%F0%9F%92%A1-implementations-8C8E93.svg?style=flat-square -[implementations]: https://github.com/ropengov/eurostat/blob/master/other/IMPLEMENTATIONS.md -[github-watch-badge]: https://img.shields.io/github/watchers/ropengov/eurostat.svg?style=social -[github-watch]: https://github.com/ropengov/eurostat/watchers -[github-star-badge]: https://img.shields.io/github/stars/ropengov/eurostat.svg?style=social -[github-star]: https://github.com/ropengov/eurostat/stargazers -[twitter]: https://twitter.com/intent/tweet?text=Check%20out%20eurostat!%20%E2%9C%A8%20Recognize%20all%20contributors,%20not%20just%20the%20ones%20who%20commit%20code%20%E2%9C%A8%20https://github.com/ropengov/eurostat%20%F0%9F%A4%97 -[twitter-badge]: https://img.shields.io/twitter/url/https/github.com/ropengov/eurostat.svg?style=social -[emojis]: https://github.com/ropengov/eurostat#emoji-key -[eurostat]: https://github.com/ropengov/eurostat +When using data retrieved from Eurostat database in your work, please +indicate that the data source is Eurostat. If your re-use involves some +kind of modification to data or text, please state this clearly to the +end user. See Eurostat policy on [copyright and free re-use of +data](https://ec.europa.eu/eurostat/about/policies/copyright) for more +detailed information and certain exceptions. diff --git a/appveyor.yml b/appveyor.yml deleted file mode 100755 index e32d316c..00000000 --- a/appveyor.yml +++ /dev/null @@ -1,42 +0,0 @@ -# DO NOT CHANGE the "init" and "install" sections below - -# Download script file from GitHub -init: - ps: | - $ErrorActionPreference = "Stop" - Invoke-WebRequest http://raw.github.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "..\appveyor-tool.ps1" - Import-Module '..\appveyor-tool.ps1' - -install: - ps: Bootstrap - -# Adapt as necessary starting from here - -build_script: - - travis-tool.sh install_deps - -test_script: - - travis-tool.sh run_tests - -on_failure: - - 7z a failure.zip *.Rcheck\* - - appveyor PushArtifact failure.zip - -artifacts: - - path: '*.Rcheck\**\*.log' - name: Logs - - - path: '*.Rcheck\**\*.out' - name: Logs - - - path: '*.Rcheck\**\*.fail' - name: Logs - - - path: '*.Rcheck\**\*.Rout' - name: Logs - - - path: '\*_*.tar.gz' - name: Bits - - - path: '\*_*.zip' - name: Bits diff --git a/cran-comments.md b/cran-comments.md old mode 100755 new mode 100644 index 50c6b450..5eef97f9 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,24 +1,21 @@ -## Test environments -* local OS X install, R 3.3.1 -* ubuntu 12.04 (on travis-ci), R 3.3.1 -* win-builder (devel and release) - ## R CMD check results -0 errors | 0 warnings | 1 note +On R 4.2.2 under aarch64-apple-darwin20 (64-bit), macOS Ventura 13.2.1 -* This is a new release. +0 errors | 0 warnings | 1 note -## Reverse dependencies +* checking for future file timestamps ... NOTE + unable to verify current time + +Seems to be a problem related to worldclockapi.com, that has already been discussed in 2019 on r-package-devel mailing list. -To be checked. +On R 2023-02-21 r83888 ucrt under x86_64-w64-mingw32 (64-bit), Windows Server 2022 x64 (devtools::check_win_devel): ---- +0 errors | 0 warnings | 0 notes -* I have run R CMD check on the NUMBER downstream dependencies. - (Summary at ...). - -* FAILURE SUMMARY +## Downstream dependencies -* All revdep maintainers were notified of the release on RELEASE DATE. +We checked 8 reverse dependencies with revdepcheck, comparing R CMD check results across CRAN and dev versions of this package. + * We saw 0 new problems + * We failed to check 0 packages diff --git a/data-raw/country_list.R b/data-raw/country_list.R index fa1b1835..31efcbb5 100755 --- a/data-raw/country_list.R +++ b/data-raw/country_list.R @@ -1,4 +1,3 @@ - # Code to get countries and their codes for selected country groups. library(rvest) @@ -7,30 +6,44 @@ library(devtools) load_all() country_html <- read_html("http://ec.europa.eu/eurostat/statistics-explained/index.php/Tutorial:Country_codes_and_protocol_order") -c_tables<- country_html %>% +c_tables <- country_html %>% html_table() # Country data.tables with code and name -eu_countries <- c_tables[[2]] %>% - select(code = Code, name = English) - -efta_countries <- c_tables[[3]] %>% - select(code = Code, name = English) - -eu_candidate_countries <- c_tables[[4]] %>% - select(code = Code, name = English) +# Codes, names and protocol order of European Union (EU) Member States +eu_countries <- c_tables[[2]] %>% + select(code = Code, name = English) %>% + mutate(label = eurostat::label_eurostat(code, dic = "geo")) + +# Codes and names of EFTA countries +efta_countries <- c_tables[[3]] %>% + select(code = Code, name = English) %>% + mutate(label = eurostat::label_eurostat(code, dic = "geo")) + +# United Kingdom +united_kingdom <- c_tables[[4]] %>% + select(code = Code, name = English) %>% + mutate(label = eurostat::label_eurostat(code, dic = "geo")) + +# Codes and names of candidate countries +eu_candidate_countries <- c_tables[[5]] %>% + select(code = Code, name = English) %>% + mutate(label = eurostat::label_eurostat(code, dic = "geo")) # Euro area countries ea_country_html <- read_html("http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Euro_area") ea_countries <- ea_country_html %>% - html_table(fill = TRUE) %>% - unlist() %>% - {data_frame(name = grep("^[[:alpha:]]", ., value = TRUE))} %>% - inner_join(eu_countries, .) # Get eu order and codes - + html_table(fill = TRUE) %>% + unlist() %>% + { + tibble(name = grep("^[[:alpha:]]", ., value = TRUE)) + } %>% + inner_join(eu_countries, .) %>% # Get eu order and codes + mutate(label = eurostat::label_eurostat(code, dic = "geo")) + # Eurostat data set with ID tgs00026 -tgs00026 <- get_eurostat("tgs00026", time_format = "raw") +tgs00026 <- get_eurostat("tgs00026", time_format = "raw") print("save datasets") -devtools::use_data(eu_candidate_countries, eu_countries, ea_countries, efta_countries, tgs00026, overwrite = TRUE, internal = FALSE) +usethis::use_data(eu_candidate_countries, eu_countries, ea_countries, efta_countries, tgs00026, overwrite = TRUE, internal = FALSE) diff --git a/data-raw/eurostat_geodata_60_2016.R b/data-raw/eurostat_geodata_60_2016.R new file mode 100644 index 00000000..46f0254f --- /dev/null +++ b/data-raw/eurostat_geodata_60_2016.R @@ -0,0 +1,35 @@ +## code to prepare `eurostat_geodata_60_2016` dataset goes here +library(giscoR) +library(tidyverse) + +# Current names internal) +from_gisco <- gisco_get_nuts( + year = 2016, resolution = 60, + epsg = 4326, update_cache = TRUE, + verbose = TRUE +) + +from_gisco$geo <- from_gisco$NUTS_ID +from_gisco$id <- from_gisco$NUTS_ID + +# End + +eurostat_geodata_60_2016 <- from_gisco +unique(sf::st_is_valid(from_gisco)) + +# Sort by level and alphabetically +eurostat_geodata_60_2016 <- eurostat_geodata_60_2016 %>% + arrange(LEVL_CODE, NUTS_ID) + +# Arrange names in proper order +sfcol <- attr(eurostat_geodata_60_2016, "sf_column") +rest <- c( + "id", "LEVL_CODE", "NUTS_ID", "CNTR_CODE", "NAME_LATN", + "NUTS_NAME", "MOUNT_TYPE", "URBN_TYPE", "COAST_TYPE", + "FID", "geo" +) + +reorder <- intersect(unique(c(rest, sfcol)), names(eurostat_geodata_60_2016)) +eurostat_geodata_60_2016 <- eurostat_geodata_60_2016[, reorder] + +usethis::use_data(eurostat_geodata_60_2016, overwrite = TRUE, compress = "xz") diff --git a/data-raw/nuts_coding.R b/data-raw/nuts_coding.R new file mode 100755 index 00000000..80803208 --- /dev/null +++ b/data-raw/nuts_coding.R @@ -0,0 +1,193 @@ +library(readxl) +library(purrr) +library(dplyr) +library(tidyr) +# This file reads in the NUTS correspondence table published by +# Eurostat --------------------------------------------------- + +tf <- tempfile(fileext = ".xlsx") +download.file( + url = "https://ec.europa.eu/eurostat/documents/345175/629341/NUTS2013-NUTS2016.xlsx", + destfile = tf, mode = "wb" +) + +regional_changes_2016 <- readxl::read_excel(tf, + sheet = "NUTS2013-NUTS2016", + skip = 1, col_names = TRUE +) %>% + select(1:12) %>% + purrr::set_names(., c( + "rowid", "code13", "code16", + "country_name", "nuts1_name", + "nuts2_name", "nuts3_name", + "change", "nuts_level", + "sort_countries", "sort_13", "sort_16" + )) %>% + mutate(name = case_when( + !is.na(nuts1_name) ~ nuts1_name, + !is.na(nuts2_name) ~ nuts2_name, + !is.na(nuts3_name) ~ nuts3_name, + !is.na(country_name) ~ country_name, + TRUE ~ NA_character_ + )) + +nuts1_correspondence <- readxl::read_excel( + tf, + sheet = "Correspondence NUTS-1", + skip = 0, col_names = TRUE +) %>% + purrr::set_names(., c( + "code13", "code16", + "name", + "change", "resolution" + )) %>% + mutate_if(is.factor, as.character) %>% + mutate(nuts_level = 1) %>% + filter(name != "Centre-Est") # appears to be a duplicate and incorrect row, given that FR7 is also marked as recoded to FRK + +warning("FR7 - Centre-Est appears to be an errorneous line and it is removed from the correspondence table.") + +nuts2_correspondence <- readxl::read_excel( + tf, + sheet = "Correspondence NUTS-2", + skip = 0, col_names = TRUE +) %>% + select(1:5) %>% + purrr::set_names(., c( + "code13", "code16", + "name", + "change", "resolution" + )) %>% + filter(is.na(code13) + is.na(code16) < 2) %>% + mutate(nuts_level = 2) + +nuts3_correspondence <- readxl::read_excel( + tf, + sheet = "Correspondence NUTS-3", + skip = 0, col_names = TRUE +) %>% + select(1:5) %>% + purrr::set_names(., c( + "code13", "code16", + "name", + "change", "resolution" + )) %>% + filter(is.na(code13) + is.na(code16) < 2) %>% + mutate(nuts_level = 2) + + +nuts_correspondence <- rbind( + nuts1_correspondence, + nuts2_correspondence +) %>% + rbind(nuts3_correspondence) %>% + select(code13, code16, name, nuts_level, change, resolution) + +nuts_2016_codes <- unique(regional_changes_2016$code16) + +## In these cases, the code13 == code16 ------------------------------ +unchanged_regions <- regions %>% + filter(is.na(change)) %>% + fill(nuts1_name) %>% + fill(nuts2_name) %>% + select(code13, code16, name, nuts_level, change) %>% + mutate(change = "unchanged") + +## In these cases code13 != code16 ---------------------------------- +changed_regions <- regions %>% + filter(!is.na(change)) %>% + fill(nuts1_name) %>% + fill(nuts2_name) %>% + select(code13, code16, name, nuts_level, change) + +nuts_2016_codes <- unique(regional_changes_2016$code16)[!is.na(regional_changes_2016$code16)] +nuts_2013_codes <- unique(regional_changes_2016$code13)[!is.na(regional_changes_2016$code13)] +all_region_codes <- unique(c(nuts_2016_codes, nuts_2013_codes)) + +changed_region_codes <- all_region_codes[!all_region_codes %in% unchanged_regions$code16] +changed_region_codes <- sort(changed_region_codes[!is.na(changed_region_codes)]) + +regions_in_correspondence <- unique(c(nuts_correspondence$code13, nuts_correspondence$code16)) +regions_in_correspondence <- sort(regions_in_correspondence[!is.na(regions_in_correspondence)]) + +if (length( + changed_region_codes[!changed_region_codes %in% regions_in_correspondence] +) > 0) { + message("Problem with the following regional geo labels:") + message(changed_region_codes[!changed_region_codes %in% regions_in_correspondence]) + stop("They cannot be found in the correspondence table") +} + + +## Consistency check ---------------------------------------- +## The name field is inconsistent in two sheets, at least FR7 is not consistent +regions_in_correspondence <- regions_in_correspondence[!is.na(regions_in_correspondence)] + +nuts2013_in_changed <- unique(changed_regions$code13) +nuts2013_in_changed <- nuts2013_in_changed[!is.na(nuts2013_in_changed)] + +nuts2016_in_changed <- unique(changed_regions$code16) +nuts2016_in_changed <- nuts2016_in_changed[!is.na(nuts2016_in_changed)] + +all(nuts2013_in_changed %in% regions_in_correspondence) +all(nuts2016_in_changed %in% regions_in_correspondence) + +nuts2013_in_changed[!nuts2013_in_changed %in% regions_in_correspondence] +nuts2016_in_changed[!nuts2016_in_changed %in% regions_in_correspondence] + +## Consistency II ---------------------------------------------------- + +all_nuts_codes <- unique(c(nuts_2013_codes, nuts_2016_codes)) + +only_in_correspondence <- regions_in_correspondence[regions_in_correspondence %in% all_nuts_codes] + +only_13 <- nuts_correspondence %>% + filter(code13 %in% only_in_correspondence) + +only_16 <- nuts_correspondence %>% + filter(code16 %in% only_in_correspondence) + +only <- full_join(only_13, only_16) # they are unique + + +## Changed regions to be looked up by their NUTS2016 codes ----------- +regional_changes_by_2016 <- nuts_correspondence %>% + mutate(geo = code16) %>% + filter(!is.na(code16)) + +## adding those that have no equivalent in the previous group +## some regions have to be identified by their old and new codes ----- +regional_changes_by_2013 <- nuts_correspondence %>% + mutate(geo = code13) %>% + filter(!is.na(code13)) + +## Region can be found by new or old NUTS code ----------------------- + +all_regional_changes <- regional_changes_by_2016 %>% + full_join(regional_changes_by_2013, + by = c( + "code13", "code16", "name", "nuts_level", + "change", "resolution", "geo" + ) + ) + + +all_regional_changes %>% + add_count(code13, code16, name, nuts_level, change, resolution, geo) %>% + filter(n > 1) + +## Regional changes ------------------------------------------------ + +regional_changes_2016 <- rbind(changed_regions, unchanged_regions) + +discontinued_regions <- changed_regions %>% + filter(change == "discontinued") + + +## ---------------------------------------------------------------- +message("Save changed regions") +usethis::use_data(regional_changes_2016, + nuts_correspondence, + overwrite = TRUE, + internal = FALSE +) diff --git a/data-raw/nuts_correction.R b/data-raw/nuts_correction.R new file mode 100755 index 00000000..c0fac5f5 --- /dev/null +++ b/data-raw/nuts_correction.R @@ -0,0 +1,419 @@ +library(tidyverse) + +## Check for non-unique elements in nuts_correction.R + +check_dat_input <- function(dat) { + incorrect <- FALSE + if (!"geo" %in% names(dat)) correct <- TRUE + incorrect +} + +check_unique_values <- function(df) { + if ("corrected_values" %in% names(df)) { + df <- df %>% + rename(values = corrected_values) + } + + df <- df %>% + select(geo, indicator, time) %>% + add_count(geo, indicator, time) + + if (any(df$n) > 1) { + return(TRUE) + } else { + return(FALSE) + } +} + + +regdata_files <- dir("regdata")[!grepl("rest_of_ceemid", dir("regdata"))] +regdata_files <- regdata_files[grepl(".rds", regdata_files)] +regdata_files <- regdata_files[!grepl("corrected", regdata_files)] +regdata_files + +for (test in regdata_files) { + message("Testing ", test) + readRDS(file.path("regdata", test)) +} + +message("All regional data files read, none was corrupted on disk.") +source(file.path("R", "nuts_coding.R")) + +dat <- eurostat::tgs00026 %>% + check_nuts2013() + +correct_nuts_labelling <- function(dat) { + + ## Check if geo information is present ------------------------------ + if (check_dat_input(dat)) { + stop("There is no 'geo' column in the inserted data. This is an error.") + } + + + if (!"change" %in% names(dat)) { + dat <- check_nuts2013(dat) + } + unchanged_regions <- regional_changes_2016 %>% + filter(change == "unchanged") + + changed_regions <- regional_changes_2016 %>% + filter(change != "unchanged") + + nuts_2016_codes <- unique(regional_changes_2016$code16) + # for easier debugging, this data will be re-assigned in each major + # step as tmp2, tmp3... Debugging is particulary difficult, because + # not only the program code, but the underlying logic may have faults. + + tmp_eu_only <- tmp %>% + filter(change != "not_eu") # leave out non-EU regions. + + # Find those codes that are missing from the correct NUTS2016 codes + missing_2016_codes <- nuts_2016_codes[which(!nuts_2016_codes %in% tmp_eu_only$geo)] + missing_2016_codes <- missing_2016_codes[which(stringr::str_sub(missing_2016_codes, -3, -1) != "ZZZ")] + missing_2016_codes <- missing_2016_codes[which(stringr::str_sub(missing_2016_codes, -2, -1) != "ZZ")] + + # Sort them out by NUTS1 and NUTS2 levels + missing_nuts1_2016 <- missing_2016_codes[which(nchar(missing_2016_codes) == 3)] + missing_nuts2_2016 <- missing_2016_codes[which(nchar(missing_2016_codes) == 4)] + + # Separating labels that need to be corrected into tmp3 ---------------- + + correctly_labelled_unchanged <- tmp %>% + filter(change == "unchanged") + + tmp_changed <- tmp %>% + filter(change != "unchanged") + + correctly_labelled_changed <- tmp_changed %>% + filter(geo %in% changed_regions$code16) + + ## Finding incorrectly labelled NUTS1 geo labels -------------------- + incorrectlly_labelled_nuts1 <- tmp_changed %>% + filter(geo %in% changed_regions$code13) + + incorrectly_labelled_nuts1_2013 <- incorrectlly_labelled_nuts1 %>% + filter(nchar(as.character(geo)) == 3) %>% + select(-change) %>% + left_join(nuts_correspondence %>% + filter(nuts_level == 1) %>% + rename(geo = code13) %>% + filter(!is.na(geo)) %>% + select(geo, code16, change, resolution), + by = "geo" + ) %>% + filter(change != "discontinued") %>% + mutate(problem_code = geo) %>% + mutate(geo = code16) + + + ## NUTS1 labels that are missing and which are found ------------------ + nuts1_missings <- missing_nuts1_2016[which(missing_nuts1_2016 %in% incorrectly_labelled_nuts1_2013$geo)] + + found_nuts1 <- incorrectly_labelled_nuts1_2013 %>% + filter(geo %in% missing_nuts1_2016) + message(length(unique(found_nuts1$geo)), " incorrectly labelled NUTS1 regions could be re-labelled") + + ## Finding incorrectly labelled NUTS2 geo labels -------------------- + + incorrectly_labelled_nuts2_2013 <- incorrectlly_labelled_nuts13 %>% + filter(nchar(as.character(geo)) == 4) %>% + select(-change) %>% + left_join(nuts_correspondence %>% + filter(nuts_level == 2) %>% + rename(geo = code13) %>% + filter(!is.na(geo)) %>% + select(geo, code16, change, resolution), + by = "geo" + ) %>% + filter(change != "discontinued") %>% + mutate(problem_code = geo) %>% + mutate(geo = code16) + + recoded_nuts2_2013 <- incorrectly_labelled_nuts2_2013 %>% + filter(change == "recoded") + + found_nuts2 <- recoded_nuts2_2013 %>% + filter(geo %in% missing_nuts2_2016) + + message(length(unique(found_nuts2$geo)), " incorrectly labelled NUTS2 regions could be re-labelled.") + + ## If there are no corrections to made at all, return the original data frame ------------ + if (length(unique(found_nuts2$geo)) + length(unique(found_nuts1$geo)) == 0) { + message("There is no data found that can be further arranged.\nThe data is returned in its original format.") + return(dat) + } + + ## If there are changes to be made, make them from here ------------------ + join_by <- names(correctly_labelled_unchanged) + join_by <- join_by[which(join_by %in% names(correctly_labelled_changed))] + + join_by2 <- names(correctly_labelled_unchanged) + join_by2 <- join_by2[which(join_by2 %in% names(found_nuts1))] + + ## Add unchanged regions and changed, but correctly labelled ones + so_far_joined <- full_join(correctly_labelled_unchanged, + correctly_labelled_changed, + by = join_by + ) %>% + full_join(found_nuts1, by = join_by2) + + ## Add NUTS1 regions that were recoded, if there are any + if (nrow(found_nuts1) > 0) { + join_by3 <- names(so_far_joined) + join_by3 <- join_by3[which(join_by3 %in% names(found_nuts1))] + + so_far_joined <- so_far_joined %>% + full_join(found_nuts2, by = join_by3) + } + + + ## Add NUTS2 regions that were recoded, if there are any + if (nrow(found_nuts2) > 0) { + join_by4 <- names(so_far_joined) + join_by4 <- join_by3[which(join_by4 %in% names(found_nuts2))] + + so_far_joined <- so_far_joined %>% + full_join(found_nuts2, by = join_by4) + } + + + remaining_eu_data <- tmp %>% + filter(!geo %in% so_far_joined$geo) + + ## The following geo codes will be changed using rules ------------- + used_in_correction <- c( + "FR24", + "FR26", "FR43", + "FR23", "FR25", + "FR22", "FR30", + "FR21", "FR41", "FR42", + "FR51", + "FR52", + "FR53", "FR61", "FR63", + "FR62", "FR81", + "FR7", + "FR82", + "FR83", + "FRA", + "PL11", "PL33", + "PL3", + "PL12", + "IE023", "IE024", "IE025", + "LT00", "LT00A", + "UKM2", + "UKM31", "UKM34", "UKM35", "UKM36", + "UKM24", "UKM32", "UKM33", "UKM37", "UKM38", + "HU102", "HU101" + ) + + correct_with_correspondence <- remaining_eu_data %>% + select(time, country_code, years, indicator, geo, values) + + if (any(correct_with_correspondence$geo %in% so_far_joined$geo)) { + stop("There are overlaps with the already corrected dataset") + } + + if (check_unique_values(correct_with_correspondence)) { + stop("There were joining errors, please check correct_with_correspondence") + } + + ## The data has time and space dimension, so corrections have to be + ## made for each year in the dataset---------------------------- + correspondence_by_year <- function(df, this_time) { + df <- df %>% filter(time == this_time) + + complete_with_missing <- tibble( + geo = used_in_correction[which(!unique(used_in_correction) %in% df$geo)], + country_code = case_when( + substr(geo, 1, 2) == "UK" ~ "GB", + substr(geo, 1, 2) == "EL" ~ "GR", + TRUE ~ substr(geo, 1, 2) + ), + values = NA_real_, + indicator = NA_character_, + time = this_time, + years = as.numeric(substr(as.character(this_time), 1, 4)) + ) + + correct_with_correspondence <- df %>% + full_join(., complete_with_missing, + by = c( + "indicator", "geo", "values", "years", + "time", "country_code" + ) + ) %>% + fill(indicator) %>% + spread(geo, values) %>% + mutate( + FRB = FR24, + FRC = FR26 + FR43, + FRD = FR23 + FR25, + FRE = FR22 + FR30, + FRF = FR21 + FR41 + FR42, + FRG = FR51, + FRH = FR52, + FRI = FR53 + FR61 + FR63, + FRJ = FR62 + FR81, + FRK = FR7, + FRL = FR82, + FRM = FR83, + FRY = FRA, + LT02 = LT00 - LT00A, + UKM7 = UKM2 - UKM24, + UKM8 = UKM31 + UKM34 + UKM35 + UKM36, + UKM9 = UKM24 + UKM32 + UKM33 + UKM37 + UKM38, + PL7 = PL11 + PL33, + PL8 = PL3 - PL33, + PL9 = PL12, + IE05 = IE023 + IE024 + IE025, + HU11 = HU101, + HU12 = HU102 + ) %>% + gather(geo, corrected_values, -one_of("indicator", "country_code", "time", "years")) %>% + filter(!is.na(corrected_values)) %>% + filter(geo %in% c(missing_nuts1_2016, missing_nuts2_2016)) + + correct_with_correspondence + } + + for (t in seq_along(unique(correct_with_correspondence$time))) { + this_time <- unique(correct_with_correspondence$time)[t] + to_correct_by_year <- correct_with_correspondence %>% + filter(time == this_time) + if (t == 1) { + corrected_with_correspondence <- correspondence_by_year(to_correct_by_year, this_time) + } else { + tmp <- correspondence_by_year(to_correct_by_year, + this_time = this_time + ) + if (is.null(tmp)) next + if (!nrow(tmp) == 0) { + corrected_with_correspondence <- full_join( + corrected_with_correspondence, tmp, + by = c( + "time", "country_code", "years", "indicator", + "geo", "corrected_values" + ) + ) + } + } # end of else + } # end of loop + + check_unique_values(corrected_with_correspondence) + + incorrectly_labelled_unknown <- tmp3 %>% + filter(!geo %in% c(changed_regions$code16, changed_regions$code13)) + + if (nrow(incorrectly_labelled_unknown) > 0) { + message("The following labels do not conform the NUTS2016 definition: ", paste( + unique(incorrectly_labelled_unknown$geo), + collapse = "," + )) + warning("Unknown labels found") + } + + check_unique_values(so_far_joined) + + ### If there are duplications, they look like this + dups <- so_far_joined %>% + mutate(change = ifelse(is.na(change), "NA", change)) %>% + select(geo, time, indicator, values, change) %>% + add_count(geo, time, indicator) %>% + filter(n > 1) %>% + spread(change, values) + ## If everything works well, you have two identical values vector, the + ## original Eurostat values and the recoded values. + + names(corrected_with_correspondence) + + corrected_dataset <- so_far_joined %>% + full_join(corrected_with_correspondence, + by = c("geo", "time", "country_code", "years", "indicator") + ) %>% + fill(-one_of(c( + "geo", "time", "country_code", "years", "country", + "indicator", "change", "code16", "resolution", + "problem_code", "corrected_values", "values" + ))) %>% + add_count(geo, time, indicator) + + duplications <- corrected_dataset %>% + filter(n > 1) + + if (any(corrected_dataset$n > 1)) { + message("Duplications occured, probably because of the relabelling.") + } + + corrected_dataset2 <- corrected_dataset %>% + select(-n) %>% + ungroup() %>% + filter(!is.na(change)) %>% + distinct(geo, time, indicator, values, .keep_all = TRUE) %>% + add_count(geo, time, indicator) + + if (any(corrected_dataset2$n > 1)) { + # If the problem was only relabelling, you never end up here. + # You can add here further exception handling code if you want. + corrected_dataset2 <- corrected_dataset2 %>% rowid_to_column() + + duplications <- corrected_dataset2 %>% filter(n > 1) + log_file_name <- paste0("log-", unique(duplications$indicator)[1], "-", Sys.Date(), ".rds") + saveRDS(object = duplications, file = log_file_name) + + kept <- duplications %>% filter(is.na(change)) + not_kept <- duplications %>% filter(!is.na(change)) + + corrected_dataset2 <- corrected_dataset2 %>% + filter(!rowid %in% not_kept$rowid) + + corrected_dataset3 <- corrected_dataset2 %>% + select(-n) %>% + add_count(geo, time, indicator) %>% + filter(n > 1) + nrow(corrected_dataset3) + + corrected_dataset2 <- select(corrected_dataset2, -one_of("n", "rowid")) + warning("The duplications could not be resolved fully, please review manually ", log_file_name, ".") + } else { + message("The duplications were resolved successfully.") + corrected_dataset2 <- select(corrected_dataset2, -one_of("n")) + } + + message("Corrections: ", paste( + c(missing_nuts1_2016, missing_nuts2_2016)[c(missing_nuts1_2016, missing_nuts2_2016) %in% corrected_dataset$geo], + collapse = ", " + )) + + corrected_dataset2 +} + + +check_spread <- function(dat) { + dat %>% + filter(indicator == unique(indicator)[1]) %>% + select(geo, time, indicator, values) %>% + spread(geo, values) +} + +source("fill_forecast.R") +safely_check_spread <- purrr::safely(check_spread) +safely_fill_forecast <- purrr::safely(fill_forecast) +safely_fill_data_source <- purrr::safely(fill_data_source) +i <- 2 +for (i in 1:length(regdata_files)) { + message(i, ":", regdata_files[i]) + + this_data <- readRDS(file.path("regdata", regdata_files[i])) + corrected <- correct_nuts_labelling(this_data) + test_results <- safely_check_spread(corrected) + + if (!is.null(test_results$error)) { + warning("there is a problem with ", i, ": ", regdata_files[i]) + saveRDS(corrected, file.path("corrected", paste0("problems_", regdata_files[i]))) + } else { + message("The data can be spread to wide format, saving...") + saveRDS(corrected, file.path("corrected", paste0("corrected_", regdata_files[i]))) + } + + try_forecast <- safely_fill_data_source(corrected) +} diff --git a/data/ea_countries.rda b/data/ea_countries.rda old mode 100755 new mode 100644 index ea53a63c..76ecea1d Binary files a/data/ea_countries.rda and b/data/ea_countries.rda differ diff --git a/data/efta_countries.rda b/data/efta_countries.rda old mode 100755 new mode 100644 index 84e3c9d0..f4c0124c Binary files a/data/efta_countries.rda and b/data/efta_countries.rda differ diff --git a/data/eu_candidate_countries.rda b/data/eu_candidate_countries.rda old mode 100755 new mode 100644 index 3d5732cb..4c790ec4 Binary files a/data/eu_candidate_countries.rda and b/data/eu_candidate_countries.rda differ diff --git a/data/eu_countries.rda b/data/eu_countries.rda old mode 100755 new mode 100644 index dd30d3f2..b9305db0 Binary files a/data/eu_countries.rda and b/data/eu_countries.rda differ diff --git a/data/eurostat_geodata_60_2016.rda b/data/eurostat_geodata_60_2016.rda old mode 100755 new mode 100644 index e33288f4..0e453269 Binary files a/data/eurostat_geodata_60_2016.rda and b/data/eurostat_geodata_60_2016.rda differ diff --git a/data/tgs00026.rda b/data/tgs00026.rda old mode 100755 new mode 100644 index 26e9f154..3b1cfa40 Binary files a/data/tgs00026.rda and b/data/tgs00026.rda differ diff --git a/docs/LICENSE b/docs/LICENSE deleted file mode 100755 index 34ae01c5..00000000 --- a/docs/LICENSE +++ /dev/null @@ -1,2 +0,0 @@ -YEAR: 2014-2017 -COPYRIGHT HOLDER: Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html deleted file mode 100755 index 6e43b68d..00000000 --- a/docs/LICENSE-text.html +++ /dev/null @@ -1,146 +0,0 @@ - - - - - - - - -License • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for the publication: Retrieval and Analysis of Eurostat Open Data with the eurostat Package Leo Lahti, Janne Huovari, Markus Kainu and Przemysław Biecek. R Journal 9(1), 385-392, 2017. https://journal.r-project.org/archive/2017/RJ-2017-019/index.html

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Cheat sheet: eurostat R package

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Tutorial (vignette) for the eurostat R package

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2018-10-16

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-R Tools for Eurostat Open Data

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This rOpenGov R package provides tools to access Eurostat database, which you can also browse on-line for the data sets and documentation. For contact information and source code, see the package website.

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-Installation

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Release version (CRAN):

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install.packages("eurostat")
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Development version (Github):

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library(devtools)
-install_github("ropengov/eurostat")
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Overall, the eurostat package includes the following functions:

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clean_eurostat_cache    Clean Eurostat Cache
-cut_to_classes          Cuts the Values Column into Classes and
-                        Polishes the Labels
-dic_order               Order of Variable Levels from Eurostat
-                        Dictionary.
-eu_countries            Countries and Country Codes
-eurostat-package        R Tools for Eurostat open data
-eurostat_geodata_60_2016
-                        Geospatial data of Europe from Gisco in 1:60
-                        million scale from year 2016
-eurotime2date           Date Conversion from Eurostat Time Format
-eurotime2num            Conversion of Eurostat Time Format to Numeric
-get_eurostat            Read Eurostat Data
-get_eurostat_dic        Download Eurostat Dictionary
-get_eurostat_geospatial
-                        Download Geospatial Data from GISGO
-get_eurostat_json       Get Data from Eurostat API in JSON
-get_eurostat_raw        Download Data from Eurostat Database
-get_eurostat_toc        Download Table of Contents of Eurostat Data
-                        Sets
-harmonize_country_code
-                        Harmonize Country Code
-label_eurostat          Get Eurostat Codes
-search_eurostat         Grep Datasets Titles from Eurostat
-tgs00026                Auxiliary Data
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-Finding data

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Function get_eurostat_toc() downloads a table of contents of eurostat datasets. The values in column ‘code’ should be used to download a selected dataset.

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# Load the package
-library(eurostat)
-library(rvest)
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-# Get Eurostat data listing
-toc <- get_eurostat_toc()
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-# Check the first items
-library(knitr)
-kable(head(toc))
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titlecodetypelast update of datalast table structure changedata startdata endvalues
Database by themesdatafolderNANANANANA
General and regional statisticsgeneralfolderNANANANANA
European and national indicators for short-term analysiseuroindfolderNANANANANA
Business and consumer surveys (source: DG ECFIN)ei_bcsfolderNANANANANA
Consumer surveys (source: DG ECFIN)ei_bcs_csfolderNANANANANA
Consumers - monthly dataei_bsco_mdataset27.09.201827.09.20181980M012018M09NA
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With search_eurostat() you can search the table of contents for particular patterns, e.g. all datasets related to passenger transport. The kable function to produces nice markdown output. Note that with the type argument of this function you could restrict the search to for instance datasets or tables.

-
# info about passengers
-kable(head(search_eurostat("passenger transport")))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
titlecodetypelast update of datalast table structure changedata startdata endvalues
Volume of passenger transport relative to GDPtran_hv_pstradataset30.07.201830.07.201820002016NA
Modal split of passenger transporttran_hv_psmoddataset27.08.201827.08.201819902016NA
Railway transport - total annual passenger transport (1 000 pass., million pkm)rail_pa_totaldataset20.09.201807.09.201820042016NA
Railway transport - passenger transport by type of transport (detailed reporting only) (1 000 pass.)rail_pa_typepasdataset20.09.201831.08.201720042016NA
Railway transport - passenger transport by type of transport (detailed reporting only) (million pkm)rail_pa_typepkmdataset20.09.201831.08.201720042016NA
International railway passenger transport from the reporting country to the country of disembarkation (1 000 passengers)rail_pa_intgongdataset20.09.201817.07.201820042017NA
-

Codes for the dataset can be searched also from the Eurostat database. The Eurostat database gives codes in the Data Navigation Tree after every dataset in parenthesis.

-
-
-

-Downloading data

-

The package supports two of the Eurostats download methods: the bulk download facility and the Web Services’ JSON API. The bulk download facility is the fastest method to download whole datasets. It is also often the only way as the JSON API has limitation of maximum 50 sub-indicators at a time and whole datasets usually exceeds that. To download only a small section of the dataset the JSON API is faster, as it allows to make a data selection before downloading.

-

A user does not usually have to bother with methods, as both are used via main function get_eurostat(). If only the table id is given, the whole table is downloaded from the bulk download facility. If also filters are defined the JSON API is used.

-

Here an example of indicator ‘Modal split of passenger transport’. This is the percentage share of each mode of transport in total inland transport, expressed in passenger-kilometres (pkm) based on transport by passenger cars, buses and coaches, and trains. All data should be based on movements on national territory, regardless of the nationality of the vehicle. However, the data collection is not harmonized at the EU level.

-

Pick and print the id of the data set to download:

-
# For the original data, see
-# http://ec.europa.eu/eurostat/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tsdtr210
-id <- search_eurostat("Modal split of passenger transport", 
-                         type = "table")$code[1]
-print(id)
-

[1] “t2020_rk310”

-

Get the whole corresponding table. As the table is annual data, it is more convient to use a numeric time variable than use the default date format:

-
dat <- get_eurostat(id, time_format = "num")
-

Investigate the structure of the downloaded data set:

-
str(dat)
-
## Classes 'tbl_df', 'tbl' and 'data.frame':    2536 obs. of  5 variables:
-##  $ unit   : Factor w/ 1 level "PC": 1 1 1 1 1 1 1 1 1 1 ...
-##  $ vehicle: Factor w/ 3 levels "BUS_TOT","CAR",..: 1 1 1 1 1 1 1 1 1 1 ...
-##  $ geo    : Factor w/ 35 levels "AT","BE","CH",..: 1 2 3 4 5 6 7 8 9 10 ...
-##  $ time   : num  1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
-##  $ values : num  11 10.6 3.7 9.1 11.3 32.4 14.9 13.5 6 24.8 ...
-
kable(head(dat))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unitvehiclegeotimevalues
PCBUS_TOTAT199011.0
PCBUS_TOTBE199010.6
PCBUS_TOTCH19903.7
PCBUS_TOTDE19909.1
PCBUS_TOTDK199011.3
PCBUS_TOTEL199032.4
-

Or you can get only a part of the dataset by defining filters argument. It should be named list, where names corresponds to variable names (lower case) and values are vectors of codes corresponding desidered series (upper case). For time variable, in addition to a time, also a sinceTimePeriod and a lastTimePeriod can be used.

-
dat2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), lastTimePeriod=1), time_format = "num")
-kable(dat2)
-
-

-Replacing codes with labels

-

By default variables are returned as Eurostat codes, but to get human-readable labels instead, use a type = "label" argument.

-
datl2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), 
-                                         lastTimePeriod = 1), 
-                      type = "label", time_format = "num")
-kable(head(datl2))
-

Eurostat codes in the downloaded data set can be replaced with human-readable labels from the Eurostat dictionaries with the label_eurostat() function.

-
datl <- label_eurostat(dat)
-kable(head(datl))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unitvehiclegeotimevalues
PercentageMotor coaches, buses and trolley busesAustria199011.0
PercentageMotor coaches, buses and trolley busesBelgium199010.6
PercentageMotor coaches, buses and trolley busesSwitzerland19903.7
PercentageMotor coaches, buses and trolley busesGermany (until 1990 former territory of the FRG)19909.1
PercentageMotor coaches, buses and trolley busesDenmark199011.3
PercentageMotor coaches, buses and trolley busesGreece199032.4
-

The label_eurostat() allows conversion of individual variable vectors or variable names as well.

-
label_eurostat_vars(names(datl))
-

Vehicle information has 3 levels. You can check them now with:

-
levels(datl$vehicle)
-
-
-
-

-Selecting and modifying data

-
-

-EFTA, Eurozone, EU and EU candidate countries

-

To facilitate smooth visualization of standard European geographic areas, the package provides ready-made lists of the country codes used in the eurostat database for EFTA (efta_countries), Euro area (ea_countries), EU (eu_countries) and EU candidate countries (eu_candidate_countries). These can be used to select specific groups of countries for closer investigation. For conversions with other standard country coding systems, see the countrycode R package. To retrieve the country code list for EFTA, for instance, use:

-
data(efta_countries)
-kable(efta_countries)
- - - - - - - - - - - - - - - - - - - - - - - -
codename
ISIceland
LILiechtenstein
NONorway
CHSwitzerland
-
-
-

-EU data from 2012 in all vehicles:

-
dat_eu12 <- subset(datl, geo == "European Union (current composition)" & time == 2012)
-kable(dat_eu12, row.names = FALSE)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unitvehiclegeotimevalues
PercentageMotor coaches, buses and trolley busesEuropean Union (current composition)20129.5
PercentagePassenger carsEuropean Union (current composition)201282.9
PercentageTrainsEuropean Union (current composition)20127.7
-
-
-

-EU data from 2000 - 2012 with vehicle types as variables:

-

Reshaping the data is best done with spread() in tidyr.

-
library("tidyr")
-dat_eu_0012 <- subset(dat, geo == "EU28" & time %in% 2000:2012)
-dat_eu_0012_wide <- spread(dat_eu_0012, vehicle, values)
-kable(subset(dat_eu_0012_wide, select = -geo), row.names = FALSE)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unittimeBUS_TOTCARTRN
PC200010.482.47.2
PC200110.282.77.1
PC20029.983.36.8
PC20039.983.56.7
PC20049.883.46.8
PC20059.883.36.9
PC20069.783.27.1
PC20079.883.17.1
PC20089.982.87.4
PC20099.383.67.1
PC20109.483.47.2
PC20119.483.27.4
PC20129.582.97.7
-
-
-

-Train passengers for selected EU countries in 2000 - 2012

-
dat_trains <- subset(datl, geo %in% c("Austria", "Belgium", "Finland", "Sweden")
-                     & time %in% 2000:2012 
-                     & vehicle == "Trains")
-
-dat_trains_wide <- spread(dat_trains, geo, values) 
-kable(subset(dat_trains_wide, select = -vehicle), row.names = FALSE)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unittimeAustriaBelgiumFinlandSweden
Percentage20009.76.35.16.8
Percentage20019.76.44.87.1
Percentage20029.76.54.87.1
Percentage20039.56.54.77.0
Percentage20049.47.14.76.8
Percentage20059.86.64.87.1
Percentage200610.06.94.87.6
Percentage200710.07.15.07.9
Percentage200811.17.55.48.6
Percentage200911.17.55.18.7
Percentage201011.07.75.28.7
Percentage201111.37.75.08.7
Percentage201211.87.85.39.1
-
-
-
-

-Visualization

-

Visualizing train passenger data with ggplot2:

-
library(ggplot2)
-p <- ggplot(dat_trains, aes(x = time, y = values, colour = geo)) 
-p <- p + geom_line()
-print(p)
-

-

Triangle plot

-

Triangle plot is handy for visualizing data sets with three variables.

-
library(tidyr)
-library(plotrix)
-library(eurostat)
-library(dplyr)
-library(tidyr)
-
-# All sources of renewable energy are to be grouped into three sets
- dict <- c("Solid biofuels (excluding charcoal)" = "Biofuels",
- "Biogasoline" = "Biofuels",
- "Other liquid biofuels" = "Biofuels",
- "Biodiesels" = "Biofuels",
- "Biogas" = "Biofuels",
- "Hydro power" = "Hydro power",
- "Tide, Wave and Ocean" = "Hydro power",
- "Solar thermal" = "Wind, solar, waste and Other",
- "Geothermal Energy" = "Wind, solar, waste and Other",
- "Solar photovoltaic" = "Wind, solar, waste and Other",
- "Municipal waste (renewable)" = "Wind, solar, waste and Other",
- "Wind power" = "Wind, solar, waste and Other",
- "Bio jet kerosene" = "Wind, solar, waste and Other")
-# Some cleaning of the data is required
- energy3 <- get_eurostat("ten00081") %>%
- label_eurostat(dat) %>%
- filter(time == "2013-01-01",
- product != "Renewable energies") %>%
- mutate(nproduct = dict[as.character(product)], # just three categories
- geo = gsub(geo, pattern=" \\(.*", replacement="")) %>%
- select(nproduct, geo, values) %>%
- group_by(nproduct, geo) %>%
- summarise(svalue = sum(values)) %>%
- group_by(geo) %>%
- mutate(tvalue = sum(svalue),
- svalue = svalue/sum(svalue)) %>%
- filter(tvalue > 1000) %>% # only large countries
- spread(nproduct, svalue)
- 
-# Triangle plot
- par(cex=0.75, mar=c(0,0,0,0))
- positions <- plotrix::triax.plot(as.matrix(energy3[, c(3,5,4)]),
-                     show.grid = TRUE,
-                     label.points= FALSE, point.labels = energy3$geo,
-                     col.axis="gray50", col.grid="gray90",
-                     pch = 19, cex.axis=0.8, cex.ticks=0.7, col="grey50")
-
- # Larger labels
- ind <- which(energy3$geo %in%  c("Norway", "Iceland","Denmark","Estonia", "Turkey", "Italy", "Finland"))
- df <- data.frame(positions$xypos, geo = energy3$geo)
- points(df$x[ind], df$y[ind], cex=2, col="red", pch=19)
- text(df$x[ind], df$y[ind], df$geo[ind], adj = c(0.5,-1), cex=1.5)
-

-
-

-Maps

-
-

-Disposable income of private households by NUTS 2 regions at 1:60mln resolution using tmap

-

The mapping examples below use tmap package.

-
library(dplyr)
-library(eurostat)
-library(sf)
-
## Linking to GEOS 3.6.2, GDAL 2.2.3, proj.4 4.9.3
-
library(tmap)
-
-# Download attribute data from Eurostat
-sp_data <- eurostat::get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% 
-  # subset to have only a single row per geo
-  dplyr::filter(time == 2010, nchar(geo) == 4) %>% 
-  # categorise
-  dplyr::mutate(income = cut_to_classes(values, n = 5))
-
## Table tgs00026 cached at /tmp/RtmpEtdcUo/eurostat/tgs00026_raw_code_FF.rds
-
# Download geospatial data from GISCO
-geodata <- get_eurostat_geospatial(output_class = "sf", resolution = "60", nuts_level = 2, year = 2013)
-
## 
-## COPYRIGHT NOTICE
-## 
-## When data downloaded from this page 
-## <http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units>
-## is used in any printed or electronic publication, 
-## in addition to any other provisions 
-## applicable to the whole Eurostat website, 
-## data source will have to be acknowledged 
-## in the legend of the map and 
-## in the introductory page of the publication 
-## with the following copyright notice:
-## 
-## - EN: (C) EuroGeographics for the administrative boundaries
-## - FR: (C) EuroGeographics pour les limites administratives
-## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen
-## 
-## For publications in languages other than 
-## English, French or German, 
-## the translation of the copyright notice 
-## in the language of the publication shall be used.
-## 
-## If you intend to use the data commercially, 
-## please contact EuroGeographics for 
-## information regarding their licence agreements.
-##
-
## No encoding supplied: defaulting to UTF-8.
-
## 
-## # --------------------------
-## HEADS UP!!
-## 
-## Function now returns the data in 'sf'-class (simple features) 
-## by default which is different 
-## from previous behaviour's 'SpatialPolygonDataFrame'. 
-## 
-## If you prefer either 'SpatialPolygonDataFrame' or 
-## fortified 'data_frame' (for ggplot2::geom_polygon), 
-## please specify it explicitly to 'output_class'-argument!
-## 
-## # --------------------------          
-##
-
# merge with attribute data with geodata
-map_data <- inner_join(geodata, sp_data)
-
## Joining, by = "geo"
-

Construct the map

-
map1 <- tmap::tm_shape(geodata) +
-  tmap::tm_fill("lightgrey") +
-  tmap::tm_shape(map_data) +
-  tmap::tm_grid() +
-  tmap::tm_polygons("income", title = "Disposable household\nincomes in 2010",  
-                    palette = "Oranges")
-print(map1)
-

-

Interactive maps can be generated as well

-
# Interactive
-tmap_mode("view")
-map1
-
-# Set the mode back to normal plotting
-tmap_mode("plot")
-print(map1)
-
-
-

-Disposable income of private households by NUTS 2 regions in Poland with labels at 1:1mln resolution using tmap

-
library(eurostat)
-library(dplyr)
-library(sf)
-library(RColorBrewer)
-
-# Downloading and manipulating the tabular data
-print("Let us focus on year 2014 and NUTS-3 level")
-
## [1] "Let us focus on year 2014 and NUTS-3 level"
-
euro_sf2 <- get_eurostat("tgs00026", time_format = "raw",
-                         stringsAsFactors = FALSE,
-             filter = list(time = "2014")) %>% 
- 
-  # Subset to NUTS-3 level
-  dplyr::filter(grepl("PL",geo)) %>% 
-  # label the single geo column
-  mutate(label = paste0(label_eurostat(.)[["geo"]], "\n", values, "€"),
-         income = cut_to_classes(values))
-
-print("Download geospatial data from GISCO")
-
## [1] "Download geospatial data from GISCO"
-
geodata <- get_eurostat_geospatial(output_class = "sf", resolution = "60", nuts_level = 2, year = 2013)
-
## 
-## COPYRIGHT NOTICE
-## 
-## When data downloaded from this page 
-## <http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units>
-## is used in any printed or electronic publication, 
-## in addition to any other provisions 
-## applicable to the whole Eurostat website, 
-## data source will have to be acknowledged 
-## in the legend of the map and 
-## in the introductory page of the publication 
-## with the following copyright notice:
-## 
-## - EN: (C) EuroGeographics for the administrative boundaries
-## - FR: (C) EuroGeographics pour les limites administratives
-## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen
-## 
-## For publications in languages other than 
-## English, French or German, 
-## the translation of the copyright notice 
-## in the language of the publication shall be used.
-## 
-## If you intend to use the data commercially, 
-## please contact EuroGeographics for 
-## information regarding their licence agreements.
-##
-
## No encoding supplied: defaulting to UTF-8.
-
## 
-## # --------------------------
-## HEADS UP!!
-## 
-## Function now returns the data in 'sf'-class (simple features) 
-## by default which is different 
-## from previous behaviour's 'SpatialPolygonDataFrame'. 
-## 
-## If you prefer either 'SpatialPolygonDataFrame' or 
-## fortified 'data_frame' (for ggplot2::geom_polygon), 
-## please specify it explicitly to 'output_class'-argument!
-## 
-## # --------------------------          
-##
-
# Merge with attribute data with geodata
-map_data <- inner_join(geodata, euro_sf2)
-
## Joining, by = "geo"
-
# plot map
-map2 <- tm_shape(geodata) +
-  tm_fill("lightgrey") +
-  tm_shape(map_data, is.master = TRUE) +
-  tm_polygons("income", title = "Disposable household incomes in 2014",
-              palette = "Oranges", border.col = "white") + 
-  tm_text("NUTS_NAME", just = "center") + 
-  tm_scale_bar()
-map2
-

-
-
-

-Disposable income of private households by NUTS 2 regions at 1:10mln resolution using spplot

-
library(sp)
-library(eurostat)
-library(dplyr)
-library(RColorBrewer)
-dat <- get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% 
-  # subsetting to year 2014 and NUTS-2 level
-  dplyr::filter(time == 2014, nchar(geo) == 4) %>% 
-  # classifying the values the variable
-  dplyr::mutate(cat = cut_to_classes(values))
-
## Reading cache file /tmp/RtmpEtdcUo/eurostat/tgs00026_raw_code_FF.rds
-
## Table  tgs00026  read from cache file:  /tmp/RtmpEtdcUo/eurostat/tgs00026_raw_code_FF.rds
-
# Download geospatial data from GISCO
-geodata <- get_eurostat_geospatial(output_class = "spdf", resolution = "10", nuts_level = 2, year = 2013)
-
## 
-## COPYRIGHT NOTICE
-## 
-## When data downloaded from this page 
-## <http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units>
-## is used in any printed or electronic publication, 
-## in addition to any other provisions 
-## applicable to the whole Eurostat website, 
-## data source will have to be acknowledged 
-## in the legend of the map and 
-## in the introductory page of the publication 
-## with the following copyright notice:
-## 
-## - EN: (C) EuroGeographics for the administrative boundaries
-## - FR: (C) EuroGeographics pour les limites administratives
-## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen
-## 
-## For publications in languages other than 
-## English, French or German, 
-## the translation of the copyright notice 
-## in the language of the publication shall be used.
-## 
-## If you intend to use the data commercially, 
-## please contact EuroGeographics for 
-## information regarding their licence agreements.
-##
-
## No encoding supplied: defaulting to UTF-8.
-
## SpatialPolygonDataFrame at resolution 1: 10  cached at:  /tmp/RtmpEtdcUo/eurostat/spdf1022013.RData
-
## 
-## # --------------------------
-## HEADS UP!!
-## 
-## Function now returns the data in 'sf'-class (simple features) 
-## by default which is different 
-## from previous behaviour's 'SpatialPolygonDataFrame'. 
-## 
-## If you prefer either 'SpatialPolygonDataFrame' or 
-## fortified 'data_frame' (for ggplot2::geom_polygon), 
-## please specify it explicitly to 'output_class'-argument!
-## 
-## # --------------------------          
-##
-
# merge with attribute data with geodata
-geodata@data <- left_join(geodata@data, dat)
-
## Joining, by = "geo"
-
# plot map
-sp::spplot(obj = geodata, "cat", main = "Disposable household income",
-       xlim = c(-22,34), ylim = c(35,70), 
-           col.regions = c("dim grey", brewer.pal(n = 5, name = "Oranges")),
-       col = "white", usePolypath = FALSE)
-

-
-
-

-Disposable income of private households by NUTS 2 regions at 1:60mln resolution using ggplot2

-

Meanwhile the CRAN version of ggplot2 is lacking support for simple features, you can plot maps with ggplot2 by downloading geospatial data as data.frame with output_class argument set as df.

-
library(eurostat)
-library(dplyr)
-library(ggplot2)
-dat <- get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% 
-  # subsetting to year 2014 and NUTS-2 level
-  dplyr::filter(time == 2014, nchar(geo) == 4) %>% 
-  # classifying the values the variable
-  dplyr::mutate(cat = cut_to_classes(values))
-
## Reading cache file /tmp/RtmpEtdcUo/eurostat/tgs00026_raw_code_FF.rds
-
## Table  tgs00026  read from cache file:  /tmp/RtmpEtdcUo/eurostat/tgs00026_raw_code_FF.rds
-
# Download geospatial data from GISCO
-geodata <- get_eurostat_geospatial(resolution = "60", nuts_level = "2", year = 2013)
-
## 
-## COPYRIGHT NOTICE
-## 
-## When data downloaded from this page 
-## <http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units>
-## is used in any printed or electronic publication, 
-## in addition to any other provisions 
-## applicable to the whole Eurostat website, 
-## data source will have to be acknowledged 
-## in the legend of the map and 
-## in the introductory page of the publication 
-## with the following copyright notice:
-## 
-## - EN: (C) EuroGeographics for the administrative boundaries
-## - FR: (C) EuroGeographics pour les limites administratives
-## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen
-## 
-## For publications in languages other than 
-## English, French or German, 
-## the translation of the copyright notice 
-## in the language of the publication shall be used.
-## 
-## If you intend to use the data commercially, 
-## please contact EuroGeographics for 
-## information regarding their licence agreements.
-##
-
## No encoding supplied: defaulting to UTF-8.
-
## 
-## # --------------------------
-## HEADS UP!!
-## 
-## Function now returns the data in 'sf'-class (simple features) 
-## by default which is different 
-## from previous behaviour's 'SpatialPolygonDataFrame'. 
-## 
-## If you prefer either 'SpatialPolygonDataFrame' or 
-## fortified 'data_frame' (for ggplot2::geom_polygon), 
-## please specify it explicitly to 'output_class'-argument!
-## 
-## # --------------------------          
-##
-
# merge with attribute data with geodata
-map_data <- inner_join(geodata, dat)
-
## Joining, by = "geo"
-
ggplot(data=map_data) + geom_sf(aes(fill=cat),color="dim grey", size=.1) + 
-    scale_fill_brewer(palette = "Oranges") +
-  guides(fill = guide_legend(reverse=T, title = "euro")) +
-  labs(title="Disposable household income in 2014",
-       caption="(C) EuroGeographics for the administrative boundaries 
-                Map produced in R with a help from Eurostat-package <github.com/ropengov/eurostat/>") +
-  theme_light() + theme(legend.position=c(.8,.8)) +
-  coord_sf(xlim=c(-12,44), ylim=c(35,70))
-

-
-
-
-

-SDMX

-

Eurostat data is available also in the SDMX format. The eurostat R package does not provide custom tools for this but the generic rsdmx and rjsdmx R packages can be used to access data in that format when necessary:

-
library(rsdmx)
-
-# Data set URL
-url <- "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/data/cdh_e_fos/..PC.FOS1.BE/?startperiod=2005&endPeriod=2011"
-
-# Read the data from eurostat
-d <- readSDMX(url)
-
-# Convert to data frame and show the first entries
-df <- as.data.frame(d)
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-kable(head(df))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
UNITY_GRADFOS07GEOFREQobsTimeobsValueOBS_STATUS
PCTOTALFOS1BEA2009NAna
PCTOTALFOS1BEA2006NAna
PCY_GE1990FOS1BEA200943.75NA
PCY_GE1990FOS1BEA2006NAna
-
-
-
-

-Further examples

-

For further examples, see the package homepage.

-
- -
-

-Version info

-

This tutorial was created with

-
sessionInfo()
-
## R version 3.5.1 (2018-07-02)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 18.04.1 LTS
-## 
-## Matrix products: default
-## BLAS: /home/lei/bin/R-3.5.1/lib/libRblas.so
-## LAPACK: /home/lei/bin/R-3.5.1/lib/libRlapack.so
-## 
-## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
-##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
-##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
-## 
-## attached base packages:
-## [1] stats     graphics  grDevices utils     datasets  methods   base     
-## 
-## other attached packages:
-##  [1] rsdmx_0.5-13       sp_1.3-1           RColorBrewer_1.1-2
-##  [4] tmap_2.1-1         sf_0.6-3           dplyr_0.7.6       
-##  [7] plotrix_3.7-4      ggplot2_3.0.0      tidyr_0.8.1       
-## [10] bindrcpp_0.2.2     rvest_0.3.2        xml2_1.2.0        
-## [13] eurostat_3.2.93    knitr_1.20        
-## 
-## loaded via a namespace (and not attached):
-##  [1] httr_1.3.1         viridisLite_0.3.0  jsonlite_1.5      
-##  [4] shiny_1.1.0        assertthat_0.2.0   stats4_3.5.1      
-##  [7] highr_0.7          yaml_2.2.0         pillar_1.3.0      
-## [10] backports_1.1.2    lattice_0.20-35    glue_1.3.0        
-## [13] digest_0.6.18      promises_1.0.1     colorspace_1.3-2  
-## [16] htmltools_0.3.6    httpuv_1.4.5       plyr_1.8.4        
-## [19] XML_3.98-1.16      pkgconfig_2.0.2    raster_2.6-7      
-## [22] broom_0.5.0        purrr_0.2.5        xtable_1.8-3      
-## [25] webshot_0.5.1      scales_1.0.0       satellite_1.0.1   
-## [28] later_0.7.5        tibble_1.4.2       withr_2.1.2       
-## [31] lazyeval_0.2.1     mapview_2.6.0      magrittr_1.5      
-## [34] crayon_1.3.4       mime_0.6           memoise_1.1.0     
-## [37] evaluate_0.12      fs_1.2.6           nlme_3.1-137      
-## [40] MASS_7.3-50        lwgeom_0.1-4       class_7.3-14      
-## [43] Cairo_1.5-9        tools_3.5.1        hms_0.4.2         
-## [46] stringr_1.3.1      munsell_0.5.0      compiler_3.5.1    
-## [49] pkgdown_1.1.0.9000 e1071_1.7-0        rlang_0.2.2       
-## [52] RCurl_1.95-4.11    classInt_0.2-3     units_0.6-1       
-## [55] grid_3.5.1         tmaptools_2.0-1    dichromat_2.0-0   
-## [58] htmlwidgets_1.3    crosstalk_1.0.0    bitops_1.0-6      
-## [61] base64enc_0.1-3    labeling_0.3       rmarkdown_1.10    
-## [64] gtable_0.2.0       curl_3.2           DBI_1.0.0         
-## [67] roxygen2_6.1.0     R6_2.3.0           lubridate_1.7.4   
-## [70] rgdal_1.3-4        rgeos_0.3-28       bindr_0.1.1       
-## [73] commonmark_1.6     rprojroot_1.3-2    KernSmooth_2.23-15
-## [76] readr_1.1.1        desc_1.2.0         stringi_1.2.4     
-## [79] Rcpp_0.12.19       png_0.1-7          leaflet_2.0.2     
-## [82] spData_0.2.9.4     tidyselect_0.2.5
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Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

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Site built with pkgdown.

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- - - - - diff --git a/docs/articles/eurostat_tutorial.md b/docs/articles/eurostat_tutorial.md deleted file mode 100755 index 6ddfdebd..00000000 --- a/docs/articles/eurostat_tutorial.md +++ /dev/null @@ -1,1449 +0,0 @@ -R Tools for Eurostat Open Data -============================== - -This [rOpenGov](http://ropengov.github.io) R package provides tools to -access [Eurostat database](http://ec.europa.eu/eurostat/data/database), -which you can also browse on-line for the data sets and documentation. -For contact information and source code, see the [package -website](http://ropengov.github.io/eurostat/). - -Installation -============ - -Release version -[(CRAN)](https://cran.r-project.org/web/packages/eurostat/index.html): - - install.packages("eurostat") - -Development version [(Github)](https://github.com/rOpenGov/eurostat): - - library(devtools) - install_github("ropengov/eurostat") - -Overall, the eurostat package includes the following functions: - -Finding data -============ - -Function `get_eurostat_toc()` downloads a table of contents of eurostat -datasets. The values in column 'code' should be used to download a -selected dataset. - - # Load the package - library(eurostat) - library(rvest) - - # Get Eurostat data listing - toc <- get_eurostat_toc() - - # Check the first items - library(knitr) - kable(head(toc)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
titlecodetypelast update of datalast table structure changedata startdata endvalues
Database by themesdatafolderNANANANANA
General and regional statisticsgeneralfolderNANANANANA
European and national indicators for short-term analysiseuroindfolderNANANANANA
Business and consumer surveys (source: DG ECFIN)ei_bcsfolderNANANANANA
Consumer surveys (source: DG ECFIN)ei_bcs_csfolderNANANANANA
Consumers - monthly dataei_bsco_mdataset27.03.201827.03.20181980M012018M03NA
- -With `search_eurostat()` you can search the table of contents for -particular patterns, e.g. all datasets related to *passenger transport*. -The kable function to produces nice markdown output. Note that with the -`type` argument of this function you could restrict the search to for -instance datasets or tables. - - # info about passengers - kable(head(search_eurostat("passenger transport"))) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
titlecodetypelast update of datalast table structure changedata startdata endvalues
Volume of passenger transport relative to GDPtran_hv_pstradataset16.08.201714.08.201720002015NA
Modal split of passenger transporttran_hv_psmoddataset17.08.201717.08.201719902015NA
Railway transport - total annual passenger transport (1 000 pass., million pkm)rail_pa_totaldataset27.02.201808.11.201620042015NA
Railway transport - passenger transport by type of transport (detailed reporting only) (1 000 pass.)rail_pa_typepasdataset13.04.201831.08.201720042016NA
Railway transport - passenger transport by type of transport (detailed reporting only) (million pkm)rail_pa_typepkmdataset13.04.201831.08.201720042016NA
International railway passenger transport from the reporting country to the country of disembarkation (1 000 passengers)rail_pa_intgongdataset13.04.201818.07.201720022016NA
- -Codes for the dataset can be searched also from the [Eurostat -database](http://ec.europa.eu/eurostat/data/database). The Eurostat -database gives codes in the Data Navigation Tree after every dataset in -parenthesis. - -Downloading data -================ - -The package supports two of the Eurostats download methods: the bulk -download facility and the Web Services' JSON API. The bulk download -facility is the fastest method to download whole datasets. It is also -often the only way as the JSON API has limitation of maximum 50 -sub-indicators at a time and whole datasets usually exceeds that. To -download only a small section of the dataset the JSON API is faster, as -it allows to make a data selection before downloading. - -A user does not usually have to bother with methods, as both are used -via main function `get_eurostat()`. If only the table id is given, the -whole table is downloaded from the bulk download facility. If also -filters are defined the JSON API is used. - -Here an example of indicator 'Modal split of passenger transport'. This -is the percentage share of each mode of transport in total inland -transport, expressed in passenger-kilometres (pkm) based on transport by -passenger cars, buses and coaches, and trains. All data should be based -on movements on national territory, regardless of the nationality of the -vehicle. However, the data collection is not harmonized at the EU level. - -Pick and print the id of the data set to download: - - # For the original data, see - # http://ec.europa.eu/eurostat/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tsdtr210 - id <- search_eurostat("Modal split of passenger transport", - type = "table")$code[1] - print(id) - -\[1\] "t2020\_rk310" - -Get the whole corresponding table. As the table is annual data, it is -more convient to use a numeric time variable than use the default date -format: - - dat <- get_eurostat(id, time_format = "num") - -Investigate the structure of the downloaded data set: - - str(dat) - - ## Classes 'tbl_df', 'tbl' and 'data.frame': 2431 obs. of 5 variables: - ## $ unit : Factor w/ 1 level "PC": 1 1 1 1 1 1 1 1 1 1 ... - ## $ vehicle: Factor w/ 3 levels "BUS_TOT","CAR",..: 1 1 1 1 1 1 1 1 1 1 ... - ## $ geo : Factor w/ 35 levels "AT","BE","CH",..: 1 2 3 4 5 6 7 8 9 10 ... - ## $ time : num 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ... - ## $ values : num 11 10.6 3.7 9.1 11.3 32.4 14.9 13.5 6 24.8 ... - - kable(head(dat)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unitvehiclegeotimevalues
PCBUS_TOTAT199011.0
PCBUS_TOTBE199010.6
PCBUS_TOTCH19903.7
PCBUS_TOTDE19909.1
PCBUS_TOTDK199011.3
PCBUS_TOTEL199032.4
- -Or you can get only a part of the dataset by defining `filters` -argument. It should be named list, where names corresponds to variable -names (lower case) and values are vectors of codes corresponding -desidered series (upper case). For time variable, in addition to a -`time`, also a `sinceTimePeriod` and a `lastTimePeriod` can be used. - - dat2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), lastTimePeriod=1), time_format = "num") - kable(dat2) - -Replacing codes with labels ---------------------------- - -By default variables are returned as Eurostat codes, but to get -human-readable labels instead, use a `type = "label"` argument. - - datl2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), - lastTimePeriod = 1), - type = "label", time_format = "num") - kable(head(datl2)) - -Eurostat codes in the downloaded data set can be replaced with -human-readable labels from the Eurostat dictionaries with the -`label_eurostat()` function. - - datl <- label_eurostat(dat) - kable(head(datl)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unitvehiclegeotimevalues
PercentageMotor coaches, buses and trolley busesAustria199011.0
PercentageMotor coaches, buses and trolley busesBelgium199010.6
PercentageMotor coaches, buses and trolley busesSwitzerland19903.7
PercentageMotor coaches, buses and trolley busesGermany (until 1990 former territory of the FRG)19909.1
PercentageMotor coaches, buses and trolley busesDenmark199011.3
PercentageMotor coaches, buses and trolley busesGreece199032.4
- -The `label_eurostat()` allows conversion of individual variable vectors -or variable names as well. - - label_eurostat_vars(names(datl)) - -Vehicle information has 3 levels. You can check them now with: - - levels(datl$vehicle) - -Selecting and modifying data -============================ - -EFTA, Eurozone, EU and EU candidate countries ---------------------------------------------- - -To facilitate smooth visualization of standard European geographic -areas, the package provides ready-made lists of the country codes used -in the eurostat database for EFTA (efta\_countries), Euro area -(ea\_countries), EU (eu\_countries) and EU candidate countries -(eu\_candidate\_countries). These can be used to select specific groups -of countries for closer investigation. For conversions with other -standard country coding systems, see the [countrycode](...) R package. -To retrieve the country code list for EFTA, for instance, use: - - data(efta_countries) - kable(efta_countries) - - - - - - - - - - - - - - - - - - - - - - - - - - -
codename
ISIceland
LILiechtenstein
NONorway
CHSwitzerland
- -EU data from 2012 in all vehicles: ----------------------------------- - - dat_eu12 <- subset(datl, geo == "European Union (current composition)" & time == 2012) - kable(dat_eu12, row.names = FALSE) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unitvehiclegeotimevalues
PercentageMotor coaches, buses and trolley busesEuropean Union (current composition)20129.5
PercentagePassenger carsEuropean Union (current composition)201282.8
PercentageTrainsEuropean Union (current composition)20127.7
- -EU data from 2000 - 2012 with vehicle types as variables: ---------------------------------------------------------- - -Reshaping the data is best done with `spread()` in `tidyr`. - - library("tidyr") - dat_eu_0012 <- subset(dat, geo == "EU28" & time %in% 2000:2012) - dat_eu_0012_wide <- spread(dat_eu_0012, vehicle, values) - kable(subset(dat_eu_0012_wide, select = -geo), row.names = FALSE) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unittimeBUS_TOTCARTRN
PC200010.482.47.2
PC200110.282.77.1
PC20029.983.36.8
PC20039.983.56.7
PC20049.883.46.8
PC20059.883.36.9
PC20069.783.27.1
PC20079.883.17.1
PC20089.982.87.4
PC20099.383.67.1
PC20109.483.57.2
PC20119.483.27.3
PC20129.582.87.7
- -Train passengers for selected EU countries in 2000 - 2012 ---------------------------------------------------------- - - dat_trains <- subset(datl, geo %in% c("Austria", "Belgium", "Finland", "Sweden") - & time %in% 2000:2012 - & vehicle == "Trains") - - dat_trains_wide <- spread(dat_trains, geo, values) - kable(subset(dat_trains_wide, select = -vehicle), row.names = FALSE) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unittimeAustriaBelgiumFinlandSweden
Percentage20009.76.35.16.8
Percentage20019.76.44.87.1
Percentage20029.76.54.87.1
Percentage20039.56.54.77.0
Percentage20049.47.14.76.8
Percentage20059.86.64.87.1
Percentage200610.06.94.87.6
Percentage200710.07.15.07.9
Percentage200811.17.55.48.6
Percentage200911.17.55.18.7
Percentage201011.07.75.28.7
Percentage201111.37.75.08.7
Percentage201211.87.85.39.1
- -Visualization -============= - -Visualizing train passenger data with `ggplot2`: - - library(ggplot2) - - ## Want to understand how all the pieces fit together? See the R for - ## Data Science book: http://r4ds.had.co.nz/ - - p <- ggplot(dat_trains, aes(x = time, y = values, colour = geo)) - p <- p + geom_line() - print(p) - -![](fig/trains_plot-1.png) - -**Triangle plot** - -Triangle plot is handy for visualizing data sets with three variables. - - library(tidyr) - library(plotrix) - library(eurostat) - library(dplyr) - library(tidyr) - - # All sources of renewable energy are to be grouped into three sets - dict <- c("Solid biofuels (excluding charcoal)" = "Biofuels", - "Biogasoline" = "Biofuels", - "Other liquid biofuels" = "Biofuels", - "Biodiesels" = "Biofuels", - "Biogas" = "Biofuels", - "Hydro power" = "Hydro power", - "Tide, Wave and Ocean" = "Hydro power", - "Solar thermal" = "Wind, solar, waste and Other", - "Geothermal Energy" = "Wind, solar, waste and Other", - "Solar photovoltaic" = "Wind, solar, waste and Other", - "Municipal waste (renewable)" = "Wind, solar, waste and Other", - "Wind power" = "Wind, solar, waste and Other", - "Bio jet kerosene" = "Wind, solar, waste and Other") - # Some cleaning of the data is required - energy3 <- get_eurostat("ten00081") %>% - label_eurostat(dat) %>% - filter(time == "2013-01-01", - product != "Renewable energies") %>% - mutate(nproduct = dict[as.character(product)], # just three categories - geo = gsub(geo, pattern=" \\(.*", replacement="")) %>% - select(nproduct, geo, values) %>% - group_by(nproduct, geo) %>% - summarise(svalue = sum(values)) %>% - group_by(geo) %>% - mutate(tvalue = sum(svalue), - svalue = svalue/sum(svalue)) %>% - filter(tvalue > 1000) %>% # only large countries - spread(nproduct, svalue) - - # Triangle plot - par(cex=0.75, mar=c(0,0,0,0)) - positions <- plotrix::triax.plot(as.matrix(energy3[, c(3,5,4)]), - show.grid = TRUE, - label.points= FALSE, point.labels = energy3$geo, - col.axis="gray50", col.grid="gray90", - pch = 19, cex.axis=0.8, cex.ticks=0.7, col="grey50") - - # Larger labels - ind <- which(energy3$geo %in% c("Norway", "Iceland","Denmark","Estonia", "Turkey", "Italy", "Finland")) - df <- data.frame(positions$xypos, geo = energy3$geo) - points(df$x[ind], df$y[ind], cex=2, col="red", pch=19) - text(df$x[ind], df$y[ind], df$geo[ind], adj = c(0.5,-1), cex=1.5) - -![](fig/plotGallery-1.png) - -Maps ----- - -### Disposable income of private households by NUTS 2 regions at 1:60mln resolution using tmap - -The mapping examples below use -[`tmap`](https://github.com/mtennekes/tmap) package. - - library(dplyr) - library(eurostat) - library(sf) - - ## Linking to GEOS 3.5.1, GDAL 2.2.1, proj.4 4.9.3 - - library(tmap) - - # Download attribute data from Eurostat - sp_data <- eurostat::get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subset to have only a single row per geo - dplyr::filter(time == 2010, nchar(geo) == 4) %>% - # categorise - dplyr::mutate(income = cut_to_classes(values, n = 5)) - - ## Table tgs00026 cached at /tmp/RtmpCypIG8/eurostat/tgs00026_raw_code_FF.rds - - # Download geospatial data from CISGO - geodata <- get_eurostat_geospatial(output_class = "sf", resolution = "60") - - ## - ## COPYRIGHT NOTICE - ## - ## When data downloaded from this page - ## - ## is used in any printed or electronic publication, - ## in addition to any other provisions - ## applicable to the whole Eurostat website, - ## data source will have to be acknowledged - ## in the legend of the map and - ## in the introductory page of the publication - ## with the following copyright notice: - ## - ## - EN: (C) EuroGeographics for the administrative boundaries - ## - FR: (C) EuroGeographics pour les limites administratives - ## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen - ## - ## For publications in languages other than - ## English, French or German, - ## the translation of the copyright notice - ## in the language of the publication shall be used. - ## - ## If you intend to use the data commercially, - ## please contact EuroGeographics for - ## information regarding their licence agreements. - ## - - ## sf at resolution 1:60 read from local file - - ## - ## # -------------------------- - ## HEADS UP!! - ## - ## Function now returns the data in 'sf'-class (simple features) - ## by default which is different - ## from previous behaviour's 'SpatialPolygonDataFrame'. - ## - ## If you prefer either 'SpatialPolygonDataFrame' or - ## fortified 'data_frame' (for ggplot2::geom_polygon), - ## please specify it explicitly to 'output_class'-argument! - ## - ## # -------------------------- - ## - - # merge with attribute data with geodata - map_data <- inner_join(geodata, sp_data) - - ## Joining, by = "geo" - - # plot map using tmap - data(Europe) - -Construct the map - - map1 <- tmap::tm_shape(Europe) + - tmap::tm_fill("lightgrey") + - tmap::tm_shape(map_data) + - tmap::tm_grid() + - tmap::tm_polygons("income", title = "Disposable household\nincomes in 2010", - palette = "Oranges") + - tmap::tm_format_Europe() - print(map1) - -![](fig/map1ex-1.png) - -Interactive maps can be generated as well - - # Interactive - tmap_mode("view") - map1 - - # Set the mode back to normal plotting - tmap_mode("plot") - print(map1) - -### Disposable income of private households by NUTS 2 regions in Poland with labels at 1:1mln resolution using tmap - - library(eurostat) - library(dplyr) - library(sf) - library(RColorBrewer) - - # Downloading and manipulating the tabular data - euro_sf2 <- eurostat::get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subsetting to year 2014 and NUTS-3 level - dplyr::filter(time == 2014, grepl("PL",geo)) %>% - # label the single geo column - mutate(label = paste0(label_eurostat(.)[["geo"]], "\n", values, "€"), - income = cut_to_classes(values)) - - ## Reading cache file /tmp/RtmpCypIG8/eurostat/tgs00026_raw_code_FF.rds - - ## Table tgs00026 read from cache file: /tmp/RtmpCypIG8/eurostat/tgs00026_raw_code_FF.rds - - # Download geospatial data from CISGO - geodata <- get_eurostat_geospatial(output_class = "sf", resolution = "60") - - ## - ## COPYRIGHT NOTICE - ## - ## When data downloaded from this page - ## - ## is used in any printed or electronic publication, - ## in addition to any other provisions - ## applicable to the whole Eurostat website, - ## data source will have to be acknowledged - ## in the legend of the map and - ## in the introductory page of the publication - ## with the following copyright notice: - ## - ## - EN: (C) EuroGeographics for the administrative boundaries - ## - FR: (C) EuroGeographics pour les limites administratives - ## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen - ## - ## For publications in languages other than - ## English, French or German, - ## the translation of the copyright notice - ## in the language of the publication shall be used. - ## - ## If you intend to use the data commercially, - ## please contact EuroGeographics for - ## information regarding their licence agreements. - ## - - ## sf at resolution 1:60 read from local file - - ## - ## # -------------------------- - ## HEADS UP!! - ## - ## Function now returns the data in 'sf'-class (simple features) - ## by default which is different - ## from previous behaviour's 'SpatialPolygonDataFrame'. - ## - ## If you prefer either 'SpatialPolygonDataFrame' or - ## fortified 'data_frame' (for ggplot2::geom_polygon), - ## please specify it explicitly to 'output_class'-argument! - ## - ## # -------------------------- - ## - - # merge with attribute data with geodata - map_data <- inner_join(geodata, euro_sf2) - - ## Joining, by = "geo" - - # plot map - map2 <- tm_shape(Europe) + - tm_fill("lightgrey") + - tm_shape(map_data, is.master = TRUE) + - tm_polygons("income", title = "Disposable household incomes in 2014", - palette = "Oranges", border.col = "white") + - tm_text("NUTS_NAME", just = "center") + - tm_scale_bar() + - tm_format_Europe(legend.outside = TRUE, attr.outside = TRUE) - map2 - -![](fig/maps2-1.png) - -### Disposable income of private households by NUTS 2 regions at 1:10mln resolution using spplot - - library(sp) - library(eurostat) - library(dplyr) - library(RColorBrewer) - dat <- get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subsetting to year 2014 and NUTS-3 level - dplyr::filter(time == 2014, nchar(geo) == 4) %>% - # classifying the values the variable - dplyr::mutate(cat = cut_to_classes(values)) - - ## Reading cache file /tmp/RtmpCypIG8/eurostat/tgs00026_raw_code_FF.rds - - ## Table tgs00026 read from cache file: /tmp/RtmpCypIG8/eurostat/tgs00026_raw_code_FF.rds - - # Download geospatial data from CISGO - geodata <- get_eurostat_geospatial(output_class = "spdf", resolution = "10", nuts_level = 2) - - ## - ## COPYRIGHT NOTICE - ## - ## When data downloaded from this page - ## - ## is used in any printed or electronic publication, - ## in addition to any other provisions - ## applicable to the whole Eurostat website, - ## data source will have to be acknowledged - ## in the legend of the map and - ## in the introductory page of the publication - ## with the following copyright notice: - ## - ## - EN: (C) EuroGeographics for the administrative boundaries - ## - FR: (C) EuroGeographics pour les limites administratives - ## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen - ## - ## For publications in languages other than - ## English, French or German, - ## the translation of the copyright notice - ## in the language of the publication shall be used. - ## - ## If you intend to use the data commercially, - ## please contact EuroGeographics for - ## information regarding their licence agreements. - ## - - ## No encoding supplied: defaulting to UTF-8. - - ## SpatialPolygonDataFrame at resolution 1: 10 cached at: /tmp/RtmpCypIG8/eurostat/spdf102.RData - - ## - ## # -------------------------- - ## HEADS UP!! - ## - ## Function now returns the data in 'sf'-class (simple features) - ## by default which is different - ## from previous behaviour's 'SpatialPolygonDataFrame'. - ## - ## If you prefer either 'SpatialPolygonDataFrame' or - ## fortified 'data_frame' (for ggplot2::geom_polygon), - ## please specify it explicitly to 'output_class'-argument! - ## - ## # -------------------------- - ## - - # merge with attribute data with geodata - geodata@data <- left_join(geodata@data, dat) - - ## Joining, by = "geo" - - # plot map - sp::spplot(obj = geodata, "cat", main = "Disposable household income", - xlim = c(-22,34), ylim = c(35,70), - col.regions = c("dim grey", brewer.pal(n = 5, name = "Oranges")), - col = "white", usePolypath = FALSE) - -![](fig/maps3-1.png) - -### Disposable income of private households by NUTS 2 regions at 1:60mln resolution using ggplot2 - -Meanwhile the CRAN version of `ggplot2` is lacking support for simple -features, you can plot maps with `ggplot2` by downloading geospatial -data as `data.frame` with `output_class` argument set as `df`. - - library(eurostat) - library(dplyr) - library(ggplot2) - dat <- get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subsetting to year 2014 and NUTS-2 level - dplyr::filter(time == 2014, nchar(geo) == 4) %>% - # classifying the values the variable - dplyr::mutate(cat = cut_to_classes(values)) - - ## Reading cache file /tmp/RtmpCypIG8/eurostat/tgs00026_raw_code_FF.rds - - ## Table tgs00026 read from cache file: /tmp/RtmpCypIG8/eurostat/tgs00026_raw_code_FF.rds - - # Download geospatial data from CISGO - geodata <- get_eurostat_geospatial(output_class = "df", resolution = "60", nuts_level = "2") - - ## - ## COPYRIGHT NOTICE - ## - ## When data downloaded from this page - ## - ## is used in any printed or electronic publication, - ## in addition to any other provisions - ## applicable to the whole Eurostat website, - ## data source will have to be acknowledged - ## in the legend of the map and - ## in the introductory page of the publication - ## with the following copyright notice: - ## - ## - EN: (C) EuroGeographics for the administrative boundaries - ## - FR: (C) EuroGeographics pour les limites administratives - ## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen - ## - ## For publications in languages other than - ## English, French or German, - ## the translation of the copyright notice - ## in the language of the publication shall be used. - ## - ## If you intend to use the data commercially, - ## please contact EuroGeographics for - ## information regarding their licence agreements. - ## - - ## Regions defined for each Polygons - - ## Joining, by = "id" - - ## data_frame at resolution 1:60 read from local file - - ## - ## # -------------------------- - ## HEADS UP!! - ## - ## Function now returns the data in 'sf'-class (simple features) - ## by default which is different - ## from previous behaviour's 'SpatialPolygonDataFrame'. - ## - ## If you prefer either 'SpatialPolygonDataFrame' or - ## fortified 'data_frame' (for ggplot2::geom_polygon), - ## please specify it explicitly to 'output_class'-argument! - ## - ## # -------------------------- - ## - - # merge with attribute data with geodata - map_data <- inner_join(geodata, dat) - - ## Joining, by = "geo" - - # plot map - ggplot(data=map_data, aes(x=long,y=lat,group=group)) + - geom_polygon(aes(fill=cat),color="dim grey", size=.1) + - scale_fill_brewer(palette = "Oranges") + - # scale_fill_continuous(trans = 'reverse', ) + - guides(fill = guide_legend(reverse=T, title = "€")) + - labs(title="Disposable household income in 2014", - caption="(C) EuroGeographics for the administrative boundaries - Map produced in R with a help from Eurostat-package ") + - theme_light() + theme(legend.position=c(.8,.8)) + - coord_map(project="orthographic", xlim=c(-12,44), ylim=c(35,70)) - - ## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font - ## metrics unknown for Unicode character U+20ac - - ## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font - ## metrics unknown for Unicode character U+20ac - - ## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): conversion - ## failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): conversion - ## failure on '€' in 'mbcsToSbcs': dot substituted for <82> - - ## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): conversion - ## failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for <82> - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for <82> - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for <82> - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for <82> - - ## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : - ## conversion failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x - ## $y, : conversion failure on '€' in 'mbcsToSbcs': dot substituted for - - ## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x - ## $y, : conversion failure on '€' in 'mbcsToSbcs': dot substituted for <82> - - ## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x - ## $y, : conversion failure on '€' in 'mbcsToSbcs': dot substituted for - -![](fig/maps4-1.png) - -SDMX ----- - -Eurostat data is available also in the SDMX format. The eurostat R -package does not provide custom tools for this but the generic rsdmx R -package can be used to access data in that format when necessary: - - library(rsdmx) - - # Data set URL - url <- "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/data/cdh_e_fos/..PC.FOS1.BE/?startperiod=2005&endPeriod=2011" - - # Read the data from eurostat - d <- readSDMX(url) - - # Convert to data frame and show the first entries - df <- as.data.frame(d) - - kable(head(df)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
UNITY_GRADFOS07GEOFREQobsTimeobsValueOBS_STATUS
PCTOTALFOS1BEA2009NAna
PCTOTALFOS1BEA2006NAna
PCY_GE1990FOS1BEA200943.75NA
PCY_GE1990FOS1BEA2006NAna
- -Further examples -================ - -For further examples, see the [package -homepage](http://ropengov.github.io/eurostat/articles/index.html). - -Citations and related work -========================== - -### Citing the data sources - -Eurostat data: cite [Eurostat](http://ec.europa.eu/eurostat/). - -Administrative boundaries: cite EuroGeographics - -### Citing the eurostat R package - -For main developers and contributors, see the [package -homepage](http://ropengov.github.io/eurostat). - -This work can be freely used, modified and distributed under the -BSD-2-clause (modified FreeBSD) license: - - citation("eurostat") - - ## - ## Kindly cite the eurostat R package as follows: - ## - ## (C) Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek. - ## Retrieval and analysis of Eurostat open data with the eurostat - ## package. R Journal 9(1):385-392, 2017. Version 3.1.6003 Package - ## URL: http://ropengov.github.io/eurostat Manuscript URL: - ## https://journal.r-project.org/archive/2017/RJ-2017-019/index.html - ## - ## A BibTeX entry for LaTeX users is - ## - ## @Misc{, - ## title = {eurostat R package}, - ## author = {Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek}, - ## journal = {R Journal}, - ## volume = {9}, - ## number = {1}, - ## pages = {385-392}, - ## year = {2017}, - ## url = {https://journal.r-project.org/archive/2017/RJ-2017-019/index.html}, - ## note = {Version 3.1.6003}, - ## } - -### Related work - -This [rOpenGov](http://ropengov.github.io) R package is based on the -earlier CRAN packages -[statfi](https://cran.r-project.org/package=statfi) and -[smarterpoland](https://cran.r-project.org/package=SmarterPoland). - -The independent [reurostat](https://github.com/Tungurahua/reurostat) -package develops related Eurostat tools but seems to be in an -experimental stage at the time of writing this tutorial. - -The more generic [quandl](https://cran.r-project.org/package=quandl), -[datamart](https://cran.r-project.org/package=datamart), -[rsdmx](https://cran.r-project.org/package=rsdmx), and -[pdfetch](https://cran.r-project.org/package=pdfetch) packages may -provide access to some versions of eurostat data but these packages are -more generic and hence, in contrast to the eurostat R package, lack -tools that are specifically customized to facilitate eurostat analysis. - -### Contact - -For contact information, see the [package -homepage](http://ropengov.github.io/eurostat). - -Version info -============ - -This tutorial was created with - - sessionInfo() - - ## R version 3.4.3 (2017-11-30) - ## Platform: x86_64-pc-linux-gnu (64-bit) - ## Running under: Ubuntu 17.10 - ## - ## Matrix products: default - ## BLAS: /home/lei/bin/R-3.4.3/lib/libRblas.so - ## LAPACK: /home/lei/bin/R-3.4.3/lib/libRlapack.so - ## - ## locale: - ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C - ## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 - ## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 - ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C - ## [9] LC_ADDRESS=C LC_TELEPHONE=C - ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C - ## - ## attached base packages: - ## [1] stats graphics grDevices utils datasets methods base - ## - ## other attached packages: - ## [1] rsdmx_0.5-10 sp_1.2-7 RColorBrewer_1.1-2 - ## [4] tmap_1.11-1 sf_0.6-1 dplyr_0.7.4 - ## [7] plotrix_3.7 ggplot2_2.2.1.9000 tidyr_0.8.0 - ## [10] bindrcpp_0.2 rvest_0.3.2 xml2_1.2.0 - ## [13] rmarkdown_1.8 eurostat_3.1.6003 testthat_2.0.0 - ## [16] pkgdown_0.1.0.9000 knitr_1.19 devtools_1.13.4 - ## - ## loaded via a namespace (and not attached): - ## [1] colorspace_1.3-2 deldir_0.1-14 gdalUtils_2.0.1.7 - ## [4] class_7.3-14 leaflet_1.1.0 rgdal_1.2-16 - ## [7] rprojroot_1.3-2 satellite_1.0.1 base64enc_0.1-3 - ## [10] dichromat_2.0-0 roxygen2_6.0.1 R.methodsS3_1.7.1 - ## [13] codetools_0.2-15 splines_3.4.3 mnormt_1.5-5 - ## [16] pkgload_0.0.0.9000 geojsonlint_0.2.0 jsonlite_1.5 - ## [19] Cairo_1.5-9 tmaptools_1.2-3 rematch_1.0.1 - ## [22] broom_0.4.4 png_0.1-7 R.oo_1.21.0 - ## [25] rgeos_0.3-26 shiny_1.0.5 mapproj_1.2-5 - ## [28] readr_1.1.1 compiler_3.4.3 httr_1.3.1 - ## [31] mapview_2.3.0 backports_1.1.2 assertthat_0.2.0 - ## [34] Matrix_1.2-12 lazyeval_0.2.1 htmltools_0.3.6 - ## [37] tools_3.4.3 coda_0.19-1 gtable_0.2.0 - ## [40] glue_1.2.0 reshape2_1.4.3 maps_3.2.0 - ## [43] gmodels_2.16.2 V8_1.5 Rcpp_0.12.16 - ## [46] highlight_0.4.7.2 raster_2.6-7 spdep_0.7-4 - ## [49] gdata_2.18.0 debugme_1.1.0 nlme_3.1-131 - ## [52] udunits2_0.13 iterators_1.0.9 crosstalk_1.0.0 - ## [55] psych_1.8.3.3 stringr_1.3.0 mime_0.5 - ## [58] gtools_3.5.0 XML_3.98-1.9 LearnBayes_2.15 - ## [61] MASS_7.3-48 scales_0.5.0.9000 BiocInstaller_1.28.0 - ## [64] hms_0.4.1 parallel_3.4.3 expm_0.999-2 - ## [67] yaml_2.1.16 curl_3.2 memoise_1.1.0 - ## [70] geosphere_1.5-7 stringi_1.1.7 jsonvalidate_1.0.0 - ## [73] highr_0.6 desc_1.1.1 foreach_1.4.4 - ## [76] e1071_1.6-8 boot_1.3-20 pkgbuild_0.0.0.9000 - ## [79] spData_0.2.8.3 rlang_0.1.6.9003 pkgconfig_2.0.1 - ## [82] commonmark_1.4 bitops_1.0-6 evaluate_0.10.1 - ## [85] lattice_0.20-35 purrr_0.2.4 bindr_0.1 - ## [88] htmlwidgets_1.0 labeling_0.3 tidyselect_0.2.3 - ## [91] osmar_1.1-7 plyr_1.8.4 magrittr_1.5 - ## [94] R6_2.2.2 DBI_0.8 pillar_1.1.0 - ## [97] whisker_0.4 foreign_0.8-69 withr_2.1.1.9000 - ## [100] units_0.5-1 RCurl_1.95-4.10 tibble_1.4.2 - ## [103] crayon_1.3.4 rmapshaper_0.3.0 KernSmooth_2.23-15 - ## [106] grid_3.4.3 callr_2.0.1 webshot_0.5.0.9000 - ## [109] digest_0.6.15 classInt_0.2-3 xtable_1.8-2 - ## [112] httpuv_1.3.5 R.utils_2.6.0 stats4_3.4.3 - ## [115] munsell_0.4.3 viridisLite_0.3.0 diff --git a/docs/articles/eurostat_tutorial.pdf b/docs/articles/eurostat_tutorial.pdf deleted file mode 100755 index a2a7bd93..00000000 Binary files a/docs/articles/eurostat_tutorial.pdf and /dev/null differ diff --git a/docs/articles/index.html b/docs/articles/index.html deleted file mode 100755 index 23cf4b8e..00000000 --- a/docs/articles/index.html +++ /dev/null @@ -1,151 +0,0 @@ - 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Publications using the eurostat R package

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2018-10-16

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You can send us a note via the issue tracker if you like to add a publication using the eurostat R package on this list:

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  • Retrieval and analysis of Eurostat open data with the eurostat package. R Journal 2017. (manuscript; source code).
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External publications using the eurostat package:

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  • Kenett RDS, Shmueli G. Information Quality: The Potential of Data and Analytics to Generate Knowledge. John Wiley & Sons, 2016.
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Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

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Site built with pkgdown.

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Citation

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Lahti L, Huovari J, Kainu M, Biecek P (2017). -“eurostat R package.” -Version 3.2.93, https://journal.r-project.org/archive/2017/RJ-2017-019/index.html. -

- 
@Misc{,
-  title = {eurostat R package},
-  author = {Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek},
-  journal = {R Journal},
-  volume = {9},
-  number = {1},
-  pages = {385-392},
-  year = {2017},
-  url = {https://journal.r-project.org/archive/2017/RJ-2017-019/index.html},
-  note = {Version 3.2.93},
-}
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Authors

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    Leo Lahti. Author, maintainer. -

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    Janne Huovari. Author. -

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    Markus Kainu. Author. -

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    Przemyslaw Biecek. Author. -

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    Joona Lehtomaki. Contributor. -

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    Francois Briatte. Contributor. -

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    Oliver Reiter. Contributor. -

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Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

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Site built with pkgdown.

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- - - - - - diff --git a/docs/docsearch.css b/docs/docsearch.css deleted file mode 100755 index e5f1fe1d..00000000 --- a/docs/docsearch.css +++ /dev/null @@ -1,148 +0,0 @@ -/* Docsearch -------------------------------------------------------------- */ -/* - Source: https://github.com/algolia/docsearch/ - License: MIT -*/ - -.algolia-autocomplete { - display: block; - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; -} - -.algolia-autocomplete .ds-dropdown-menu .ds-suggestions { - margin-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion { - padding: 0; - overflow: visible -} - -.algolia-autocomplete .algolia-docsearch-suggestion--category-header { - padding: .125rem 1rem; - margin-top: 0; - font-size: 1.3em; - font-weight: 500; - color: #00008B; - border-bottom: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--wrapper { - float: none; - padding-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column { - float: none; - width: auto; - padding: 0; - text-align: left -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content { - float: none; - width: auto; - padding: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content::before { - display: none -} - -.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header { - padding-top: .75rem; - margin-top: .75rem; - border-top: 1px solid rgba(0, 0, 0, .1) -} - -.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column { - display: block; - padding: .1rem 1rem; - margin-bottom: 0.1; - font-size: 1.0em; - font-weight: 400 - /* display: none */ -} - -.algolia-autocomplete .algolia-docsearch-suggestion--title { - display: block; - padding: .25rem 1rem; - margin-bottom: 0; - font-size: 0.9em; - font-weight: 400 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--text { - padding: 0 1rem .5rem; - margin-top: -.25rem; - font-size: 0.8em; - font-weight: 400; - line-height: 1.25 -} - -.algolia-autocomplete .algolia-docsearch-footer { - width: 110px; - height: 20px; - z-index: 3; - margin-top: 10.66667px; - float: right; - font-size: 0; - line-height: 0; -} - -.algolia-autocomplete .algolia-docsearch-footer--logo { - background-image: url("data:image/svg+xml;utf8,"); - background-repeat: no-repeat; - background-position: 50%; - background-size: 100%; - overflow: hidden; - text-indent: -9000px; - width: 100%; - height: 100%; - display: block; - transform: translate(-8px); -} - -.algolia-autocomplete .algolia-docsearch-suggestion--highlight { - color: #FF8C00; - background: rgba(232, 189, 54, 0.1) -} - - -.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { - box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) -} - -.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { - background-color: rgba(192, 192, 192, .15) -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100755 index 1343af8e..00000000 --- a/docs/index.html +++ /dev/null @@ -1,201 +0,0 @@ - - - - - - - -Tools for Eurostat Open Data • eurostat - - - - - - - - - -
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license PRs Welcome Code of Conduct

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Build Status AppVeyor Status codecov.io Downloads Downloads

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Gitter Watch on GitHub Star on GitHub Follow

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-eurostat R package

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R tools to access open data from Eurostat. Data search, download, manipulation and visualization.

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-Installation

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To install the CRAN release version, use:

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install.packages("eurostat")
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For the latest github development version, use:

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install_github("ropengov/eurostat")
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-Using the package

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The R Journal manuscript provides an overview of the package. Various tutorials are available and provide more detailed demonstrations. Check the following:

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-Contribute

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Check the Github page for source code.

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Contributions are very welcome:

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-Acknowledgements

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Kindly cite this work as follows: Leo Lahti, Przemyslaw Biecek, Markus Kainu and Janne Huovari. Retrieval and analysis of Eurostat open data with the eurostat package. R Journal 9(1):385-392, 2017). R package version 3.2.93. URL: http://ropengov.github.io/eurostat

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We are greatful to all contributors, including Joona Lehtomäki, Francois Briatte, and Oliver Reiter, and for the Eurostat open data portal! This project is part of rOpenGov.

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-Disclaimer

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This package is in no way officially related to or endorsed by Eurostat.

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Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

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Site built with pkgdown.

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- - - - - diff --git a/docs/jquery.sticky-kit.min.js b/docs/jquery.sticky-kit.min.js deleted file mode 100755 index 1c162717..00000000 --- a/docs/jquery.sticky-kit.min.js +++ /dev/null @@ -1,11 +0,0 @@ -/* Sticky-kit v1.1.2 | WTFPL | Leaf Corcoran 2015 | */ -/* - Source: https://github.com/leafo/sticky-kit - License: MIT -*/ -(function(){var b,f;b=this.jQuery||window.jQuery;f=b(window);b.fn.stick_in_parent=function(d){var A,w,J,n,B,K,p,q,k,E,t;null==d&&(d={});t=d.sticky_class;B=d.inner_scrolling;E=d.recalc_every;k=d.parent;q=d.offset_top;p=d.spacer;w=d.bottoming;null==q&&(q=0);null==k&&(k=void 0);null==B&&(B=!0);null==t&&(t="is_stuck");A=b(document);null==w&&(w=!0);J=function(a,d,n,C,F,u,r,G){var v,H,m,D,I,c,g,x,y,z,h,l;if(!a.data("sticky_kit")){a.data("sticky_kit",!0);I=A.height();g=a.parent();null!=k&&(g=g.closest(k)); -if(!g.length)throw"failed to find stick parent";v=m=!1;(h=null!=p?p&&a.closest(p):b("
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Changelog

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-eurostat 3.2.3 Unreleased -

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-Minor features

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-eurostat 3.2.1 2018-05-20 -

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-eurostat 3.1.5 2017-08-09 -

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  • Added new example data set to reduce repeated downloads from eurostat service

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  • Now label_eurostat() gives always an error by default, if labelling introduces duplicated labels. A new fix_duplicated argument is add to fix duplicated labels automatically. (#79, #90)

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  • Shrinked the package tarball size

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-eurostat 3.1.1 2017-03-16 -

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-R Journal submission

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-eurostat 2.2.1 2016-09-14 -

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-eurostat 2.1.1 Unreleased -

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-eurostat 1.2.27 Unreleased -

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-eurostat 1.2.22 Unreleased -

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  • The European Commission and the Eurostat generally uses ISO 3166-1 alpha-2 codes with two exceptions: EL (not GR) is used to represent Greece, and UK (not GB) is used to represent the United Kingdom. This now can be handled with harmonize_country_code() which converts the raw data values from EL to GR and from UK to GB.
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-eurostat 1.2.21 2016-03-11 -

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-eurostat 1.2.13 2016-01-19 -

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-eurostat 1.0.16 2015-03-27 -

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-eurostat 1.0.14 (2015-03-19) Unreleased -

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-eurostat 0.9.1 (2014-04-24) Unreleased -

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Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

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Site built with pkgdown.

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- } -} - - -/* Sidebar --------------------------*/ - -#sidebar { - margin-top: 30px; -} -#sidebar h2 { - font-size: 1.5em; - margin-top: 1em; -} - -#sidebar h2:first-child { - margin-top: 0; -} - -#sidebar .list-unstyled li { - margin-bottom: 0.5em; -} - -.orcid { - height: 16px; - vertical-align: middle; -} - -/* Reference index & topics ----------------------------------------------- */ - -.ref-index th {font-weight: normal;} - -.ref-index td {vertical-align: top;} -.ref-index .alias {width: 40%;} -.ref-index .title {width: 60%;} - -.ref-index .alias {width: 40%;} -.ref-index .title {width: 60%;} - -.ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top;} -.ref-arguments .name {width: 20%;} -.ref-arguments .desc {width: 80%;} - -/* Nice scrolling for wide elements --------------------------------------- */ - -table { - display: block; - overflow: auto; -} - -/* Syntax highlighting ---------------------------------------------------- */ - -pre { - word-wrap: normal; 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- border-bottom: 2px solid rgba(255, 153, 51, 0.3); - padding: 1px; -} - -/* vertical spacing after htmlwidgets */ -.html-widget { - margin-bottom: 10px; -} diff --git a/docs/pkgdown.js b/docs/pkgdown.js deleted file mode 100755 index de9bd724..00000000 --- a/docs/pkgdown.js +++ /dev/null @@ -1,110 +0,0 @@ -/* http://gregfranko.com/blog/jquery-best-practices/ */ -(function($) { - $(function() { - - $("#sidebar") - .stick_in_parent({offset_top: 40}) - .on('sticky_kit:bottom', function(e) { - $(this).parent().css('position', 'static'); - }) - .on('sticky_kit:unbottom', function(e) { - $(this).parent().css('position', 'relative'); - }); - - $('body').scrollspy({ - target: '#sidebar', - offset: 60 - }); - - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - var path = paths(links[i].pathname); - - var length = prefix_length(cur_path, path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(0); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 1 : 0); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(Clipboard.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $(".examples, div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new Clipboard('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent; - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100755 index fc2b6ebb..00000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,9 +0,0 @@ -pandoc: 1.19.2.4 -pkgdown: 1.1.0.9000 -pkgdown_sha: 98bfe0fdfd59b33ef1a88ef6896cd60f805f0e81 -articles: - blogposts: blogposts.html - cheatsheet: cheatsheet.html - eurostat_tutorial: eurostat_tutorial.html - publications: publications.html - diff --git a/docs/reference/clean_eurostat_cache.html b/docs/reference/clean_eurostat_cache.html deleted file mode 100755 index 275dd068..00000000 --- a/docs/reference/clean_eurostat_cache.html +++ /dev/null @@ -1,177 +0,0 @@ - - - - - - - - -Clean Eurostat Cache — clean_eurostat_cache • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Clean Eurostat Cache

    - - -
    - -
    -

    Delete all .rds files from the eurostat cache directory. - See get_eurostat for more on cache.

    -
    - - 
    clean_eurostat_cache(cache_dir = NULL)
    - -

    Arguments

    - - - - - - -
    cache_dir

    A path to cache directory. If NULL (default) -tries to clean default temporary cache directory.

    - - -

    Examples

    - 
    clean_eurostat_cache()
    #> The cache does not exist
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/convert_time_col.html b/docs/reference/convert_time_col.html deleted file mode 100755 index 62470b59..00000000 --- a/docs/reference/convert_time_col.html +++ /dev/null @@ -1,171 +0,0 @@ - - - - - - - - -Time Column Conversions — convert_time_col • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Time Column Conversions

    - - -
    - -
    -

    Internal function to convert time column.

    -
    - - 
    convert_time_col(x, time_format)
    - -

    Arguments

    - - - - - - - - - - -
    x

    A time column (vector)

    time_format

    see tidy_eurostat

    - - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/cut_to_classes.html b/docs/reference/cut_to_classes.html deleted file mode 100755 index 2d47ecbe..00000000 --- a/docs/reference/cut_to_classes.html +++ /dev/null @@ -1,210 +0,0 @@ - - - - - - - - -Cuts the Values Column into Classes and Polishes the Labels — cut_to_classes • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Cuts the Values Column into Classes and Polishes the Labels

    - - -
    - -
    -

    Categorises a numeric vector into automatic or manually defined categories. -and polishes the labels ready for used in mapping with merge_with_geodata function and ggplot2.

    -
    - - 
    cut_to_classes(x, n = 5, style = "equal", manual = FALSE,
-  manual_breaks = NULL, decimals = 0, nodata_label = "No data")
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    x

    A numeric vector, eg. values variable in data returned by get_eurostat

    n

    A numeric. number of classes/categories

    style

    Chosen style: one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", -"bclust", "fisher", or "jenks"

    manual

    Logical. If manual breaks are being used

    manual_breaks

    Numeric vector with manual threshold values

    decimals

    Number of decimals to include with labels

    nodata_label

    String. Text label for NA category.

    - -

    Value

    - -

    a factor.

    - -

    Examples

    - 
     
    # NOT RUN {
-   lp <- get_eurostat("nama_aux_lp")
-   lp$class <- cut_to_classes(lp$values, n=5, style="equal", decimals=1)
-
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/dic_order.html b/docs/reference/dic_order.html deleted file mode 100755 index 74385f5c..00000000 --- a/docs/reference/dic_order.html +++ /dev/null @@ -1,189 +0,0 @@ - - - - - - - - -Order of Variable Levels from Eurostat Dictionary. — dic_order • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Order of Variable Levels from Eurostat Dictionary.

    - - -
    - -
    -

    Orders the factor levels.

    -
    - - 
    dic_order(x, dic, type)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    x

    a variable (code or labelled) to get order for.

    dic

    a name of the dictionary. Correspond a variable name in the -data_frame from get_eurostat. Can be also data_frame from -get_eurostat_dic.

    type

    a type of the x. Could be code or label.

    - -

    Value

    - -

    A numeric vector of orders.

    -

    Details

    - -

    Some variables, like classifications, have logical or conventional ordering. Eurostat data tables are nor neccessary ordered in this order. The function dic_order get the ordering from Eurostat classifications dictionaries. The function label_eurostat can also order factor levels of labels with argument eu_order = TRUE.

    - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/eu_countries.html b/docs/reference/eu_countries.html deleted file mode 100755 index 1a791dbd..00000000 --- a/docs/reference/eu_countries.html +++ /dev/null @@ -1,178 +0,0 @@ - - - - - - - - -Countries and Country Codes — eu_countries • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Countries and Country Codes

    - - -
    - -
    -

    Countries and country codes in EU, -Euro area, EFTA and EU candidate countries.

    -
    - - 
    eu_countries
-
-ea_countries
-
-efta_countries
-
-eu_candidate_countries
    - - -

    Format

    - -

    A data_frame:

    -
    code

    Country code in the Eurostat database

    -
    name

    Country name in English

    -
    -

    Source

    - -

    http://ec.europa.eu/eurostat/statistics-explained/index.php/Tutorial:Country_codes_and_protocol_order, - http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Euro_area

    - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/eurostat-package.html b/docs/reference/eurostat-package.html deleted file mode 100755 index f987a741..00000000 --- a/docs/reference/eurostat-package.html +++ /dev/null @@ -1,182 +0,0 @@ - - - - - - - - -R Tools for Eurostat open data — eurostat-package • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    R Tools for Eurostat open data

    - - -
    - -
    -

    Brief summary of the eurostat package

    -
    - - - -

    Details

    - - - - - - - - - -
    Package:eurostat
    Type:Package
    Version:See sessionInfo() or DESCRIPTION file
    Date:2014-2017
    License:BSD_2_clause + LICENSE
    LazyLoad:yes
    Package:
    -

    R Tools for Eurostat Open Data

    -

    References

    - -

    See citation("eurostat") -https://ropengov.github.io/eurostat

    - -

    Examples

    - 
    library(eurostat)
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/eurostat_geodata_60.html b/docs/reference/eurostat_geodata_60.html deleted file mode 100755 index a638b018..00000000 --- a/docs/reference/eurostat_geodata_60.html +++ /dev/null @@ -1,172 +0,0 @@ - - - - - - - - -Geospatial data of Europe from Gisco in 1:60 million scale — eurostat_geodata_60 • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Geospatial data of Europe from Gisco in 1:60 million scale

    - - -
    - -
    -

    Geospatial data of Europe from Gisco in 1:60 million scale

    -
    - - 
    eurostat_geodata_60
    - - -

    Format

    - -

    sf

    -
    id

    Country code in the Eurostat database

    -
    CNTRY_CODE

    Country code

    -
    NUTS_NAME

    NUTS name in local language

    -
    LEVL_CODE

    NUTS code

    -
    FID

    Country code

    -
    NUTS_ID

    NUTS code

    -
    geo

    NUTS code

    -
    geometry

    geospatial information

    -
    -

    Source

    - -

    http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units

    - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/eurostat_geodata_60_2013.html b/docs/reference/eurostat_geodata_60_2013.html deleted file mode 100755 index 437696f0..00000000 --- a/docs/reference/eurostat_geodata_60_2013.html +++ /dev/null @@ -1,175 +0,0 @@ - - - - - - - - -Geospatial data of Europe from Gisco in 1:60 million scale from year 2013 — eurostat_geodata_60_2013 • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Geospatial data of Europe from Gisco in 1:60 million scale from year 2013

    - - -
    - -
    -

    Geospatial data of Europe from Gisco in 1:60 million scale from year 2013

    -
    - - 
    eurostat_geodata_60_2013
    - - -

    Format

    - -

    sf

    -
    id

    Country code in the Eurostat database

    -
    CNTRY_CODE

    Country code

    -
    NUTS_NAME

    NUTS name in local language

    -
    LEVL_CODE

    NUTS code

    -
    FID

    Country code

    -
    NUTS_ID

    NUTS code

    -
    geo

    NUTS code

    -
    geometry

    geospatial information

    -
    -

    Source

    - -

    http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units

    - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/eurostat_geodata_60_2016.html b/docs/reference/eurostat_geodata_60_2016.html deleted file mode 100755 index 293f1141..00000000 --- a/docs/reference/eurostat_geodata_60_2016.html +++ /dev/null @@ -1,175 +0,0 @@ - - - - - - - - -Geospatial data of Europe from Gisco in 1:60 million scale from year 2016 — eurostat_geodata_60_2016 • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Geospatial data of Europe from Gisco in 1:60 million scale from year 2016

    - - -
    - -
    -

    Geospatial data of Europe from Gisco in 1:60 million scale from year 2016

    -
    - - 
    eurostat_geodata_60_2016
    - - -

    Format

    - -

    sf

    -
    id

    Country code in the Eurostat database

    -
    CNTRY_CODE

    Country code

    -
    NUTS_NAME

    NUTS name in local language

    -
    LEVL_CODE

    NUTS code

    -
    FID

    Country code

    -
    NUTS_ID

    NUTS code

    -
    geometry

    geospatial information

    -
    geo

    NUTS code

    -
    -

    Source

    - -

    http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units

    - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/eurostat_url.html b/docs/reference/eurostat_url.html deleted file mode 100755 index 29f6898f..00000000 --- a/docs/reference/eurostat_url.html +++ /dev/null @@ -1,179 +0,0 @@ - - - - - - - - -Eurostat Base URL — eurostat_url • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Eurostat Base URL

    - - -
    - -
    -

    Base URL

    -
    - - 
    eurostat_url(...)
    - -

    Arguments

    - - - - - - -
    ...

    Arguments to be passed

    - -

    Value

    - -

    url

    -

    References

    - -

    see citation("eurostat").

    - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/eurotime2date.html b/docs/reference/eurotime2date.html deleted file mode 100755 index 95af4110..00000000 --- a/docs/reference/eurotime2date.html +++ /dev/null @@ -1,201 +0,0 @@ - - - - - - - - -Date Conversion from Eurostat Time Format — eurotime2date • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Date Conversion from Eurostat Time Format

    - - -
    - -
    -

    Date conversion from Eurostat time format. A function to -convert Eurostat time values to objects of class Date -representing calendar dates.

    -
    - - 
    eurotime2date(x, last = FALSE)
    - -

    Arguments

    - - - - - - - - - - -
    x

    a charter string with time information in Eurostat time format.

    last

    a logical. If FALsE (default) the date is -the first date of the period (month, quarter or year). If TRUE -the date is the last date of the period.

    - -

    Value

    - -

    an object of class Date.

    - -

    Examples

    - 
    # NOT RUN {
-   na_q <- get_eurostat("namq_10_pc", time_format = "raw")
-   na_q$time <- eurotime2date(x = na_q$time)
-
-   un <- get_eurostat("une_rt_m", time_format = "raw")
-   un$time <- eurotime2date(x = un$time)
-
-   na_a <- get_eurostat("nama_10_pc", time_format = "raw")
-   na_a$time <- eurotime2date(x = na_a$time)
-
-   eur_d <- get_eurostat("ert_bil_eur_d", time_format = "raw")
-   eur_d$time <- eurotime2date(x = eur_d$time)
-
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/eurotime2num.html b/docs/reference/eurotime2num.html deleted file mode 100755 index 65a11ed5..00000000 --- a/docs/reference/eurotime2num.html +++ /dev/null @@ -1,194 +0,0 @@ - - - - - - - - -Conversion of Eurostat Time Format to Numeric — eurotime2num • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Conversion of Eurostat Time Format to Numeric

    - - -
    - -
    -

    A conversion of a Eurostat time format to numeric.

    -
    - - 
    eurotime2num(x)
    - -

    Arguments

    - - - - - - -
    x

    a charter string with time information in Eurostat time format.

    - -

    Value

    - -

    see as.numeric.

    -

    Details

    - -

    Bi-annual, quarterly and monthly data is presented as fraction of - the year in beginning of the period. Conversion of daily data is not - supported.

    - -

    Examples

    - 
    # NOT RUN {
-   na_q <- get_eurostat("namq_10_pc", time_format = "raw")
-   na_q$time <- eurotime2num(x = na_q$time)
-
-   un <- get_eurostat("une_rt_m", time_format = "raw")
-   un$time <- eurotime2num(x = un$time)
-
-   na_a <- get_eurostat("nama_10_pc", time_format = "raw")
-   na_a$time <- eurotime2num(x = na_a$time)
-
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/get_eurostat.html b/docs/reference/get_eurostat.html deleted file mode 100755 index 95220202..00000000 --- a/docs/reference/get_eurostat.html +++ /dev/null @@ -1,320 +0,0 @@ - - - - - - - - -Read Eurostat Data — get_eurostat • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Read Eurostat Data

    - - -
    - -
    -

    Download data sets from Eurostat ec.europa.eu/eurostat.

    -
    - - 
    get_eurostat(id, time_format = "date", filters = "none",
-  type = "code", select_time = NULL, cache = TRUE,
-  update_cache = FALSE, cache_dir = NULL, compress_file = TRUE,
-  stringsAsFactors = default.stringsAsFactors(), keepFlags = FALSE,
-  ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    id

    A code name for the dataset of interest. -See search_eurostat or details for how to get code.

    time_format

    a string giving a type of the conversion of the time -column from the eurostat format. A "date" (default) convers to -a Date with a first date of the period. -A "date_last" convers to a Date with - a last date of the period. A "num" convers to a numeric and "raw" - does not do conversion. See eurotime2date and - eurotime2num.

    filters

    a "none" (default) to get a whole dataset or a named list of -filters to get just part of the table. Names of list objects are -Eurostat variable codes and values are vectors of observation codes. -If NULL the whole -dataset is returned via API. More on details. See more on filters and -limitations per query via API from for -get_eurostat_json.

    type

    A type of variables, "code" (default) or "label".

    select_time

    a character symbol for a time frequence or NULL, -which is used by default as most datasets have just one time -frequency. For datasets with multiple time -frequencies, select the desired time format with: - Y = annual, S = semi-annual, Q = quarterly, M = monthly. -For all frequencies in same data frame time_format = "raw" -should be used.

    cache

    a logical whether to do caching. Default is TRUE. Affects -only queries from the bulk download facility.

    update_cache

    a locigal whether to update cache. Can be set also with -options(eurostat_update = TRUE)

    cache_dir

    a path to a cache directory. The directory have to exist. -The NULL (default) uses and creates -'eurostat' directory in the temporary directory from -tempdir. Directory can also be set with -option eurostat_cache_dir.

    compress_file

    a logical whether to compress the -RDS-file in caching. Default is TRUE.

    stringsAsFactors

    if TRUE (the default) variables are -converted to factors in original Eurostat order. If FALSE -they are returned as a character.

    keepFlags

    a logical whether the flags (e.g. "confidential", -"provisional") should be kept in a separate column or if they -can be removed. Default is FALSE. For flag values see: -http://ec.europa.eu/eurostat/data/database/information. -Also possible non-real zero "0n" is indicated in flags column. -Flags are not available for eurostat API, so keepFlags -can not be used with a filters.

    ...

    further argument for get_eurostat_json.

    - -

    Value

    - -

    a tibble. One column for each dimension in the data, - the time column for a time dimension and - the values column for numerical values. - Eurostat data does not include all missing values and a treatment of - missing values depend on source. In bulk download - facility missing values are dropped if all dimensions are missing - on particular time. In JSON API missing values are dropped - only if all dimensions are missing on all times. The data from - bulk download facility can be completed for example with - complete.

    -

    Details

    - -

    Data sets are downloaded from -the Eurostat bulk download facility or from The Eurostat Web Services -JSON API. -If only the table id is given, the whole table is downloaded from the -bulk download facility. If also filters are defined the JSON API is -used.

    -

    The bulk download facility is the fastest method to download whole datasets. -It is also often the only way as the JSON API has limitation of maximum -50 sub-indicators at time and whole datasets usually exceeds that. Also, -it seems that multi frequency datasets can only be retrived via -bulk download facility and the select_time is not available for -JSON API method.

    -

    If your connection is thru a proxy, you probably have to set proxy parameters -to use JSON API, see get_eurostat_json.

    -

    By default datasets from the bulk download facility are cached as they are -often rather large. Caching is not (currently) possible for datasets from -JSON API. -Cache files are stored in a temporary directory by default or in -a named directory if cache_dir or option eurostat_cache_dir is defined. -The cache can be emptied with clean_eurostat_cache.

    -

    The id, a code, for the dataset can be searched with -the search_eurostat or from the Eurostat database -http://ec.europa.eu/eurostat/data/database. The Eurostat -database gives codes in the Data Navigation Tree after every dataset -in parenthesis.

    -

    See also

    - -

    search_eurostat, label_eurostat

    - -

    Examples

    - 
    # NOT RUN {
-k <- get_eurostat("nama_10_lp_ulc")
-k <- get_eurostat("nama_10_lp_ulc", time_format = "num")
-k <- get_eurostat("nama_10_lp_ulc", update_cache = TRUE)
-dir.create(file.path(tempdir(), "r_cache"))
-k <- get_eurostat("nama_10_lp_ulc",
-                  cache_dir = file.path(tempdir(), "r_cache"))
-options(eurostat_update = TRUE)
-k <- get_eurostat("nama_10_lp_ulc")
-options(eurostat_update = FALSE)
-options(eurostat_cache_dir = file.path(tempdir(), "r_cache"))
-k <- get_eurostat("nama_10_lp_ulc")
-k <- get_eurostat("nama_10_lp_ulc", cache = FALSE)
-k <- get_eurostat("avia_gonc", select_time = "Y", cache = FALSE)
-
-dd <- get_eurostat("nama_10_gdp",
-                     filters = list(geo = "FI",
-                                    na_item = "B1GQ",
-                                    unit = "CLV_I10"))
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/get_eurostat_dic.html b/docs/reference/get_eurostat_dic.html deleted file mode 100755 index cb313b3b..00000000 --- a/docs/reference/get_eurostat_dic.html +++ /dev/null @@ -1,202 +0,0 @@ - - - - - - - - -Download Eurostat Dictionary — get_eurostat_dic • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Download Eurostat Dictionary

    - - -
    - -
    -

    Download a Eurostat dictionary.

    -
    - - 
    get_eurostat_dic(dictname, lang = "en")
    - -

    Arguments

    - - - - - - - - - - -
    dictname

    A character, dictionary for the variable to be downloaded.

    lang

    A character, language code. Options: "en" (default) / "fr" / "de".

    - -

    Value

    - -

    tibble with two columns: code names and full names.

    -

    Details

    - -

    For given coded variable from Eurostat ec.europa.eu/eurostat. - The dictionaries link codes with human-readable labels. - To translate codes to labels, use label_eurostat.

    -

    References

    - -

    See citation("eurostat").

    -

    See also

    - -

    label_eurostat, get_eurostat, - search_eurostat.

    - -

    Examples

    - 
    # NOT RUN {
-               tmp <- get_eurostat_dic("crop_pro")
-               head(tmp)
-               tmp <- get_eurostat_dic("crop_pro", lang = "fr")
-
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/get_eurostat_geospatial.html b/docs/reference/get_eurostat_geospatial.html deleted file mode 100755 index 0957bb92..00000000 --- a/docs/reference/get_eurostat_geospatial.html +++ /dev/null @@ -1,231 +0,0 @@ - - - - - - - - -Download Geospatial Data from GISGO — get_eurostat_geospatial • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Download Geospatial Data from GISGO

    - - -
    - -
    -

    Downloads either a simple features (sf), SpatialPolygonDataFrame or a data_frame preprocessed using - broom::tidy().

    -
    - - 
    get_eurostat_geospatial(output_class = "sf", resolution = "60",
-  nuts_level = "all", year = "2016", cache = TRUE,
-  update_cache = FALSE, cache_dir = NULL)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    output_class

    A string. Class of object returned, -either sf simple features, df (data_frame) or - spdf (SpatialPolygonDataFrame)

    resolution

    Resolution of the geospatial data. One of -"60" (1:60million), "20" (1:20million), "10" (1:10million), "03" (1:3million) or "01" (1:1million).

    nuts_level

    Level of NUTS classification of the geospatial data. One of -"0", "1", "2", "3" or "all" (mimics the original behaviour)

    year

    NUTS release year. One of -"2003", "2006", "2010", "2013" or "2016"

    cache

    a logical whether to do caching. Default is TRUE. Affects -only queries from the bulk download facility.

    update_cache

    a locigal whether to update cache. Can be set also with -options(eurostat_update = TRUE)

    cache_dir

    a path to a cache directory. The directory have to exist. -The NULL (default) uses and creates -'eurostat' directory in the temporary directory from -tempdir. Directory can also be set with -option eurostat_cache_dir.

    - -

    Value

    - -

    a sf, data_frame or SpatialPolygonDataFrame.

    -

    Details

    - -

    The data source URL is http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units.

    - -

    Examples

    - 
     
    # NOT RUN {
-   lp <- get_eurostat_geospatial(output_class = "sf", resolution = "60", nuts_level = "all")
-   lp %>%  select(NUTS_ID) %>%  plot()
-   lp <- get_eurostat_geospatial(output_class = "spdf", resolution = "60", nuts_level = "all")
-   spplot(lp, "STAT_LEVL_")
-   # or
-   lp <- get_eurostat_geospatial(output_class = "df", resolution = "60", nuts_level = "all")
-   ggplot(lp, aes(x=long,y=lat,group=group,fill=STAT_LEVL_),color="white") + geom_polygon()
-
-# }
     
-
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/get_eurostat_json.html b/docs/reference/get_eurostat_json.html deleted file mode 100755 index 7957495a..00000000 --- a/docs/reference/get_eurostat_json.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - -Get Data from Eurostat API in JSON — get_eurostat_json • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Get Data from Eurostat API in JSON

    - - -
    - -
    -

    Retrieve data from Eurostat API in JSON format.

    -
    - - 
    get_eurostat_json(id, filters = NULL, type = c("code", "label",
-  "both"), lang = c("en", "fr", "de"),
-  stringsAsFactors = default.stringsAsFactors(), ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    id

    A code name for the dataset of interested. See the table of -contents of eurostat datasets for more details.

    filters

    A named list of filters. Names of list objects are Eurostat -variable codes and values are vectors of observation codes. If NULL -(default) the whole dataset is returned. See details for more on filters -and limitations per query.

    type

    A type of variables, "code" (default), "label" or "both". The -"both" will return a data_frame with named vectors, labels as values and -codes as names.

    lang

    A language used for metadata (en/fr/de).

    stringsAsFactors

    if TRUE (the default) variables are converted -to factors in original Eurostat order. If FALSE they are returned as -a character.

    ...

    Other arguments passed on to GET. For example -a proxy parameters, see details. -.

    - -

    Value

    - -

    A dataset as a data_frame.

    -

    Details

    - -

    Data to retrieve from - The - Eurostat Web Services can be specified with filters. Normally, it is - better to use JSON query through get_eurostat, than to use - get_eurostat_json directly.

    -

    Queries are limited to 50 sub-indicators at a time. A time can be - filtered with fixed "time" filter or with "sinceTimePeriod" and - "lastTimePeriod" filters. A sinceTimePeriod = 2000 returns - observations from 2000 to a last available. A lastTimePeriod = 10 - returns a 10 last observations.

    -

    To use a proxy to connect, a use_proxy can be - passed to GET. For example - get_eurostat_json(id, filters, - config = httr::use_proxy(url, port, username, password)).

    - -

    Examples

    - 
     
    # NOT RUN {
-   tmp <- get_eurostat_json("cdh_e_fos")
-   yy <- get_eurostat_json(id = "nama_gdp_c", filters = list(geo=c("EU28", "FI"),
-                                                        unit="EUR_HAB",
-                                                        indic_na="B1GM"))
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/get_eurostat_raw.html b/docs/reference/get_eurostat_raw.html deleted file mode 100755 index 9609f435..00000000 --- a/docs/reference/get_eurostat_raw.html +++ /dev/null @@ -1,200 +0,0 @@ - - - - - - - - -Download Data from Eurostat Database — get_eurostat_raw • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Download Data from Eurostat Database

    - - -
    - -
    -

    Download data from the eurostat database.

    -
    - - 
    get_eurostat_raw(id)
    - -

    Arguments

    - - - - - - -
    id

    A code name for the dataset of interested. See the table of -contents of eurostat datasets for more details.

    - -

    Value

    - -

    A dataset in tibble format. First column contains comma - separated codes of cases. Other columns usually corresponds to - years and column names are years with preceding X. Data is in - character format as it contains values together with eurostat - flags for data.

    -

    Details

    - -

    Data is downloaded from - http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing and transformed into tabular format.

    -

    References

    - -

    see citation("eurostat")

    -

    See also

    - -

    get_eurostat.

    - -

    Examples

    - 
    # NOT RUN {
-               tmp <- eurostat:::get_eurostat_raw("educ_iste")
-               head(tmp)
-
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/get_eurostat_toc.html b/docs/reference/get_eurostat_toc.html deleted file mode 100755 index ae205496..00000000 --- a/docs/reference/get_eurostat_toc.html +++ /dev/null @@ -1,189 +0,0 @@ - - - - - - - - -Download Table of Contents of Eurostat Data Sets — get_eurostat_toc • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Download Table of Contents of Eurostat Data Sets

    - - -
    - -
    -

    Download table of contents (TOC) of eurostat datasets.

    -
    - - 
    get_eurostat_toc()
    - - -

    Value

    - -

    A tibble with eight columns

      -

    • titleThe name of dataset of theme

      • -

    • codeThe codename of dataset of theme, will be used by the eurostat and get_eurostat_raw functions.

      • -

    • typeIs it a dataset, folder or table.

      • -

    • last.update.of.data, last.table.structure.change, data.start, data.endDates.

      • -
    - -

    Details

    - -

    The TOC is downloaded from http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&file=table_of_contents_en.txt. The values in column 'code' should be used to download a selected dataset.

    -

    References

    - -

    See citation("eurostat").

    -

    See also

    - -

    get_eurostat, search_eurostat.

    - -

    Examples

    - 
    # NOT RUN {
-tmp <- get_eurostat_toc(); head(tmp)
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/harmonize_country_code.html b/docs/reference/harmonize_country_code.html deleted file mode 100755 index 840ed0aa..00000000 --- a/docs/reference/harmonize_country_code.html +++ /dev/null @@ -1,190 +0,0 @@ - - - - - - - - -Harmonize Country Code — harmonize_country_code • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Harmonize Country Code

    - - -
    - -
    -

    The European Commission and the Eurostat generally uses ISO - 3166-1 alpha-2 codes with two exceptions: EL (not GR) is used to - represent Greece, and UK (not GB) is used to represent the - United Kingdom. This function turns country codes into to ISO - 3166-1 alpha-2.

    -
    - - 
    harmonize_country_code(x)
    - -

    Arguments

    - - - - - - -
    x

    A character or a factor vector of eurostat countycodes.

    - -

    Value

    - -

    a vector.

    - -

    Examples

    - 
     
    # NOT RUN {
-   lp <- get_eurostat("nama_aux_lp")
-   lp$geo <- harmonize_country_code(lp$geo)
-
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/index.html b/docs/reference/index.html deleted file mode 100755 index a735926e..00000000 --- a/docs/reference/index.html +++ /dev/null @@ -1,273 +0,0 @@ - - - - - - - - -Function reference • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Reference

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -

    All functions

    -

    -
    -

    clean_eurostat_cache()

    -

    Clean Eurostat Cache

    -

    cut_to_classes()

    -

    Cuts the Values Column into Classes and Polishes the Labels

    -

    dic_order()

    -

    Order of Variable Levels from Eurostat Dictionary.

    -

    eu_countries ea_countries efta_countries eu_candidate_countries

    -

    Countries and Country Codes

    -

    eurostat-package

    -

    R Tools for Eurostat open data

    -

    eurostat_geodata_60_2016

    -

    Geospatial data of Europe from Gisco in 1:60 million scale from year 2016

    -

    eurotime2date()

    -

    Date Conversion from Eurostat Time Format

    -

    eurotime2num()

    -

    Conversion of Eurostat Time Format to Numeric

    -

    get_eurostat()

    -

    Read Eurostat Data

    -

    get_eurostat_dic()

    -

    Download Eurostat Dictionary

    -

    get_eurostat_geospatial()

    -

    Download Geospatial Data from GISGO

    -

    get_eurostat_json()

    -

    Get Data from Eurostat API in JSON

    -

    get_eurostat_raw()

    -

    Download Data from Eurostat Database

    -

    get_eurostat_toc()

    -

    Download Table of Contents of Eurostat Data Sets

    -

    harmonize_country_code()

    -

    Harmonize Country Code

    -

    label_eurostat() label_eurostat_vars() label_eurostat_tables()

    -

    Get Eurostat Codes

    -

    search_eurostat() grepEurostatTOC()

    -

    Grep Datasets Titles from Eurostat

    -

    tgs00026

    -

    Auxiliary Data

    -
    - - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/label_eurostat.html b/docs/reference/label_eurostat.html deleted file mode 100755 index ec31c287..00000000 --- a/docs/reference/label_eurostat.html +++ /dev/null @@ -1,270 +0,0 @@ - - - - - - - - -Get Eurostat Codes — label_eurostat • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Get Eurostat Codes

    - - -
    - -
    -

    Get definitions for Eurostat codes from Eurostat dictionaries.

    -
    - - 
    label_eurostat(x, dic = NULL, code = NULL, eu_order = FALSE,
-  lang = "en", countrycode = NULL, countrycode_nomatch = NULL,
-  custom_dic = NULL, fix_duplicated = FALSE)
-
-label_eurostat_vars(x, lang = "en")
-
-label_eurostat_tables(x, lang = "en")
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    x

    A character or a factor vector or a data_frame.

    dic

    A string (vector) naming eurostat dictionary or dictionaries. -If NULL (default) dictionry names taken from column names of -the data_frame.

    code

    For data_frames names of the column for which also code columns -should be retained. The suffix "_code" is added to code column names.

    eu_order

    Logical. Should Eurostat ordering used for label levels. -Affects only factors.

    lang

    A character, code for language. Available are "en" (default), -"fr" and "de".

    countrycode

    A NULL or a name of the coding scheme for -the countrycode -to label "geo" variable with countrycode-package. It can be used to -convert to short and long country names in many different languages. -If NULL (default) eurostat dictionary is used instead.

    countrycode_nomatch

    What to do when using the countrycode to label a "geo" and -countrycode fails to find a match, for example other than country codes like EU28. -the original code is used with -a NULL (default), eurostat dictionary label is used with "eurostat", -and NA is used with NA.

    custom_dic

    a named vector or named list of named vectors to give an own dictionary -for (part of) codes. Names of the vector should be codes and values labels. List -can be used to spesify dictonaries and then list names should be dictionary codes.

    fix_duplicated

    A logical. If TRUE, the code is added to the -duplicated label values. If FALSE (default) error is given if -labelling produce duplicates.

    - -

    Value

    - -

    a vector or a data_frame.

    -

    Details

    - -

    A character or a factor vector of codes returns a corresponding - vector of definitions. label_eurostat labels also data_frames from - get_eurostat. For vectors a dictionary name have to be - supplied. For data_frames dictonary names are taken from column names. - "time" and "values" columns are returned as they were, so you can supply - data_frame from get_eurostat and get data_frame with - definitions instead of codes.

    -

    Some Eurostat dictionaries includes dublicated labels. By default - dublicated labels cause an error, but they can be fixed automatically - with fix_duplicated = TRUE.

    -

    Functions

    - -
      -

    • label_eurostat_vars: Get definitions for variable (column) names. For -objects other than characters or factors definitions are get for names.

      • -

    • label_eurostat_tables: Get definitions for table names

      • -
    - -

    Examples

    - 
     
    # NOT RUN {
-   lp <- get_eurostat("nama_10_lp_ulc")
-   lpl <- label_eurostat(lp)
-   str(lpl)
-   lpl_order <- label_eurostat(lp, eu_order = TRUE)
-   lpl_code <- label_eurostat(lp, code = "unit")
-   label_eurostat_vars(names(lp))
-   label_eurostat_tables("nama_10_lp_ulc")
-   label_eurostat(c("FI", "DE", "EU28"), dic = "geo")
-   label_eurostat(c("FI", "DE", "EU28"), dic = "geo", custom_dic = c(DE = "Germany"))
-   label_eurostat(c("FI", "DE", "EU28"), dic = "geo", countrycode = "country.name",
-                  custom_dic = c(EU28 = "EU"))
-   label_eurostat(c("FI", "DE", "EU28"), dic = "geo", countrycode = "country.name")
-   # In Finnish
-   label_eurostat(c("FI", "DE", "EU28"), dic = "geo", countrycode = "cldr.short.fi")
-
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/merge_eurostat_geodata.html b/docs/reference/merge_eurostat_geodata.html deleted file mode 100755 index abc45974..00000000 --- a/docs/reference/merge_eurostat_geodata.html +++ /dev/null @@ -1,189 +0,0 @@ - - - - - - - - -Merge Geospatial GISCO Data with Eurostat data_frame — merge_eurostat_geodata • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Merge Geospatial GISCO Data with Eurostat data_frame

    -
    - -

    Merges data_frame obtained from Eurostat with get_eurostat with geospatial data preprocessed either using ggplot2::fortify into a data_frame or a regular SpatialPolygonDataFrame. The resulting data_frame can be plotted using ggplot2 and SpatialPolygonDataFrame using sp::spplot.

    - - 
    merge_eurostat_geodata(data, geocolumn = "geo", resolution = "60",
-  all_regions = FALSE, cache = TRUE, update_cache = FALSE,
-  cache_dir = NULL)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    data

    A data_frame including a character vector that -consists of values following current NUTS classification

    geocolumn

    A string. Name of the column with NUTS information -(geo in data_frames fetched using get_eurostat)

    resolution

    Resolution of the geospatial data. One of "60" (1:60million), "20" (1:20million), "10" (1:10million), "01" (1:1million),

    all_regions

    Logical. To include all the regions from spatial data or only the ones included in the fetched Eurostat attribute data

    cache

    cache. Logical.

    update_cache

    Update cache. Logical.

    cache_dir

    Cache directory.

    - -

    Value

    - -

    a data_frame or SpatialPolygonDataFrame.

    - -

    Examples

    - 
     
    # NOT RUN {
-   lp <- get_eurostat("nama_aux_lp")
-   lpl <- merge_eurostat_geodata(lp, geocolumn="geo", resolution=60,
-                            all_regions=FALSE)
-   str(lpl)
-
-# }
     
-
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - diff --git a/docs/reference/search_eurostat.html b/docs/reference/search_eurostat.html deleted file mode 100755 index d38d64b8..00000000 --- a/docs/reference/search_eurostat.html +++ /dev/null @@ -1,226 +0,0 @@ - - - - - - - - -Grep Datasets Titles from Eurostat — search_eurostat • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Grep Datasets Titles from Eurostat

    - - -
    - -
    -

    Lists names of dataset from eurostat with the particular -pattern in the description.

    -
    - - 
    search_eurostat(pattern, type = "dataset", fixed = TRUE)
-
-grepEurostatTOC(pattern, type = "dataset")
    - -

    Arguments

    - - - - - - - - - - - - - - -
    pattern

    Character, datasets, folder or tables with this pattern in -the description will be returned (depending on the 'type' argument)

    type

    Grep the Eurostat table of contents either for -'dataset' (default), 'folder', 'table' or "all" (for all types).

    fixed

    logical. If TRUE, pattern is a string to be matched as is. -Change to FALSE if more complex regex matching is needed.

    - -

    Value

    - -

    A tibble with eight columns

      -

    • titleThe name of dataset of theme

      • -

    • codeThe codename of dataset of theme, will be used by the get_eurostat and get_eurostat_raw functions.

      • -

    • typeIs it a dataset, folder or table.

      • -

    • last.update.of.data, last.table.structure.change, data.start, data.endDates.

      • -
    - -

    Details

    - -

    Downloads list of all datasets available on -eurostat and return list of names of datasets that contains particular -pattern in the dataset description. E.g. all datasets related to -education of teaching.

    -

    Functions

    - -
      -

    • grepEurostatTOC: Old deprecated version

      • -
    -

    References

    - -

    See citation("eurostat")

    -

    See also

    - -

    get_eurostat, get_eurostat_toc

    - -

    Examples

    - 
    # NOT RUN {
-  tmp <- search_eurostat("education")
-  head(tmp)
-  # Use "fixed = TRUE" when pattern has characters that would need escaping.
-  # Here, parentheses would normally need to be escaped in regex
-  tmp <- search_eurostat("Live births (total) by NUTS 3 region", fixed = TRUE)
-# }
    -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/set_eurostat_toc.html b/docs/reference/set_eurostat_toc.html deleted file mode 100755 index 0fd7428e..00000000 --- a/docs/reference/set_eurostat_toc.html +++ /dev/null @@ -1,179 +0,0 @@ - - - - - - - - -Set Eurostat TOC — set_eurostat_toc • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Set Eurostat TOC

    - - -
    - -
    -

    Internal function.

    -
    - - 
    set_eurostat_toc(...)
    - -

    Arguments

    - - - - - - -
    ...

    Arguments to be passed

    - -

    Value

    - -

    Empty element

    -

    References

    - -

    see citation("eurostat")

    - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/tgs00026.html b/docs/reference/tgs00026.html deleted file mode 100755 index 7bb912f9..00000000 --- a/docs/reference/tgs00026.html +++ /dev/null @@ -1,166 +0,0 @@ - - - - - - - - -Auxiliary Data — tgs00026 • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Auxiliary Data

    - - -
    - -
    -

    Auxiliary Data Sets

    -
    - - 
    tgs00026
    - - -

    Format

    - -

    data_frame

    -

    Details

    - -

    Retrieved with: tgs00026 <- get_eurostat("tgs00026", time_format = "raw")

    - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/docs/reference/tidy_eurostat.html b/docs/reference/tidy_eurostat.html deleted file mode 100755 index ec203ff4..00000000 --- a/docs/reference/tidy_eurostat.html +++ /dev/null @@ -1,214 +0,0 @@ - - - - - - - - -Transform Data into Row-Column-Value Format — tidy_eurostat • eurostat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - -
    - -
    -
    -
    -

    Transform Data into Row-Column-Value Format

    - - -
    - -
    -

    Transform raw Eurostat data table into the row-column-value -format (RCV).

    -
    - - 
    tidy_eurostat(dat, time_format = "date", select_time = NULL,
-  stringsAsFactors = default.stringsAsFactors(), keepFlags = FALSE)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    dat

    a data_frame from get_eurostat_raw.

    time_format

    a string giving a type of the conversion of the - time column from the eurostat format. - A "date" (default) convers to a Date - with a first date of the period. A "date_last" - convers to a Date with -a last date of the period. A "num" convers to a numeric and "raw" -does not do conversion. See eurotime2date and -eurotime2num.

    select_time

    a character symbol for a time frequence or NULL -(default).

    stringsAsFactors

    if TRUE (the default) variables are -converted to factors in original Eurostat order. If FALSE -they are returned as strings.

    keepFlags

    a logical whether the flags (e.g. "confidential", -"provisional") should be kept in a separate column or if they -can be removed. Default is FALSE

    - -

    Value

    - -

    tibble in the molten format with the last column 'values'.

    -

    References

    - -

    See citation("eurostat").

    -

    See also

    - -

    get_eurostat

    - -
    - -
    - -
    -
    -

    Developed by Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.

    -
    - -
    -

    Site built with pkgdown.

    -
    - -
    -
    - - - - - - diff --git a/index.Rmd b/index.Rmd deleted file mode 100755 index 58c5dda5..00000000 --- a/index.Rmd +++ /dev/null @@ -1,112 +0,0 @@ ---- -output: - html_document: - keep_md: yes - home: - links: - - text: eurostat homepage - href: http://ropengov.github.io/eurostat/ ---- - - -
    - -[![license](https://img.shields.io/github/license/mashape/apistatus.svg)]() - -[![PRs Welcome][prs-badge]][prs] -[![Code of Conduct][coc-badge]][coc] - -[![Build Status](https://travis-ci.org/rOpenGov/eurostat.svg?branch=master)](https://travis-ci.org/rOpenGov/eurostat) -[![AppVeyor Status](https://ci.appveyor.com/api/projects/status/github/rOpenGov/eurostat?branch=master&svg=true)](https://ci.appveyor.com/project/rOpenGov/eurostat) -[![codecov.io](https://codecov.io/github/rOpenGov/eurostat/coverage.svg?branch=master)](https://codecov.io/github/rOpenGov/eurostat?branch=master) -[![Downloads](http://cranlogs.r-pkg.org/badges/grand-total/eurostat)](https://cran.r-project.org/package=eurostat) -[![Downloads](http://cranlogs.r-pkg.org/badges/eurostat)](https://cran.r-project.org/package=eurostat) - -[![Gitter](https://badges.gitter.im/rOpenGov/eurostat.svg)](https://gitter.im/rOpenGov/eurostat?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) -[![Watch on GitHub][github-watch-badge]][github-watch] -[![Star on GitHub][github-star-badge]][github-star] -[![Follow](https://img.shields.io/twitter/follow/ropengov.svg?style=social)](https://twitter.com/intent/follow?screen_name=ropengov) - -
    - -# eurostat R package - -R tools to access open data from [Eurostat](http://ec.europa.eu/eurostat). Data search, download, manipulation and visualization. - - -### Installation - -To install the CRAN release version, use: - -```{r install, eval=FALSE} -install.packages("eurostat") -``` - - -For the latest github development version, use: - -```{r installdev, eval=FALSE} -install_github("ropengov/eurostat") -``` -```{r lib, include=FALSE, echo=FALSE, warning=FALSE, message=FALSE} -library(eurostat) -``` - -### Using the package - -The [R Journal manuscript](https://journal.r-project.org/archive/2017/RJ-2017-019/index.html) provides an overview of the package. Various tutorials [are available](http://ropengov.github.io/eurostat/articles/index.html) and provide more detailed demonstrations. Check the following: - - * [Cheat sheet: eurostat R package](http://ropengov.github.io/eurostat/articles/cheatsheet.html) - * [Tutorial (vignette) for the eurostat R package](http://ropengov.github.io/eurostat/articles/eurostat_tutorial.html) - * [Blog posts](http://ropengov.github.io/eurostat/articles/blogposts.html) - * [Detailed function documentation](http://ropengov.github.io/eurostat/reference/index.html) - - - -### Contribute - -Check the Github page for [source code](https://github.com/rOpenGov/eurostat/). - -Contributions are very welcome: - - * [Use issue tracker](https://github.com/ropengov/eurostat/issues) for feedback and bug reports. - * [Send pull requests](https://github.com/ropengov/eurostat/) - * [Star us on the Github page](https://github.com/ropengov/eurostat) - * [Join the discussion in Gitter](https://gitter.im/rOpenGov/eurostat) - -### Acknowledgements - -**Kindly cite this work** as follows: [Leo Lahti](https://github.com/antagomir), Przemyslaw Biecek, Markus Kainu and Janne Huovari. Retrieval and analysis of Eurostat open data with the eurostat package. [R Journal 9(1):385-392, 2017](https://journal.r-project.org/archive/2017/RJ-2017-019/index.html)). R package version `r sessionInfo()$otherPkgs$eurostat$Version`. URL: [http://ropengov.github.io/eurostat](http://ropengov.github.io/eurostat) - -We are greatful to all [contributors](https://github.com/rOpenGov/eurostat/graphs/contributors), including Joona Lehtomäki, Francois Briatte, and Oliver Reiter, and for the [Eurostat](http://ec.europa.eu/eurostat/) open data portal! This project is part of [rOpenGov](http://ropengov.github.io). - - -### Disclaimer - -This package is in no way officially related to or endorsed by Eurostat. - - -[chat-badge]: https://img.shields.io/badge/chat-on%20gitter-46BC99.svg?style=flat-square -[chat]: https://gitter.im/ropengov/eurostat?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge -[build-badge]: https://img.shields.io/travis/ropengov/eurostat.svg?style=flat-square -[build]: https://travis-ci.org/ropengov/eurostat -[version-badge]: https://img.shields.io/npm/v/eurostat.svg?style=flat-square -[package]: https://www.npmjs.com/package/eurostat -[license-badge]: https://img.shields.io/npm/l/eurostat.svg?style=flat-square -[license]: https://github.com/ropengov/eurostat/blob/master/LICENSE -[prs-badge]: https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square -[prs]: http://makeapullrequest.com -[donate-badge]: https://img.shields.io/badge/$-support-green.svg?style=flat-square -[donate]: http://kcd.im/donate -[coc-badge]: https://img.shields.io/badge/code%20of-conduct-ff69b4.svg?style=flat-square -[coc]: https://github.com/ropengov/eurostat/blob/master/other/CODE_OF_CONDUCT.md -[implementations-badge]: https://img.shields.io/badge/%F0%9F%92%A1-implementations-8C8E93.svg?style=flat-square -[implementations]: https://github.com/ropengov/eurostat/blob/master/other/IMPLEMENTATIONS.md -[github-watch-badge]: https://img.shields.io/github/watchers/ropengov/eurostat.svg?style=social -[github-watch]: https://github.com/ropengov/eurostat/watchers -[github-star-badge]: https://img.shields.io/github/stars/ropengov/eurostat.svg?style=social -[github-star]: https://github.com/ropengov/eurostat/stargazers -[twitter]: https://twitter.com/intent/tweet?text=Check%20out%20eurostat!%20%E2%9C%A8%20Recognize%20all%20contributors,%20not%20just%20the%20ones%20who%20commit%20code%20%E2%9C%A8%20https://github.com/ropengov/eurostat%20%F0%9F%A4%97 -[twitter-badge]: https://img.shields.io/twitter/url/https/github.com/ropengov/eurostat.svg?style=social -[emojis]: https://github.com/ropengov/eurostat#emoji-key -[eurostat]: https://github.com/ropengov/eurostat diff --git a/inst/CITATION b/inst/CITATION index b06947df..ca7e261b 100755 --- a/inst/CITATION +++ b/inst/CITATION @@ -1,31 +1,54 @@ citHeader("Kindly cite the eurostat R package as follows:") +year <- sub("-.*", "", meta$Date) +vers <- paste("R package version", meta$Version) -note <- sprintf("Version %s", meta$Version) - -citEntry(entry="misc", - title = "eurostat R package", - author = personList( - person(given ="Leo", family="Lahti", email = "leo.lahti@iki.fi"), - person(given = "Janne", family="Huovari"), - person(given = "Markus", family="Kainu"), - person(given = "Przemyslaw", family="Biecek") - ), - journal = "R Journal", - volume = "9", - number = "1", - pages = "385-392", - year = "2017", - url = "https://journal.r-project.org/archive/2017/RJ-2017-019/index.html", - note = note, - textVersion = - paste("(C) Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek.", - "Retrieval and analysis of Eurostat open data with the eurostat package. R Journal 9(1):385-392, 2017.", - note, - "Package URL: http://ropengov.github.io/eurostat", - "Manuscript URL: https://journal.r-project.org/archive/2017/RJ-2017-019/index.html", - - sep=" ") - ) - +bibentry("Article", + key = "10.32614/RJ-2017-019", + title = "Retrieval and Analysis of Eurostat Open Data with the eurostat Package", + author = c( + person("Leo", "Lahti"), + person("Janne", "Huovari"), + person("Markus", "Kainu"), + person("Przemyslaw", "Biecek") + ), + journal = "The R Journal", + volume = 9, + number = 1, + pages = "385--392", + year = "2017", + doi = "10.32614/RJ-2017-019", + url = "https://doi.org/10.32614/RJ-2017-019", + textVersion = + paste("Lahti L., Huovari J., Kainu M., and Biecek P. (2017).", + "Retrieval and analysis of Eurostat open data with the eurostat package. The R Journal 9(1), pp. 385-392.", + "doi: 10.32614/RJ-2017-019", + sep = " " + ) +) +bibentry(bibtype = "Misc", + key = "eurostat", + title = "eurostat: Tools for Eurostat Open Data", + author = c( + person("Leo", "Lahti"), + person("Janne", "Huovari"), + person("Markus", "Kainu"), + person("Przemyslaw", "Biecek"), + person("Diego", "Hernangomez"), + person("Daniel", "Antal"), + person("Pyry", "Kantanen") + ), + url = "https://github.com/rOpenGov/eurostat", + type = "Computer software", + year = year, + note = vers, + textVersion = + paste0( + "Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., and Kantanen P. (", + sub("-.*", "", meta$Date), + "). eurostat: Tools for Eurostat Open Data [Computer software]. ", + vers, + ". https://github.com/rOpenGov/eurostat" + ) +) \ No newline at end of file diff --git a/inst/WORDLIST b/inst/WORDLIST new file mode 100644 index 00000000..4db0c91c --- /dev/null +++ b/inst/WORDLIST @@ -0,0 +1,130 @@ +Acknowledgements +Analytics +Biblatex +Bibtex +CNTRY +Categorises +Choropleth +DOI +Dol +EEA +EFTA +EL +EPSG +ETRS +EuroGeographics +Eurostat's +Eurozone +FID +FTE +GISCO +GeoJSON +Geospatial +Gisco +Github +Gitter +Hernangómez +JSON +Kenett +LAEA +LEVL +LazyLoad +Lehtomäki +RCV +RJ +RStudio +Recode +Recoding +Renviron +SDMX +Shmueli +SpatialPolygonDataFrame +TOC +WGS +Wietse +avia +bclust +behaviour +bibentry +codecov +codename +comext +comformity +comparativity +countrycode +countycodes +de +dieghernan +dir +dpih +eXchange +ec +efta +eg +enps +eu +europa +europe +eurozone +facto +fi +geo +geospatial +ggplot +giscoR +github +gmail +googlegroups +hclust +headtails +hostname +http +https +huovari +hv +iki +io +janne +jenks +json +kable +kainu +kapsi +kilometres +kmeans +labelled +lahti +lastTimePeriod +leo +lifecycle +markus +markuskainu +mln +num +paoa +paoc +paocc +persreg +pkm +pre +preprocessed +psmod +pstra +ptt +rOG +rOpenGov +rds +reaggregate +recode +recoding +relabelled +restatapi +rjsdmx +ropengov +rsdmx +sd +sinceTimePeriod +spplot +tibble +tmap +tran diff --git a/inst/extdata/sdmx_to_http_error_mapping.csv b/inst/extdata/sdmx_to_http_error_mapping.csv new file mode 100644 index 00000000..303fdf49 --- /dev/null +++ b/inst/extdata/sdmx_to_http_error_mapping.csv @@ -0,0 +1,11 @@ +type;sdmx.faultcode;sdmx.faultstring;http.status_code;http.status_code.label;detail +Client error;100;No results found;400;Bad request;The result from the query is empty +Client error;100;No results found;404;Not found;The requested resource is not available +Client error;140;Syntax error;400;Bad request;The query is invalid +Client error;150;Semantic error;400;Bad request;The request is syntactically correct but fails a semantic validation or violates agreed business rules +Client error;110;Unauthorized;401;Unauthorized;Authentication is needed but has failed or has not yet been provided +Client error;130;Response too large due to client request;413;Request entity too large;The request results in a response that is larger than the client is willing or able to process +Server error;500;Internal server error;500;Internal server error;The webservice returns this error code when none of the other error codes better describe the reason for the failure of the service to provide a meaningful response +Server error;501;Not implemented;501;Not implemented;The webservice has not yet implemented one of the methods defined in the API +Server error;503;Service unavailable;503;Service unavailable;The web service is temporarily unavailable because of maintenance or for some other similar reason +Server error;510;Response size exceeds service limit;413;Request entity too large;The request results in a response that is larger than the server is willing or able to process \ No newline at end of file diff --git a/inst/extras/build.cran.sh b/inst/extras/build.cran.sh index 0cc70c01..4855a53b 100755 --- a/inst/extras/build.cran.sh +++ b/inst/extras/build.cran.sh @@ -1,5 +1,4 @@ #/usr/bin/R CMD BATCH document.R -~/bin/R-3.5.1/bin/R CMD build ../../ #--no-build-vignettes -~/bin/R-3.5.1/bin/R CMD check --as-cran eurostat_3.2.93.tar.gz #--no-build-vignettes -~/bin/R-3.5.1/bin/R CMD INSTALL eurostat_3.2.93.tar.gz - +~/bin/R-4.2.2/bin/R CMD build ../../ # --no-build-vignettes +~/bin/R-4.2.2/bin/R CMD check --as-cran eurostat_3.8.2.tar.gz # --no-build-vignettes +~/bin/R-4.2.2/bin/R CMD INSTALL eurostat_3.8.2.tar.gz diff --git a/inst/extras/cheatsheet/barplot.pdf b/inst/extras/cheatsheet/barplot.pdf new file mode 100755 index 00000000..269bb7a9 Binary files /dev/null and b/inst/extras/cheatsheet/barplot.pdf differ diff --git a/inst/extras/cheatsheet/by.png b/inst/extras/cheatsheet/by.png new file mode 100755 index 00000000..c8473a24 Binary files /dev/null and b/inst/extras/cheatsheet/by.png differ diff --git a/inst/extras/cheatsheet/cheat_sheet_background.png b/inst/extras/cheatsheet/cheat_sheet_background.png new file mode 100755 index 00000000..d57ae97f Binary files /dev/null and b/inst/extras/cheatsheet/cheat_sheet_background.png differ diff --git a/inst/extras/cheatsheet/cheat_sheet_content.Rmd b/inst/extras/cheatsheet/cheat_sheet_content.Rmd new file mode 100755 index 00000000..9565edf0 --- /dev/null +++ b/inst/extras/cheatsheet/cheat_sheet_content.Rmd @@ -0,0 +1,172 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + +```{r} +here_path <- here::here() +library(extrafont) +loadfonts() +library(ggplot2) +theme_set(theme_minimal(base_family = "PT Sans")) +``` + + +R tools to access open data from Eurostat database +========================================= + +# Search and download + +Data in the Eurostat database is stored in tables. +Each table has an identifier, a short table_code, and +a description (e.g. tps00199 - Total fertility rate). + +Key eurostat functions allow to find the table_code, +download the eurostat table and polish labels in the table. + +## Find the table code + +The search_eurostat(pattern, ...) function scans the directory +of Eurostat tables and returns codes and descriptions of +tables that match pattern. + +```{r} +library("eurostat") +query <- search_eurostat(pattern = "fertility rate", + type = "table", fixed = FALSE) +query[,1:2] +## title code +## +## Total fertility rate by NUTS 2 region tgs00100 +## Total fertility rate tps00199 +## Total fertility rate by NUTS 2 region tgs00100 +``` + + +## Download the table + +The get_eurostat(id, time_format = "date", filters = "none", +type = "code", cache = TRUE, ...) function downloads the +requested table from the Eurostat bulk download facility or +from The Eurostat Web Services JSON API (if filters are defined). +Downloaded data is cached (if cache=TRUE). +Additional arguments define how to read the time column +(time_format) and if table dimensions shall be kept as +codes or converted to labels (type). + +```{r} +ct <- c("AT","BE","BG","CH","CY","CZ","DE","DK","EE","EL","ES", + "FI","FR","HR","HU","IE","IS","IT","LI","LT","LU","LV", + "MT","NL","NO","PL","PT","RO","SE","SI","SK","UK") +dat <- get_eurostat(id="tps00199", time_format="num", + filters = list(geo = ct)) +dat[1:2,] +## indic_de geo time values +## TOTFERRT AT 2006 1.41 +## TOTFERRT AT 2007 1.38 +``` + +## Add labels + +The label_eurostat(x, lang = "en", ...) gets definitions for Eurostat +codes and replace them with labels in given language ("en", "fr" or "de") + +```{r} +dat <- label_eurostat(dat) +dat[1:3,] +## indic_de geo time values +## +## Total fertility rate Austria 2006 1.41 +## Total fertility rate Austria 2007 1.38 +## Total fertility rate Austria 2008 1.42 +``` + + +## eurostat and plots + +The get_eurostat() function returns tibbles in the long format. Packages +dplyr and tidyr are well suited to transform these objects. The ggplot2 +package is well suited to plot these objects. + +```{r} +dat <- get_eurostat(id="tps00199", filters = list(geo = ct)) +library(ggplot2) +library(dplyr) +ggplot(dat, + aes(x = time, y = values, color = geo, label = geo)) + + geom_line(alpha = .5) + + geom_text(data = dat %>% group_by(geo) %>% + filter(time == max(time)), + size = 2.6) + + theme(legend.position = "none") + + labs(title = "Total fertility rate, 2006-2017", + x = "Year", y = "%") -> p +# save plot +ggsave(filename = glue::glue("{here_path}/inst/extras/cheatsheet/lineplot.pdf"),plot = p, width = 4.84, height = 3.26, device = cairo_pdf) +``` + +```{r} +dat_2015 <- dat %>% + filter(time == "2015-01-01") +ggplot(dat_2015, aes(x = reorder(geo, values), y = values)) + + geom_col(color = "white", fill = "grey80") + + theme(axis.text.x = element_text(size = 6)) + + labs(title = "Total fertility rate, 2015", + y = "%", x = NULL) -> p +# save plot +ggsave(filename = glue::glue("{here_path}/inst/extras/cheatsheet/barplot.pdf"),plot = p, width = 4.84, height = 3.26, device = cairo_pdf) +``` + + +# eurostat and maps + +## Fetch and process data + +There are three function to work with geospatial data from GISCO. The +get_eurostat_geospatial() returns spatial data as sf-object. +Object can me merged with data.frames using dplyr::*_join()-functions. +The cut_to_classes() is a wrapper for cut() - function and +is used for categorizing data for maps with tidy labels. + +```{r} +mapdata <- get_eurostat_geospatial(nuts_level = 0) %>% + right_join(dat_2015) %>% + mutate(cat = cut_to_classes(values, n=4, decimals=1)) +head(select(mapdata,geo,values,cat), 3) +## geo values cat geometry +## AT 1.49 1.5 ~< 1.6 MULTIPOLYGON (((15.54245 48... +## BE 1.70 1.6 ~< 1.8 MULTIPOLYGON (((5.10218 51.... +## BG 1.53 1.5 ~< 1.6 MULTIPOLYGON (((22.99717 43... +``` + + +## Plot a map + +The sf-object returned are ready to be plotted with +ggplot::geom_sf()-function. + +```{r} +ggplot(mapdata, aes(fill = cat)) + + scale_fill_brewer(palette = "RdYlBu") + + geom_sf(color = alpha("white",1/3), alpha = .6) + + xlim(c(-12,37)) + ylim(c(35,70)) + + labs(title = "Total fertility rate, 2015", + subtitle = "Avg. number of life births per woman", + fill = "%") -> p +# save plot +ggsave(filename = glue::glue("{here_path}/inst/extras/cheatsheet/mapplot.pdf"),plot = p, width = 5.14, height = 4.26, device = cairo_pdf) +``` + +******* + +This onepager presents the eurostat package 2014-2019 +Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek +package version 3.3.55 URL: https://github.com/rOpenGov/eurostat + +Retrieval and Analysis of Eurostat Open Data with the eurostat Package. +Leo Lahti, Janne Huovari, Markus Kainu, and Przemysław Biecek. +The R Journal, 9(1):385–392, 2017. + +CC BY Przemyslaw Biecek & Markus Kainu +https://creativecommons.org/licenses/by/4.0/ diff --git a/inst/extras/cheatsheet/cheatsheet2019.pdf b/inst/extras/cheatsheet/cheatsheet2019.pdf new file mode 100755 index 00000000..137d50a2 Binary files /dev/null and b/inst/extras/cheatsheet/cheatsheet2019.pdf differ diff --git a/inst/extras/cheatsheet/cheatsheet2019.svg b/inst/extras/cheatsheet/cheatsheet2019.svg new file mode 100755 index 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Downloaded data is cached (if cache=TRUE). Additional arguments define how to read the time column(time_format) and if table dimensions shall be kept as codes or converted to labels (type). + ct <- c("AT","BE","BG","CH","CY","CZ","DE","DK","EE","EL","ES", "FI","FR","HR","HU","IE","IS","IT","LI","LT","LU","LV", "MT","NL","NO","PL","PT","RO","SE","SI","SK","UK")dat <- get_eurostat(id="tps00199", time_format="num", filters = list(geo = ct))dat[1:2,]## indic_de geo time values## TOTFERRT AT 2006 1.41## TOTFERRT AT 2007 1.38 + Add labelsThe label_eurostat(x, lang = "en", ...) gets definitions for Eurostatcodes and replace them with labels in given language ("en", "fr" or "de") + dat <- label_eurostat(dat)dat[1:3,]## indic_de geo time values## <fct> <fct> <dbl> <dbl>## Total fertility rate Andorra 2006 1.24## Total fertility rate Albania 2006 1.67## Total fertility rate Armenia 2006 1.34 + Search and download + eurostat and plots + The get_eurostat() function returns tibbles in the long format. Packagesdplyr and tidyr are well suited to transform these objects. The ggplot2-package is well suited to plot these objects. + dat <- get_eurostat(id="tps00199", filters = list(geo = ct))library(ggplot2)library(dplyr)ggplot(dat, aes(x = time, y = values, color = geo, label = geo)) + geom_line(alpha = .5) + geom_text(data = dat %>% group_by(geo) %>% filter(time == max(time)), size = 2.6) + theme(legend.position = "none") + labs(title = "Total fertility rate, 2006-2017", x = "Year", y = "%") + dat_2015 <- dat %>% filter(time == "2015-01-01") ggplot(dat_2015, aes(x = reorder(geo, values), y = values)) + geom_col(color = "white", fill = "grey80") + theme(axis.text.x = element_text(size = 6)) + labs(title = "Total fertility rate, 2015", y = "%", x = NULL) + eurostat and maps + There are two function to work with geospatial data from GISCO. Theget_eurostat_geospatial() returns spatial data as sf-object. Object can me merged with data.frames using dplyr::*_join()-functions.The cut_to_classes() is a wrapper for cut() - function and is used for categorizing data for maps with tidy labels. + mapdata <- get_eurostat_geospatial(nuts_level = 0) %>% right_join(dat_2015) %>% mutate(cat = cut_to_classes(values, n=4, decimals=1))head(select(mapdata,geo,values,cat), 3)## geo values cat geometry## AT 1.49 1.5 ~< 1.6 MULTIPOLYGON (((15.54245 48...## BE 1.70 1.6 ~< 1.8 MULTIPOLYGON (((5.10218 51....## BG 1.53 1.5 ~< 1.6 MULTIPOLYGON (((22.99717 43... + Plot a Map The sf-object returned are ready to be plotted with ggplot::geom_sf()-function. + ggplot(mapdata, aes(fill = cat)) + scale_fill_brewer(palette = "RdYlBu") + geom_sf(color = alpha("white",1/3), alpha = .6) + xlim(c(-12,44)) + ylim(c(35,70)) + labs(title = "Total fertility rate, 2015", subtitle = "Avg. number of life births per woman", fill = "%") + CC BY Przemyslaw Biecek & Markus Kainu 2019-11-06https://creativecommons.org/licenses/by/4.0/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 0.0 + 0.5 + 1.0 + 1.5 + 2.0 + + + PTCYPLELESITMTHRLISKHULUATDEBGCHCZSIEEROFINLBELTLVDKNOISUKIESEFR + + + % + + + Total fertility rate, 2015 + + + + This onepager presents the eurostat package 2014-2019 Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek package version 3.3.55 URL: https://github.com/rOpenGov/eurostatRetrieval and Analysis of Eurostat Open Data with the eurostat Package. Leo Lahti, Janne Huovari, Markus Kainu, and Przemysław Biecek. The R Journal, 9(1):385–392, 2017. + + + + diff --git a/inst/extras/cheatsheet/lineplot.pdf b/inst/extras/cheatsheet/lineplot.pdf new file mode 100755 index 00000000..b616b373 Binary files /dev/null and b/inst/extras/cheatsheet/lineplot.pdf differ diff --git a/inst/extras/cheatsheet/logo.png b/inst/extras/cheatsheet/logo.png new file mode 100755 index 00000000..f785f2f8 Binary files /dev/null and b/inst/extras/cheatsheet/logo.png differ diff --git a/inst/extras/cheatsheet/mapplot.pdf b/inst/extras/cheatsheet/mapplot.pdf new file mode 100755 index 00000000..ec42f50d Binary files /dev/null and b/inst/extras/cheatsheet/mapplot.pdf differ diff --git a/inst/extras/document.R b/inst/extras/document.R index 84505df0..73a3bfe1 100755 --- a/inst/extras/document.R +++ b/inst/extras/document.R @@ -5,25 +5,29 @@ library(devtools) document("../../") #build("../../") #build(vignettes = FALSE) +# devtools::install(build_vignettes = TRUE) #check("../../", vignettes = FALSE) #install() -library(knitr) -knit(input = "../../README.Rmd", output = "../../README.md") +library(rmarkdown) +render(input = "../../README.Rmd", output_format = "md_document") # build the ../../docs directory as a pkg website -library(pkgdown) -setwd("../../") -build_site() +#library(pkgdown) +#setwd("../../") +#build_site() +# devtools::build_vignettes() + # Submissions: # # release() # Submit to CRAN +# devtools::check_win_devel("../../") # Windows check # submit_cran() # Submit to CRAN without all release() questions +# use_cran_comments() # Utilities: # -# build_win("../../") # Windows check # revdep_check("../../") # add_rstudio_project("../../") # use_build_ignore("../NEWS.md", pkg = "../../") # NEWS.md not supported by CRAN @@ -32,14 +36,20 @@ build_site() # test() # Run tests # run_examples() +# Add Github actions (Travis replacement) +#usethis::use_github_action_check_standard() +# -> .github/workflows/.. +# See https://github.com/r-lib/actions + # Vignettes: # # library(knitr) # knit("../../vignettes/eurostat_tutorial.Rmd", "../../vignettes/eurostat_tutorial.md") # or run main.R in vignettes -setwd("vignettes/") -source("main.R") -tools::compactPDF("./", gs_quality = "ebook") +#setwd("vignettes/") +#source("main.R") +# system("cp vignette.html ../inst/doc/") +# tools::compactPDF("./", gs_quality = "ebook") diff --git a/inst/extras/eurostat_description.md b/inst/extras/eurostat_description.md new file mode 100755 index 00000000..47f9759d --- /dev/null +++ b/inst/extras/eurostat_description.md @@ -0,0 +1,5 @@ +The eurostat R package is a mature collection of algorithms that facilitate efficient retrieval, analysis and visualization of data from Eurostat open data portal. The package has emerged from international non-profit collaboration between academic researchers and R developers. + +While we are backing up our work by pragmatic case studies and demonstrations, the overall scope of our contribution is wider and it opens up new opportunities for data scientists. R is one of the most popular open statistical programming and data science environments currently and it is widely used in research, government, data journalism, and industry. Our proposed eurostat R package is facilitating access to the European open data resources. Its popularity among the data science community has been steadily growing, with 50% increase in annual downloads since the first version in 2015. The present mature version of the package was formally published in R Journal in 2018. It is now available in the CRAN software repository and has found a considerable number of users in research and government, with roughly 20,000 downloads in 2018. + +The package provides practically tested open source tools that allow seamless integration of the eurostat open data resources and complementary data sources in open statistical environments where the data can be analyzed with the latest research algorithms. We have developed various case studies that demonstrate how these tools can facilitate the development and sharing of reproducible and transparent statistical workflows and provide insights into European demographics. Therefore, instead of a particular case study or demonstration, our main proposed innovation is the development of general-purpose algorithmic tools that provide enhanced opportunities for the utilization of European open data resources. diff --git a/inst/extras/sticker.R b/inst/extras/sticker.R new file mode 100755 index 00000000..88b90d54 --- /dev/null +++ b/inst/extras/sticker.R @@ -0,0 +1,15 @@ +# https://github.com/GuangchuangYu/hexSticker + +library(hexSticker) +library(ggplot2) + +#p <- ggplot(aes(x = mpg, y = wt), data = mtcars) + geom_point() +#p <- p + theme_void() + theme_transparent() +#png("estat.png"); print(p); dev.off() + +imgurl <- "estat.png" + +sticker(imgurl, package="eurostat", + p_size=8, s_x=1, s_y=.75, s_width=1.3, s_height=1, + filename="eurostat.png") + diff --git a/man/add_nuts_level.Rd b/man/add_nuts_level.Rd new file mode 100755 index 00000000..a231b31f --- /dev/null +++ b/man/add_nuts_level.Rd @@ -0,0 +1,54 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/deprecated_regions_functions.R +\name{add_nuts_level} +\alias{add_nuts_level} +\title{Add the statistical aggregation level to data frame} +\usage{ +add_nuts_level(dat, geo_labels = "geo") +} +\arguments{ +\item{dat}{A data frame or tibble returned by \code{\link[=get_eurostat]{get_eurostat()}}.} + +\item{geo_labels}{A geographical label, defaults to \code{geo}.} +} +\value{ +a new numeric variable nuts_level with the numeric value of +NUTS level 0 (country), 1 (greater region), +2 (region), 3 (small region). +} +\description{ +Eurostat regional statistics contain country, and various +regional level information. In many cases, for example, when mapping, +it is useful to filter out national level data from NUTS2 level regional +data, for example. + +This function will be deprecated. Use the more comprehensive +\verb{[regions::validate_nuts_regions()]} instead. +} +\details{ +DEPRECATED FUNCTIONS FOR BACKWARD COMPATIBILITY +FUNCTIONS GIVE WARNING AND CALL APPROPRIATE regions FUNCTIONS +} +\examples{ + +dat <- data.frame( + geo = c("FR", "IE04", "DEB1C"), + values = c(1000, 23, 12) +) + +add_nuts_level(dat) +} +\seealso{ +\code{\link[regions:validate_nuts_regions]{regions::validate_nuts_regions()}} + +Other regions functions: +\code{\link{harmonize_geo_code}()}, +\code{\link{recode_to_nuts_2013}()}, +\code{\link{recode_to_nuts_2016}()}, +\code{\link{reexports}} +} +\author{ +Daniel Antal +} +\concept{regions functions} +\keyword{internal} diff --git a/man/check_access_to_data.Rd b/man/check_access_to_data.Rd new file mode 100755 index 00000000..836794f6 --- /dev/null +++ b/man/check_access_to_data.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/check_access_to_data.R +\name{check_access_to_data} +\alias{check_access_to_data} +\title{Check access to ec.europe.eu} +\usage{ +check_access_to_data() +} +\value{ +a logical. +} +\description{ +Check if R has access to resources at http://ec.europa.eu +} +\examples{ +\donttest{ +check_access_to_data() +} + +} +\author{ +Markus Kainu \href{mailto:markus.kainu@kapsi.fi}{markus.kainu@kapsi.fi} +} diff --git a/man/clean_eurostat_cache.Rd b/man/clean_eurostat_cache.Rd index 18d87996..7c6b1418 100755 --- a/man/clean_eurostat_cache.Rd +++ b/man/clean_eurostat_cache.Rd @@ -4,19 +4,29 @@ \alias{clean_eurostat_cache} \title{Clean Eurostat Cache} \usage{ -clean_eurostat_cache(cache_dir = NULL) +clean_eurostat_cache(cache_dir = NULL, config = FALSE) } \arguments{ \item{cache_dir}{A path to cache directory. If \code{NULL} (default) tries to clean default temporary cache directory.} + +\item{config}{Logical \code{TRUE/FALSE}. Should the cached path be deleted?} } \description{ Delete all .rds files from the eurostat cache directory. - See \code{\link{get_eurostat}} for more on cache. +See \code{\link[=get_eurostat]{get_eurostat()}} for more on cache. } \examples{ -clean_eurostat_cache() +\dontrun{ +clean_eurostat_cache() +} +} +\seealso{ +Other cache utilities: +\code{\link{set_eurostat_cache_dir}()} } \author{ -Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu \email{ropengov-forum@googlegroups.com} \url{http://github.com/ropengov/eurostat} +Przemyslaw Biecek, Leo Lahti, Janne Huovari, Markus Kainu and +Diego Hernangómez } +\concept{cache utilities} diff --git a/man/convert_time_col.Rd b/man/convert_time_col.Rd index 8cdd0ddf..5f48f6e0 100755 --- a/man/convert_time_col.Rd +++ b/man/convert_time_col.Rd @@ -2,14 +2,15 @@ % Please edit documentation in R/tidy_eurostat.R \name{convert_time_col} \alias{convert_time_col} -\title{Time Column Conversions} +\title{Time Column Conversions for data from new dissemination API} \usage{ convert_time_col(x, time_format) } \arguments{ -\item{x}{A time column (vector)} +\item{x}{A time column (vector) from a downloaded dataset} -\item{time_format}{see \code{\link{tidy_eurostat}}} +\item{time_format}{one of the following: \code{date}, \code{date_last}, or \code{num}. +See \code{\link[=tidy_eurostat]{tidy_eurostat()}} for more information.} } \description{ Internal function to convert time column. diff --git a/man/cut_to_classes.Rd b/man/cut_to_classes.Rd index 7e9d5770..eb34cc09 100755 --- a/man/cut_to_classes.Rd +++ b/man/cut_to_classes.Rd @@ -4,16 +4,23 @@ \alias{cut_to_classes} \title{Cuts the Values Column into Classes and Polishes the Labels} \usage{ -cut_to_classes(x, n = 5, style = "equal", manual = FALSE, - manual_breaks = NULL, decimals = 0, nodata_label = "No data") +cut_to_classes( + x, + n = 5, + style = "equal", + manual = FALSE, + manual_breaks = NULL, + decimals = 0, + nodata_label = "No data" +) } \arguments{ -\item{x}{A numeric vector, eg. \code{values} variable in data returned by \code{get_eurostat}} +\item{x}{A numeric vector, eg. \code{values} variable in data returned by +\code{\link[=get_eurostat]{get_eurostat()}}.} \item{n}{A numeric. number of classes/categories} -\item{style}{Chosen style: one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", -"bclust", "fisher", or "jenks"} +\item{style}{chosen style: one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", "bclust", "fisher", "jenks", "dpih", "headtails", "maximum", or "box"} \item{manual}{Logical. If manual breaks are being used} @@ -27,15 +34,29 @@ cut_to_classes(x, n = 5, style = "equal", manual = FALSE, a factor. } \description{ -Categorises a numeric vector into automatic or manually defined categories. -and polishes the labels ready for used in mapping with \code{merge_with_geodata} function and ggplot2. +Categorises a numeric vector into automatic or manually defined +categories and polishes the labels ready for used in mapping with \code{ggplot2}. } \examples{ - \dontrun{ - lp <- get_eurostat("nama_aux_lp") - lp$class <- cut_to_classes(lp$values, n=5, style="equal", decimals=1) - } +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\donttest{ +# lp <- get_eurostat("nama_aux_lp") +lp <- get_eurostat("nama_10_lp_ulc") +lp$class <- cut_to_classes(lp$values, n = 5, style = "equal", decimals = 1) +} +\dontshow{\}) # examplesIf} +} +\seealso{ +\code{\link[classInt:classIntervals]{classInt::classIntervals()}} + +Other helpers: +\code{\link{dic_order}()}, +\code{\link{eurotime2date}()}, +\code{\link{eurotime2num}()}, +\code{\link{harmonize_country_code}()}, +\code{\link{label_eurostat}()} } \author{ -Markus Kainu +Markus Kainu \href{mailto:markuskainu@gmail.com}{markuskainu@gmail.com} } +\concept{helpers} diff --git a/man/dic_order.Rd b/man/dic_order.Rd index 0744be59..3ba550f8 100755 --- a/man/dic_order.Rd +++ b/man/dic_order.Rd @@ -9,9 +9,9 @@ dic_order(x, dic, type) \arguments{ \item{x}{a variable (code or labelled) to get order for.} -\item{dic}{a name of the dictionary. Correspond a variable name in the -data_frame from \code{\link{get_eurostat}}. Can be also data_frame from -\code{\link{get_eurostat_dic}}.} +\item{dic}{a name of the dictionary. Correspond a variable name in the +data_frame from \code{\link[=get_eurostat]{get_eurostat()}}. Can be also data_frame from +\code{\link[=get_eurostat_dic]{get_eurostat_dic()}}.} \item{type}{a type of the x. Could be \code{code} or \code{label}.} } @@ -22,8 +22,21 @@ A numeric vector of orders. Orders the factor levels. } \details{ -Some variables, like classifications, have logical or conventional ordering. Eurostat data tables are nor neccessary ordered in this order. The function \code{dic_order} get the ordering from Eurostat classifications dictionaries. The function \code{\link{label_eurostat}} can also order factor levels of labels with argument \code{eu_order = TRUE}. +Some variables, like classifications, have logical or conventional +ordering. Eurostat data tables are nor necessary ordered in this order. +The function \code{\link[=dic_order]{dic_order()}} get the ordering from Eurostat classifications +dictionaries. The function \code{\link[=label_eurostat]{label_eurostat()}} can also order factor levels +of labels with argument \code{eu_order = TRUE}. +} +\seealso{ +Other helpers: +\code{\link{cut_to_classes}()}, +\code{\link{eurotime2date}()}, +\code{\link{eurotime2num}()}, +\code{\link{harmonize_country_code}()}, +\code{\link{label_eurostat}()} } \author{ -Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu \email{ropengov-forum@googlegroups.com} \url{http://github.com/ropengov/eurostat} +Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu } +\concept{helpers} diff --git a/man/eu_countries.Rd b/man/eu_countries.Rd index a82498dd..e7112547 100755 --- a/man/eu_countries.Rd +++ b/man/eu_countries.Rd @@ -7,14 +7,23 @@ \alias{efta_countries} \alias{eu_candidate_countries} \title{Countries and Country Codes} -\format{A data_frame: -\describe{ - \item{code}{Country code in the Eurostat database} - \item{name}{Country name in English} -}} +\format{ +A data_frame: +\itemize{ +\item \strong{code}: Country code in the Eurostat database. +\item \strong{name}: Country name in English. +\item \strong{label}: Country name in the Eurostat database. +} + +An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 19 rows and 3 columns. + +An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 4 rows and 3 columns. + +An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 7 rows and 3 columns. +} \source{ -\url{http://ec.europa.eu/eurostat/statistics-explained/index.php/Tutorial:Country_codes_and_protocol_order}, - \url{http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Euro_area} +\url{https://ec.europa.eu/eurostat/statistics-explained/index.php/Tutorial:Country_codes_and_protocol_order}, +\url{https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Euro_area} } \usage{ eu_countries @@ -29,4 +38,10 @@ eu_candidate_countries Countries and country codes in EU, Euro area, EFTA and EU candidate countries. } +\seealso{ +Other datasets: +\code{\link{eurostat_geodata_60_2016}}, +\code{\link{tgs00026}} +} +\concept{datasets} \keyword{datasets} diff --git a/man/eurostat-defunct.Rd b/man/eurostat-defunct.Rd new file mode 100644 index 00000000..ac0e69e1 --- /dev/null +++ b/man/eurostat-defunct.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/eurostat-defunct.R +\name{eurostat-defunct} +\alias{eurostat-defunct} +\alias{grepEurostatTOC} +\title{Defunct functions in eurostat} +\usage{ +grepEurostatTOC(...) +} +\arguments{ +\item{...}{Generic representation of old arguments} +} +\description{ +This list of defunct functions is maintained to document changes to eurostat functions in a +transparent manner. +} +\details{ +The following functions are defunct: + +\itemize{ +\item \code{\link{grepEurostatTOC}}: Use \code{search_eurostat} instead +} +} diff --git a/man/eurostat-package.Rd b/man/eurostat-package.Rd index f20ae381..19643fc5 100755 --- a/man/eurostat-package.Rd +++ b/man/eurostat-package.Rd @@ -6,28 +6,392 @@ \alias{eurostat} \title{R Tools for Eurostat open data} \description{ -Brief summary of the eurostat package +Tools to download data from the Eurostat database +\url{https://ec.europa.eu/eurostat} together with search and manipulation +utilities. } \details{ \tabular{ll}{ -Package: \tab eurostat\cr -Type: \tab Package\cr -Version: \tab See sessionInfo() or DESCRIPTION file\cr -Date: \tab 2014-2017\cr -License: \tab BSD_2_clause + LICENSE\cr -LazyLoad: \tab yes\cr + \tab \cr + \strong{Package} \tab eurostat \cr + \strong{Type} \tab Package \cr + \strong{Version} \tab 4.0.0 \cr + \strong{Date} \tab 2014-2023 \cr + \strong{License} \tab BSD_2_clause + file LICENSE \cr + \strong{LazyLoad} \tab yes \cr } +} +\section{Eurostat}{ +Eurostat website: \url{https://ec.europa.eu/eurostat} +Eurostat database: \url{https://ec.europa.eu/eurostat/web/main/data/database} + +Information about the data update schedule from Eurostat: +"Eurostat datasets are updated twice a day at 11:00 and 23:00 CET, if newer +data is available or for structural changes, for example for the +dimensions in the dataset. + +The Eurostat database always contains the latest version of the datasets, +meaning that there is no versioning or documentation of past versions of +the data." +} + +\section{Data source: Eurostat SDMX 2.1 Dissemination API}{ +Data is downloaded from Eurostat SDMX 2.1 API endpoint +as compressed TSV files that are transformed into tabular format. +See Eurostat documentation for more information: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+data+query} + +The new dissemination API replaces the old bulk download facility that was +used by Eurostat before October 2023 and by the eurostat R package versions +before 4.0.0. +See Eurostat documentation about the transition from Bulk Download to API +for more information about the differences between the old bulk download +facility and the data provided by the new API connection: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/Transition+-+from+Eurostat+Bulk+Download+to+API} + +See especially the document Migrating_to_API_TSV.pdf that describes the +changes in TSV file format in new applications. + +For more information about SDMX 2.1, see SDMX standards: Section 7: +Guidelines for the use of web services, Version 2.1: +\url{https://sdmx.org/wp-content/uploads/SDMX_2-1_SECTION_7_WebServicesGuidelines.pdf} +} + +\section{Disclaimer: Availability of filtering functionalities}{ +Currently it only possible to download filtered data through API Statistics +(JSON API) when using \code{eurostat} package, although technically filtering +datasets downloaded through the SDMX Dissemination API is also supported by +Eurostat. We may support this feature in the future. In the meantime, if you +are interested in filtering Dissemination API data queries manually, please +consult the following Eurostat documentation: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+data+filtering} +} + +\section{Data source: Eurostat API Statistics (JSON API)}{ +Data is downloaded from Eurostat API Statistics. See Eurostat documentation +for more information about data queries in API Statistics +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query} + +This replaces the old JSON Web Services that was used by Eurostat before +February 2023 and by the eurostat R package versions before 3.7.13. +See Eurostat documentation about the migration from JSON web service to API +Statistics for more information about the differences between the old and +the new service: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+migrating+from+JSON+web+service+to+API+Statistics} + +For easily viewing which filtering options are available - in addition to +the default ones, time and language - Eurostat Web services Query builder +tool may be useful: +\url{https://ec.europa.eu/eurostat/web/query-builder} +} + +\section{Filtering datasets}{ +When using Eurostat API Statistics (JSON API), datasets can be filtered +before they are downloaded and saved in local memory. The general format +for filter parameters is \verb{=}. + +Filter parameters are optional but the used dimension codes must be present +in the data product that is being queried. Dimension codes can +vary between different data products so it may be useful to examine new +datasets in Eurostat data browser beforehand. However, most if not all +Eurostat datasets concern European countries and contain information that +was gathered at some point in time, so \code{geo} and \code{time} dimension codes +can usually be used. + +\verb{} and \verb{} are case-insensitive and they can be written +in lowercase or uppercase in the query. + +Parameters are passed onto the \code{eurostat} package functions \code{\link[=get_eurostat]{get_eurostat()}} +and \code{\link[=get_eurostat_json]{get_eurostat_json()}} as a list item. If an individual item contains +multiple items, as it often can be in the case of \code{geo} parameters and +other optional items, they must be in the form of a vector: \code{c("FI", "SE")}. +For examples on how to use these parameters, see function examples below. +\subsection{Time parameters}{ + +\code{time} and \code{time_period} address the same \code{TIME_PERIOD} dimension in the +dataset and can be used interchangeably. In the Eurostat documentation +it is stated that "Using more than one Time parameter in the same query +is not accepted", but practice has shown that actually Eurostat API allows +multiple \code{time} parameters in the same query. This makes it possible to +use R colon operator when writing queries, so \code{time = c(2015:2018)} +translates to \verb{&time=2015&time=2016&time=2017&time=2018}. + +The only exception +to this is when the queried dataset contains e.g. quarterly data and +\code{TIME_PERIOD} is saved as \code{2015-Q1}, \code{2015-Q2} etc. Then it is possible +to use \code{time=2015-Q1&time=2015-Q2} style in the query URL, but this makes it +unfeasible to use the colon operator and requires a lot of manual typing. + +Because of this, it is useful to know about other time parameters as well: +\itemize{ +\item \code{untilTimePeriod}: return dataset items from the oldest record up until the +set time, for example "all data until 2000": \code{untilTimePeriod = 2000} +\item \code{sinceTimePeriod}: return dataset items starting from set time, for example +"all datastarting from 2008": \code{sinceTimePeriod = 2008} +\item \code{lastTimePeriod}: starting from the most recent time period, how many +preceding time periods should be returned? For example 10 most +recent observations: \code{lastTimePeriod = 10} +} + +Using both \code{untilTimePeriod} and \code{sinceTimePeriod} parameters in the same +query is allowed, making the usage of the R colon operator unnecessary. +In the case of quarterly data, using \code{untilTimePeriod} and \code{sinceTimePeriod} +parameters also works, as opposed to the colon operator, so it is generally +safer to use them as well. +} + +\subsection{Other dimensions}{ + +In \code{\link[=get_eurostat_json]{get_eurostat_json()}} examples \code{nama_10_gdp} dataset is filtered with +two additional filter parameters: +\itemize{ +\item \code{na_item = "B1GQ"} +\item \code{unit = "CLV_I10"} +} + +Filters like these are most likely unique to the \code{nama_10_gdp} dataset +(or other datasets within the same domain) and should +not be used with others dataset without user discretion. +By using \code{\link[=label_eurostat]{label_eurostat()}} we know that \code{"B1GQ"} stands for +"Gross domestic product at market prices" and +\code{"CLV_I10"} means "Chain linked volumes, index 2010=100". + +Different dimension codes can be translated to a natural language by using +the \code{\link[=get_eurostat_dic]{get_eurostat_dic()}} function, which returns labels for individual +dimension items such as \code{na_item} and \code{unit}, as opposed to +\code{\link[=label_eurostat]{label_eurostat()}} which does it for whole datasets. For example, the +parameter \code{na_item} stands for "National accounts indicator (ESA 2010)" and +\code{unit} stands for "Unit of measure". +} + +\subsection{Language}{ + +All datasets have metadata available in English, French and German. If no +parameter is given, the labels are returned in English. + +Example: +\itemize{ +\item \code{lang = "fr"} +} +} + +\subsection{More information}{ + +For more information about data filtering see Eurostat documentation +on API Statistics: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query#APIStatisticsdataquery-TheparametersdefinedintheRESTrequest} +} +} + +\section{Data source: Eurostat Table of Contents}{ +The Eurostat Table of Contents (TOC) is downloaded from +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=en} +(default) or from French or German language variants: +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=fr} +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=de} + +See Eurostat documentation on TOC items: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+-+Detailed+guidelines+-+Catalogue+API+-+TOC} +} + +\section{Data source: GISCO - General Copyright}{ +"Eurostat's general copyright notice and licence policy is applicable and +can be consulted here: +\url{https://ec.europa.eu/eurostat/about-us/policies/copyright} -R Tools for Eurostat Open Data +Please also be aware of the European Commission's general conditions: +\url{https://commission.europa.eu/legal-notice_en} + +Moreover, there are specific provisions applicable to some of the following +datasets available for downloading. The download and usage of these data +is subject to their acceptance: +\itemize{ +\item Administrative Units / Statistical Units +\item Population distribution / Demography +\item Transport Networks +\item Land Cover +\item Elevation (DEM)" +} + +Of the abovementioned datasets, Administrative Units / Statistical Units +is applicable if the user wants to draw maps with borders provided by +GISCO / EuroGeographics. +} + +\section{Data source: GISCO - Administrative Units / Statistical Units}{ +The following copyright notice is provided for end user convenience. +Please check up-to-date copyright information from the GISCO website: +\href{https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units}{GISCO: Geographical information and maps - Administrative units/statistical units} + +"In addition to the \href{https://ec.europa.eu/eurostat/web/main/about/policies/copyright}{general copyright and licence policy} applicable to the whole Eurostat website, the following +specific provisions apply to the datasets you are downloading. The download +and usage of these data is subject to the acceptance of the following +clauses: +\enumerate{ +\item The Commission agrees to grant the non-exclusive and not transferable +right to use and process the Eurostat/GISCO geographical data downloaded +from this page (the "data"). +\item The permission to use the data is granted on condition that: +\enumerate{ +\item the data will not be used for commercial purposes; +\item the source will be acknowledged. A copyright notice, as specified +below, will have to be visible on any printed or electronic publication +using the data downloaded from this page." +} +} +\subsection{Copyright notice}{ + +When data downloaded from this page is used in any printed or electronic +publication, in addition to any other provisions applicable to the whole +Eurostat website, data source will have to be acknowledged in the legend of +the map and in the introductory page of the publication with the following +copyright notice: + +EN: © EuroGeographics for the administrative boundaries + +FR: © EuroGeographics pour les limites administratives + +DE: © EuroGeographics bezüglich der Verwaltungsgrenzen + +For publications in languages other than English, French or German, the +translation of the copyright notice in the language of the publication shall +be used. + +If you intend to use the data commercially, please contact EuroGeographics +for information regarding their licence agreements." +} +} + +\section{Eurostat: Copyright notice and free re-use of data}{ +The following copyright notice is provided for end user convenience. +Please check up-to-date copyright information from the eurostat website: +\url{https://ec.europa.eu/eurostat/about-us/policies/copyright} + +"(c) European Union, 1995 - today + +Eurostat has a policy of encouraging free re-use of its data, both for +non-commercial and commercial purposes. All statistical data, metadata, +content of web pages or other dissemination tools, official publications +and other documents published on its website, with the exceptions listed +below, can be reused without any payment or written licence provided that: +\itemize{ +\item the source is indicated as Eurostat; +\item when re-use involves modifications to the data or text, this must be +stated clearly to the end user of the information." +} + +For exceptions to the abovementioned principles see +\href{https://ec.europa.eu/eurostat/about-us/policies/copyright}{Eurostat website} +} + +\section{Citing Eurostat data}{ +For citing datasets, use \code{\link[=get_bibentry]{get_bibentry()}} to build a bibliography that +is suitable for your reference manager of choice. + +When using Eurostat data in other contexts than academic publications that +in-text citations or footnotes/endnotes, the following guidelines may be +helpful: +\itemize{ +\item The origin of the data should always be mentioned as "Source: Eurostat". +\item The online dataset codes(s) should also be provided in order to ensure +transparency and facilitate access to the Eurostat data and related +methodological information. For example: +"Source: Eurostat (online data code: namq_10_gdp)" +\item Online publications (e.g. web pages, PDF) should include a clickable +link to the dataset using the bookmark functionality available in the +Eurostat data browser. +} + +It should be avoided to associate different entities (e.g. Eurostat, +National Statistical Offices, other data providers) to the same dataset or +indicator without specifying the role of each of them in the treatment of +data. + +See also section "Eurostat: Copyright notice and free re-use of data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. +} + +\section{Strategies for handling large datasets more efficiently}{ +Most Eurostat datasets are relatively manageable, at least on a machine +with 16 GB of RAM. The largest dataset in Eurostat database, at the time +of writing this, had 148362539 (148 million) values, which results in an +object with 148 million rows in tidy data (long) format. The test machine +with 16 GB of RAM was able to handle the second largest dataset in the +database with 91 million values (rows). + +There are still some methods to make data fetching +functions perform faster: +\itemize{ +\item turn caching off: \code{get_eurostat(cache = FALSE)} +\item turn cache compression off (may result in rather large cache files!): +\code{get_eurostat(compress_file = FALSE)} +\item if you want faster caching with manageable file sizes, use stringsAsFactors: +\code{get_eurostat(cache = TRUE, compress_file = TRUE, stringsAsFactors = TRUE)} +\item Use faster data.table functions: \code{get_eurostat(use.data.table = TRUE)} +\item Keep column processing to a minimum: +\code{get_eurostat(time_format = "raw", type = "code")} etc. +\item Read \code{get_eurostat()} function documentation carefully so you understand +what different arguments do +\item Filter the dataset so that you fetch only the parts you need! +} +} + +\section{regions functions}{ + +For working with sub-national statistics the basic functions of the +regions package are imported \url{https://regions.dataobservatory.eu/}. } + \examples{ library(eurostat) } \references{ -See citation("eurostat") -\url{https://ropengov.github.io/eurostat} +See \code{citation("eurostat")}: + +\if{html}{\out{
    }}\preformatted{Kindly cite the eurostat R package as follows: + + Lahti L., Huovari J., Kainu M., and Biecek P. (2017). Retrieval and + analysis of Eurostat open data with the eurostat package. The R + Journal 9(1), pp. 385-392. doi: 10.32614/RJ-2017-019 + +A BibTeX entry for LaTeX users is + + @Article\{10.32614/RJ-2017-019, + title = \{Retrieval and Analysis of Eurostat Open Data with the eurostat Package\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek\}, + journal = \{The R Journal\}, + volume = \{9\}, + number = \{1\}, + pages = \{385--392\}, + year = \{2017\}, + doi = \{10.32614/RJ-2017-019\}, + url = \{https://doi.org/10.32614/RJ-2017-019\}, + \} + + Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., + and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data + [Computer software]. R package version 4.0.0. + https://github.com/rOpenGov/eurostat + +A BibTeX entry for LaTeX users is + + @Misc\{eurostat, + title = \{eurostat: Tools for Eurostat Open Data\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek and Diego Hernangomez and Daniel Antal and Pyry Kantanen\}, + url = \{https://github.com/rOpenGov/eurostat\}, + type = \{Computer software\}, + year = \{2023\}, + note = \{R package version 4.0.0\}, + \} +}\if{html}{\out{
    }} + +When citing data downloaded from Eurostat, see section "Citing Eurostat data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. +} +\seealso{ +\code{help("regions")}, \url{https://regions.dataobservatory.eu/} } \author{ -Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek \email{ropengov-forum@googlegroups.com} \url{https://ropengov.github.io/eurostat} +Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek } \keyword{package} diff --git a/man/eurostat_geodata_60_2016.Rd b/man/eurostat_geodata_60_2016.Rd index 9c75d75e..fcce0288 100755 --- a/man/eurostat_geodata_60_2016.Rd +++ b/man/eurostat_geodata_60_2016.Rd @@ -3,25 +3,147 @@ \docType{data} \name{eurostat_geodata_60_2016} \alias{eurostat_geodata_60_2016} -\title{Geospatial data of Europe from Gisco in 1:60 million scale from year 2016} -\format{sf -\describe{ - \item{id}{Country code in the Eurostat database} - \item{CNTRY_CODE}{Country code} - \item{NUTS_NAME}{NUTS name in local language} - \item{LEVL_CODE}{NUTS code} - \item{FID}{Country code} - \item{NUTS_ID}{NUTS code} - \item{geometry}{geospatial information} - \item{geo}{NUTS code} -}} -\source{ -\url{http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units} +\title{Geospatial data of Europe from GISCO in 1:60 million scale from +year 2016} +\format{ +sf object } -\usage{ -eurostat_geodata_60_2016 +\source{ +Data source: Eurostat via \code{\link[giscoR:gisco_get_nuts]{giscoR::gisco_get_nuts()}}. + +© EuroGeographics for the administrative boundaries + +Data downloaded from: +\url{https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units} } \description{ -Geospatial data of Europe from Gisco in 1:60 million scale from year 2016 +Geospatial data of Europe from GISCO in 1:60 million scale +from year 2016 +} +\details{ +The dataset contains 2016 observations (rows) and 12 variables (columns). + +The object contains the following columns: +\itemize{ +\item \strong{id}: JSON id code, the same as \strong{NUTS_ID}. See \strong{NUTS_ID} below for +further clarification. +\item \strong{LEVL_CODE}: NUTS level code: 0 (national level), 1 (major +socio-economic regions), 2 (basic regions for the application of regional +policies) or 3 (small regions). +\item \strong{NUTS_ID}: NUTS ID code, consisting of country code and numbers (1 for +NUTS 1, 2 for NUTS 2 and 3 for NUTS 3) +\item \strong{CNTR_CODE}: Country code: two-letter ISO code (ISO 3166 alpha-2), except +in the case of Greece (EL). +\item \strong{NAME_LATN}: NUTS name in local language, transliterated to Latin script +\item \strong{NUTS_NAME}: NUTS name in local language, in local script. +\item \strong{MOUNT_TYPE}: Mountain typology for NUTS 3 regions. +\itemize{ +\item 1: "where more than 50 \% of the surface is covered by topographic +mountain areas" +\item 2: "in which more than 50 \% of the regional population lives in +topographic mountain areas" +\item 3: "where more than 50 \% of the surface is covered by topographic +mountain areas and where more than 50 \% of the regional population lives +in these mountain areas" +\item 4: non-mountain region / other region +\item 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 and +non-EU countries) +} +\item \strong{URBN_TYPE}: Urban-rural typology for NUTS 3 regions. +\itemize{ +\item 1: predominantly urban region +\item 2: intermediate region +\item 3: predominantly rural region +\item 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 +regions) +} +\item \strong{COAST_TYPE}: Coastal typology for NUTS 3 regions. +\itemize{ +\item 1: coastal (on coast) +\item 2: coastal (>= 50\% of population living within 50km of the coastline) +\item 3: non-coastal region +\item 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 +regions) +} +\item \strong{FID}: Same as NUTS_ID. +\item \strong{geo}: Same as NUTS_ID, added for for easier joins with dplyr. However, +it is recommended to use other identical fields for this purpose. +\item \strong{geometry}: geospatial information. +} + +Dataset updated: 2023-06-29. For a more recent version, please use +\code{\link[giscoR:gisco_get_nuts]{giscoR::gisco_get_nuts()}} function. +} +\examples{ + +eurostat_geodata_60_2016 <- eurostat::eurostat_geodata_60_2016 + +# Manipulate and plot +if (require(sf)) { + library(sf) + # Filter NUTS3 from select countries like in a regular data frame + example_nuts <- subset(eurostat_geodata_60_2016, LEVL_CODE == 3 & + CNTR_CODE \%in\% c("DK", "DE", "PL")) + + plot(example_nuts["CNTR_CODE"]) +} + +} +\references{ +The following copyright notice is provided for end user convenience. +Please check up-to-date copyright information from the eurostat website: +\href{https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units}{GISCO: Geographical information and maps - Administrative units/statistical units} + +"In addition to the +\href{https://ec.europa.eu/eurostat/web/main/about/policies/copyright}{general copyright and licence policy} +applicable to the whole Eurostat website, the following +specific provisions apply to the datasets you are downloading. The download +and usage of these data is subject to the acceptance of the following +clauses: +\enumerate{ +\item The Commission agrees to grant the non-exclusive and not transferable +right to use and process the Eurostat/GISCO geographical data downloaded +from this page (the "data"). +\item The permission to use the data is granted on condition that: +\enumerate{ +\item the data will not be used for commercial purposes; +\item the source will be acknowledged. A copyright notice, as specified +below, will have to be visible on any printed or electronic publication +using the data downloaded from this page. +} +} +\subsection{Copyright notice}{ + +When data downloaded from this page is used in any printed or electronic +publication, in addition to any other provisions applicable to the whole +Eurostat website, data source will have to be acknowledged in the legend of +the map and in the introductory page of the publication with the following +copyright notice: + +EN: © EuroGeographics for the administrative boundaries + +FR: © EuroGeographics pour les limites administratives + +DE: © EuroGeographics bezüglich der Verwaltungsgrenzen + +For publications in languages other than English, French or German, the +translation of the copyright notice in the language of the publication shall +be used. + +If you intend to use the data commercially, please contact EuroGeographics +for information regarding their licence agreements." +} +} +\seealso{ +\code{\link[giscoR:gisco_get_nuts]{giscoR::gisco_get_nuts()}} and +\href{https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-gq-18-008}{Eurostat. (2019). Methodological manual on territorial typologies -- 2018 edition. Manuals and guidelines.} + +Other datasets: +\code{\link{eu_countries}}, +\code{\link{tgs00026}} + +Other geospatial: +\code{\link{get_eurostat_geospatial}()} } -\keyword{datasets} +\concept{datasets} +\concept{geospatial} diff --git a/man/eurotime2date.Rd b/man/eurotime2date.Rd index 35e31903..06a925c7 100755 --- a/man/eurotime2date.Rd +++ b/man/eurotime2date.Rd @@ -2,40 +2,98 @@ % Please edit documentation in R/eurotime2date.R \name{eurotime2date} \alias{eurotime2date} -\title{Date Conversion from Eurostat Time Format} +\title{Date Conversion from New Eurostat Time Format} \usage{ eurotime2date(x, last = FALSE) } \arguments{ \item{x}{a charter string with time information in Eurostat time format.} -\item{last}{a logical. If \code{FALsE} (default) the date is -the first date of the period (month, quarter or year). If \code{TRUE} +\item{last}{a logical. If \code{FALSE} (default) the date is +the first date of the period (month, quarter or year). If \code{TRUE} the date is the last date of the period.} } \value{ -an object of class \code{\link{Date}}. +an object of class \code{\link[=Date]{Date()}}. } \description{ Date conversion from Eurostat time format. A function to -convert Eurostat time values to objects of class \code{\link{Date}} +convert Eurostat time values to objects of class \code{\link[=Date]{Date()}} representing calendar dates. } +\details{ +Available patterns are YYYY (year), YYYY-SN (semester), YYYY-QN (quarter), +YYYY-MM (month), YYYY-WNN (week) and YYYY-MM-DD (day). +} \examples{ +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\donttest{ +na_q <- get_eurostat("namq_10_pc", time_format = "raw") +na_q$TIME_PERIOD <- eurotime2date(x = na_q$TIME_PERIOD) +unique(na_q$TIME_PERIOD) +} + \dontrun{ - na_q <- get_eurostat("namq_10_pc", time_format = "raw") - na_q$time <- eurotime2date(x = na_q$time) - - un <- get_eurostat("une_rt_m", time_format = "raw") - un$time <- eurotime2date(x = un$time) - - na_a <- get_eurostat("nama_10_pc", time_format = "raw") - na_a$time <- eurotime2date(x = na_a$time) - - eur_d <- get_eurostat("ert_bil_eur_d", time_format = "raw") - eur_d$time <- eurotime2date(x = eur_d$time) - } +# Test for weekly data +get_eurostat( + id = "lfsi_abs_w", + select_time = c("W"), + time_format = "date" + ) +} +\dontshow{\}) # examplesIf} +} +\references{ +See \code{citation("eurostat")}: + +\if{html}{\out{
    }}\preformatted{# Kindly cite the eurostat R package as follows: +# +# Lahti L., Huovari J., Kainu M., and Biecek P. (2017). Retrieval and +# analysis of Eurostat open data with the eurostat package. The R +# Journal 9(1), pp. 385-392. doi: 10.32614/RJ-2017-019 +# +# A BibTeX entry for LaTeX users is +# +# @Article\{10.32614/RJ-2017-019, +# title = \{Retrieval and Analysis of Eurostat Open Data with the eurostat Package\}, +# author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek\}, +# journal = \{The R Journal\}, +# volume = \{9\}, +# number = \{1\}, +# pages = \{385--392\}, +# year = \{2017\}, +# doi = \{10.32614/RJ-2017-019\}, +# url = \{https://doi.org/10.32614/RJ-2017-019\}, +# \} +# +# Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., +# and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data +# [Computer software]. R package version 4.0.0. +# https://github.com/rOpenGov/eurostat +# +# A BibTeX entry for LaTeX users is +# +# @Misc\{eurostat, +# title = \{eurostat: Tools for Eurostat Open Data\}, +# author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek and Diego Hernangomez and Daniel Antal and Pyry Kantanen\}, +# url = \{https://github.com/rOpenGov/eurostat\}, +# type = \{Computer software\}, +# year = \{2023\}, +# note = \{R package version 4.0.0\}, +# \} +}\if{html}{\out{
    }} +} +\seealso{ +\code{\link[lubridate:ymd]{lubridate::ymd()}} + +Other helpers: +\code{\link{cut_to_classes}()}, +\code{\link{dic_order}()}, +\code{\link{eurotime2num}()}, +\code{\link{harmonize_country_code}()}, +\code{\link{label_eurostat}()} } \author{ -Janne Huovari \email{janne.huovari@ptt.fi} +Janne Huovari \href{mailto:janne.huovari@ptt.fi}{janne.huovari@ptt.fi} } +\concept{helpers} diff --git a/man/eurotime2num.Rd b/man/eurotime2num.Rd index a5f01768..5e408d4d 100755 --- a/man/eurotime2num.Rd +++ b/man/eurotime2num.Rd @@ -10,28 +10,35 @@ eurotime2num(x) \item{x}{a charter string with time information in Eurostat time format.} } \value{ -see \code{\link{as.numeric}}. +see \code{\link[=as.numeric]{as.numeric()}}. } \description{ A conversion of a Eurostat time format to numeric. } \details{ -Bi-annual, quarterly and monthly data is presented as fraction of - the year in beginning of the period. Conversion of daily data is not - supported. +Bi-annual (semester), quarterly, monthly and weekly data can be presented as +a fraction of the year in beginning of the period. Conversion of daily data +is not supported. } \examples{ -\dontrun{ - na_q <- get_eurostat("namq_10_pc", time_format = "raw") - na_q$time <- eurotime2num(x = na_q$time) - - un <- get_eurostat("une_rt_m", time_format = "raw") - un$time <- eurotime2num(x = un$time) - - na_a <- get_eurostat("nama_10_pc", time_format = "raw") - na_a$time <- eurotime2num(x = na_a$time) - } +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\donttest{ +na_q <- get_eurostat("namq_10_pc", time_format = "raw") +na_q$TIME_PERIOD <- eurotime2num(x = na_q$TIME_PERIOD) + +unique(na_q$TIME_PERIOD) +} +\dontshow{\}) # examplesIf} +} +\seealso{ +Other helpers: +\code{\link{cut_to_classes}()}, +\code{\link{dic_order}()}, +\code{\link{eurotime2date}()}, +\code{\link{harmonize_country_code}()}, +\code{\link{label_eurostat}()} } \author{ -Janne Huovari \email{janne.huovari@ptt.fi} +Janne Huovari \href{mailto:janne.huovari@ptt.fi}{janne.huovari@ptt.fi}, Pyry Kantanen } +\concept{helpers} diff --git a/man/figures/logo.png b/man/figures/logo.png new file mode 100644 index 00000000..4b554470 Binary files /dev/null and b/man/figures/logo.png differ diff --git a/man/fixity_checksum.Rd b/man/fixity_checksum.Rd new file mode 100644 index 00000000..74b426b2 --- /dev/null +++ b/man/fixity_checksum.Rd @@ -0,0 +1,57 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{fixity_checksum} +\alias{fixity_checksum} +\title{Calculate a fixity checksum for an object} +\source{ +https://www.dpconline.org/handbook/technical-solutions-and-tools/fixity-and-checksums +} +\usage{ +fixity_checksum(data_object, algorithm = "md5") +} +\arguments{ +\item{data_object}{A dataset downloaded with some eurostat package function.} + +\item{algorithm}{Algorithm to use when calculating a checksum for a dataset. +Default is 'md5', but can be any supported algorithm in digest function.} +} +\description{ +Uses a hash function (md5) on an object and calculates a digest of the object +in the form of a character string. +} +\details{ +“Fixity, in the preservation sense, means the assurance that a digital file +has remained unchanged, i.e. fixed.” (Bailey, 2014). In practice, fixity +can most easily be established by calculating a checksum for the data object +that changes if anything in the data object has changed. What we use as a +checksum here is by default calculated with md5 hash algorithm. It is +possible to use other algorithms supported by the imported digest function, +see function documentation. + +In the case of big objects with millions of rows of data calculating a +checksum can take a bit longer and require some amount of RAM to be +available. Selecting another algorithm might perform faster and/or more +efficiently. Whichever algorithm you are using, please make sure to report +it transparently in your work for transparency and ensuring replicability. + +This function takes the whole data object as an input, meaning that +everything counts when calculating the fixity checksum. If the dataset +column names are labeled, if the data itself is labeled, if stringsAsFactors +is TRUE, if flags are removed or kept, if data is somehow edited... all these +affect the calculated checksum. It is advisable to calculate the checksum +immediately after downloading the data, before adding any labels or doing +other mutating operations. If you are using other arguments than the default +ones when downloading data, it is also good to report the exact arguments +used. + +This implementation fulfills the level 1 requirement of National Digital +Stewardship Alliance (NDSA) preservation levels by creating "fixity info +if it wasn’t provided with the content". In the current version of the +package, fixity information has to be created manually and is at the +responsibility of the user. +} +\seealso{ +\code{\link[digest:digest]{digest::digest()}} +} +\keyword{internal} +\keyword{utilities} diff --git a/man/get_bibentry.Rd b/man/get_bibentry.Rd new file mode 100755 index 00000000..76a800be --- /dev/null +++ b/man/get_bibentry.Rd @@ -0,0 +1,77 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/get_bibentry.R +\name{get_bibentry} +\alias{get_bibentry} +\title{Create A Data Bibliography} +\usage{ +get_bibentry(code, keywords = NULL, format = "Biblatex", lang = "en") +} +\arguments{ +\item{code}{A Eurostat data code or a vector of Eurostat data codes as +character or factor.} + +\item{keywords}{A list of keywords to be added to the entries. Defaults +to \code{NULL}.} + +\item{format}{Default is \code{'Biblatex'}, alternatives are \code{'bibentry'} +or \code{'Bibtex'} (not case sensitive)} + +\item{lang}{2-letter language code, default is "\code{en}" (English), other +options are "\code{fr}" (French) and "\code{de}" (German). Used for labeling datasets.} +} +\value{ +a bibentry, Bibtex or Biblatex object. +} +\description{ +Creates a bibliography from selected Eurostat data files, +including last Eurostat update, URL access data, and optional keywords +set by the user. +} +\section{Citing Eurostat data}{ +For citing datasets, use \code{\link[=get_bibentry]{get_bibentry()}} to build a bibliography that +is suitable for your reference manager of choice. + +When using Eurostat data in other contexts than academic publications that +in-text citations or footnotes/endnotes, the following guidelines may be +helpful: +\itemize{ +\item The origin of the data should always be mentioned as "Source: Eurostat". +\item The online dataset codes(s) should also be provided in order to ensure +transparency and facilitate access to the Eurostat data and related +methodological information. For example: +"Source: Eurostat (online data code: namq_10_gdp)" +\item Online publications (e.g. web pages, PDF) should include a clickable +link to the dataset using the bookmark functionality available in the +Eurostat data browser. +} + +It should be avoided to associate different entities (e.g. Eurostat, +National Statistical Offices, other data providers) to the same dataset or +indicator without specifying the role of each of them in the treatment of +data. + +See also section "Eurostat: Copyright notice and free re-use of data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. +} + +\examples{ +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\dontrun{ + my_bibliography <- get_bibentry( + code = c("tran_hv_frtra", "tec00001"), + keywords = list( + c("transport", "freight", "multimodal data", "GDP"), + c("economy and finance", "annual", "national accounts", "GDP") + ), + format = "Biblatex" + ) + my_bibliography +} +\dontshow{\}) # examplesIf} +} +\seealso{ +\link[utils:bibentry]{utils::bibentry} \link[RefManageR:toBiblatex]{RefManageR::toBiblatex} +} +\author{ +Daniel Antal, Przemyslaw Biecek +} diff --git a/man/get_eurostat.Rd b/man/get_eurostat.Rd index 4a09f41d..1e0ee2a8 100755 --- a/man/get_eurostat.Rd +++ b/man/get_eurostat.Rd @@ -2,144 +2,416 @@ % Please edit documentation in R/get_eurostat.R \name{get_eurostat} \alias{get_eurostat} -\title{Read Eurostat Data} +\title{Get Eurostat Data} \usage{ -get_eurostat(id, time_format = "date", filters = "none", - type = "code", select_time = NULL, cache = TRUE, - update_cache = FALSE, cache_dir = NULL, compress_file = TRUE, - stringsAsFactors = default.stringsAsFactors(), keepFlags = FALSE, - ...) +get_eurostat( + id, + time_format = "date", + filters = NULL, + type = "code", + select_time = NULL, + lang = "en", + cache = TRUE, + update_cache = FALSE, + cache_dir = NULL, + compress_file = TRUE, + stringsAsFactors = FALSE, + keepFlags = FALSE, + use.data.table = FALSE, + ... +) } \arguments{ -\item{id}{A code name for the dataset of interest. -See \code{\link{search_eurostat}} or details for how to get code.} - -\item{time_format}{a string giving a type of the conversion of the time -column from the eurostat format. A "date" (default) convers to -a \code{\link{Date}} with a first date of the period. -A "date_last" convers to a \code{\link{Date}} with - a last date of the period. A "num" convers to a numeric and "raw" - does not do conversion. See \code{\link{eurotime2date}} and - \code{\link{eurotime2num}}.} - -\item{filters}{a "none" (default) to get a whole dataset or a named list of -filters to get just part of the table. Names of list objects are -Eurostat variable codes and values are vectors of observation codes. -If \code{NULL} the whole -dataset is returned via API. More on details. See more on filters and -limitations per query via API from for -\code{\link{get_eurostat_json}}.} - -\item{type}{A type of variables, "code" (default) or "label".} - -\item{select_time}{a character symbol for a time frequence or NULL, +\item{id}{A unique identifier / code for the dataset of interest. If code is not +known \code{\link[=search_eurostat]{search_eurostat()}} function can be used to search Eurostat table +of contents.} + +\item{time_format}{a string giving a type of the conversion of the time column from the +eurostat format. The default argument "\code{date}" converts to a \code{\link[=Date]{Date()}} class +with the date being the first day of the period. A "\code{date_last}" argument +converts the dataset date to a \code{\link[=Date]{Date()}} class object with the difference +that the exact date is the last date of the period. Period can be year, +semester (half year), quarter, month, or week (See \code{\link[=eurotime2date]{eurotime2date()}} for +more information). +Argument "\code{num}" converts the date into a numeric (integer) meaning that +the first day of the year 2000 is close to 2000.01 and the last day of the +year is close to 2000.99 (see \code{\link[=eurotime2num]{eurotime2num()}} for more information). +Using the argument "\code{raw}" preserves the dates as they were in the original +Eurostat data.} + +\item{filters}{A named list of filters. Names of list objects are Eurostat +variable codes and values are vectors of observation codes. If \code{NULL} +(default) the whole dataset is returned. See details for more information +on filters and limitations per query.} + +\item{type}{A type of variables, "\code{code}" (default), "\code{label}" or "\code{both}". +The parameter "\code{both}" will return a data_frame with named vectors, +labels as values and codes as names.} + +\item{select_time}{a character symbol for a time frequency or \code{NULL}, which is used by default as most datasets have just one time frequency. For datasets with multiple time -frequencies, select the desired time format with: - Y = annual, S = semi-annual, Q = quarterly, M = monthly. -For all frequencies in same data frame \code{time_format = "raw"} -should be used.} +frequencies, select one or more of the desired frequencies with: +"Y" (or "A") = annual, "S" = semi-annual / semester, "Q" = quarterly, +"M" = monthly, "W" = weekly. For all frequencies in same data +frame \code{time_format = "raw"} should be used.} + +\item{lang}{2-letter language code, default is "\code{en}" (English), other +options are "\code{fr}" (French) and "\code{de}" (German). Used for labeling datasets.} -\item{cache}{a logical whether to do caching. Default is \code{TRUE}. Affects -only queries from the bulk download facility.} +\item{cache}{a logical whether to do caching. Default is \code{TRUE}.} -\item{update_cache}{a locigal whether to update cache. Can be set also with -options(eurostat_update = TRUE)} +\item{update_cache}{a logical whether to update cache. Can be set also with +\code{options(eurostat_update = TRUE)}} -\item{cache_dir}{a path to a cache directory. The directory have to exist. -The \code{NULL} (default) uses and creates -'eurostat' directory in the temporary directory from -\code{\link{tempdir}}. Directory can also be set with -\code{option} eurostat_cache_dir.} +\item{cache_dir}{a path to a cache directory. \code{NULL} (default) uses and creates +'eurostat' directory in the temporary directory defined by base R +\code{\link[=tempdir]{tempdir()}} function. The user can set the cache directory to an existing +directory by using this argument. The cache directory can also be set with +\code{\link[=set_eurostat_cache_dir]{set_eurostat_cache_dir()}} function.} -\item{compress_file}{a logical whether to compress the -RDS-file in caching. Default is \code{TRUE}.} +\item{compress_file}{a logical whether to compress the RDS-file in caching. Default is \code{TRUE}.} -\item{stringsAsFactors}{if \code{TRUE} (the default) variables are -converted to factors in original Eurostat order. If \code{FALSE} -they are returned as a character.} +\item{stringsAsFactors}{if \code{TRUE} (the default) variables are converted to factors in the original +Eurostat order. If \code{FALSE} they are returned as strings.} \item{keepFlags}{a logical whether the flags (e.g. "confidential", "provisional") should be kept in a separate column or if they -can be removed. Default is \code{FALSE}. For flag values see: -\url{http://ec.europa.eu/eurostat/data/database/information}. -Also possible non-real zero "0n" is indicated in flags column. +can be removed. Default is \code{FALSE}. For flag values see: +\url{https://ec.europa.eu/eurostat/data/database/information}. +Also possible non-real zero "0n" is indicated in flags column. Flags are not available for eurostat API, so \code{keepFlags} can not be used with a \code{filters}.} -\item{...}{further argument for \code{\link{get_eurostat_json}}.} +\item{use.data.table}{Use faster data.table functions? Default is FALSE. +On Windows requires that RTools is installed.} + +\item{...}{ + Arguments passed on to \code{\link[=get_eurostat_json]{get_eurostat_json}} + \describe{ + \item{\code{proxy}}{Use proxy, TRUE or FALSE (default).} + }} } \value{ -a tibble. One column for each dimension in the data, - the time column for a time dimension and - the values column for numerical values. - Eurostat data does not include all missing values and a treatment of - missing values depend on source. In bulk download - facility missing values are dropped if all dimensions are missing - on particular time. In JSON API missing values are dropped - only if all dimensions are missing on all times. The data from - bulk download facility can be completed for example with - \code{\link[tidyr]{complete}}. +a tibble. + +One column for each dimension in the data, the time column for a time +dimension and the values column for numerical values. Eurostat data does +not include all missing values and a treatment of missing values depend +on source. In bulk download facility missing values are dropped if all +dimensions are missing on particular time. In JSON API missing values are +dropped only if all dimensions are missing on all times. The data from +bulk download facility can be completed for example with \code{\link[tidyr:complete]{tidyr::complete()}}. } \description{ -Download data sets from Eurostat \url{ec.europa.eu/eurostat}. +Download data sets from Eurostat \url{https://ec.europa.eu/eurostat} } \details{ -Data sets are downloaded from -\href{http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing}{the Eurostat bulk download facility} or from The Eurostat Web Services -\href{http://ec.europa.eu/eurostat/web/json-and-unicode-web-services}{JSON API}. +Datasets are downloaded from +\href{https://wikis.ec.europa.eu/display/EUROSTATHELP/Transition+-+from+Eurostat+Bulk+Download+to+API}{the Eurostat SDMX 2.1 API} +in TSV format or from The Eurostat +\href{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query}{API Statistics JSON API}. If only the table \code{id} is given, the whole table is downloaded from the -bulk download facility. If also \code{filters} are defined the JSON API is -used. +SDMX API. If any \code{filters} are given JSON API is used instead. The bulk download facility is the fastest method to download whole datasets. -It is also often the only way as the JSON API has limitation of maximum -50 sub-indicators at time and whole datasets usually exceeds that. Also, -it seems that multi frequency datasets can only be retrived via -bulk download facility and the \code{select_time} is not available for +It is also often the only way as the JSON API has limitation of maximum +50 sub-indicators at time and whole datasets usually exceeds that. Also, +it seems that multi frequency datasets can only be retrieved via +bulk download facility and the \code{select_time} is not available for JSON API method. -If your connection is thru a proxy, you probably have to set proxy parameters -to use JSON API, see \code{\link{get_eurostat_json}}. +If your connection is through a proxy, you may have to set proxy parameters +to use JSON API, see \code{\link[=get_eurostat_json]{get_eurostat_json()}}. -By default datasets from the bulk download facility are cached as they are -often rather large. Caching is not (currently) possible for datasets from -JSON API. +By default datasets are cached to reduce load on Eurostat services and +because some datasets can be quite large. Cache files are stored in a temporary directory by default or in -a named directory if cache_dir or option eurostat_cache_dir is defined. -The cache can be emptied with \code{\link{clean_eurostat_cache}}. +a named directory (See \code{\link[=set_eurostat_cache_dir]{set_eurostat_cache_dir()}}). +The cache can be emptied with \code{\link[=clean_eurostat_cache]{clean_eurostat_cache()}}. The \code{id}, a code, for the dataset can be searched with -the \code{\link{search_eurostat}} or from the Eurostat database -\url{http://ec.europa.eu/eurostat/data/database}. The Eurostat +the \code{\link[=search_eurostat]{search_eurostat()}} or from the Eurostat database +\url{https://ec.europa.eu/eurostat/data/database}. The Eurostat database gives codes in the Data Navigation Tree after every dataset in parenthesis. } +\section{Eurostat: Copyright notice and free re-use of data}{ +The following copyright notice is provided for end user convenience. +Please check up-to-date copyright information from the eurostat website: +\url{https://ec.europa.eu/eurostat/about-us/policies/copyright} + +"(c) European Union, 1995 - today + +Eurostat has a policy of encouraging free re-use of its data, both for +non-commercial and commercial purposes. All statistical data, metadata, +content of web pages or other dissemination tools, official publications +and other documents published on its website, with the exceptions listed +below, can be reused without any payment or written licence provided that: +\itemize{ +\item the source is indicated as Eurostat; +\item when re-use involves modifications to the data or text, this must be +stated clearly to the end user of the information." +} + +For exceptions to the abovementioned principles see +\href{https://ec.europa.eu/eurostat/about-us/policies/copyright}{Eurostat website} +} + +\section{Filtering datasets}{ +When using Eurostat API Statistics (JSON API), datasets can be filtered +before they are downloaded and saved in local memory. The general format +for filter parameters is \verb{=}. + +Filter parameters are optional but the used dimension codes must be present +in the data product that is being queried. Dimension codes can +vary between different data products so it may be useful to examine new +datasets in Eurostat data browser beforehand. However, most if not all +Eurostat datasets concern European countries and contain information that +was gathered at some point in time, so \code{geo} and \code{time} dimension codes +can usually be used. + +\verb{} and \verb{} are case-insensitive and they can be written +in lowercase or uppercase in the query. + +Parameters are passed onto the \code{eurostat} package functions \code{\link[=get_eurostat]{get_eurostat()}} +and \code{\link[=get_eurostat_json]{get_eurostat_json()}} as a list item. If an individual item contains +multiple items, as it often can be in the case of \code{geo} parameters and +other optional items, they must be in the form of a vector: \code{c("FI", "SE")}. +For examples on how to use these parameters, see function examples below. +\subsection{Time parameters}{ + +\code{time} and \code{time_period} address the same \code{TIME_PERIOD} dimension in the +dataset and can be used interchangeably. In the Eurostat documentation +it is stated that "Using more than one Time parameter in the same query +is not accepted", but practice has shown that actually Eurostat API allows +multiple \code{time} parameters in the same query. This makes it possible to +use R colon operator when writing queries, so \code{time = c(2015:2018)} +translates to \verb{&time=2015&time=2016&time=2017&time=2018}. + +The only exception +to this is when the queried dataset contains e.g. quarterly data and +\code{TIME_PERIOD} is saved as \code{2015-Q1}, \code{2015-Q2} etc. Then it is possible +to use \code{time=2015-Q1&time=2015-Q2} style in the query URL, but this makes it +unfeasible to use the colon operator and requires a lot of manual typing. + +Because of this, it is useful to know about other time parameters as well: +\itemize{ +\item \code{untilTimePeriod}: return dataset items from the oldest record up until the +set time, for example "all data until 2000": \code{untilTimePeriod = 2000} +\item \code{sinceTimePeriod}: return dataset items starting from set time, for example +"all datastarting from 2008": \code{sinceTimePeriod = 2008} +\item \code{lastTimePeriod}: starting from the most recent time period, how many +preceding time periods should be returned? For example 10 most +recent observations: \code{lastTimePeriod = 10} +} + +Using both \code{untilTimePeriod} and \code{sinceTimePeriod} parameters in the same +query is allowed, making the usage of the R colon operator unnecessary. +In the case of quarterly data, using \code{untilTimePeriod} and \code{sinceTimePeriod} +parameters also works, as opposed to the colon operator, so it is generally +safer to use them as well. +} + +\subsection{Other dimensions}{ + +In \code{\link[=get_eurostat_json]{get_eurostat_json()}} examples \code{nama_10_gdp} dataset is filtered with +two additional filter parameters: +\itemize{ +\item \code{na_item = "B1GQ"} +\item \code{unit = "CLV_I10"} +} + +Filters like these are most likely unique to the \code{nama_10_gdp} dataset +(or other datasets within the same domain) and should +not be used with others dataset without user discretion. +By using \code{\link[=label_eurostat]{label_eurostat()}} we know that \code{"B1GQ"} stands for +"Gross domestic product at market prices" and +\code{"CLV_I10"} means "Chain linked volumes, index 2010=100". + +Different dimension codes can be translated to a natural language by using +the \code{\link[=get_eurostat_dic]{get_eurostat_dic()}} function, which returns labels for individual +dimension items such as \code{na_item} and \code{unit}, as opposed to +\code{\link[=label_eurostat]{label_eurostat()}} which does it for whole datasets. For example, the +parameter \code{na_item} stands for "National accounts indicator (ESA 2010)" and +\code{unit} stands for "Unit of measure". +} + +\subsection{Language}{ + +All datasets have metadata available in English, French and German. If no +parameter is given, the labels are returned in English. + +Example: +\itemize{ +\item \code{lang = "fr"} +} +} + +\subsection{More information}{ + +For more information about data filtering see Eurostat documentation +on API Statistics: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query#APIStatisticsdataquery-TheparametersdefinedintheRESTrequest} +} +} + +\section{Citing Eurostat data}{ +For citing datasets, use \code{\link[=get_bibentry]{get_bibentry()}} to build a bibliography that +is suitable for your reference manager of choice. + +When using Eurostat data in other contexts than academic publications that +in-text citations or footnotes/endnotes, the following guidelines may be +helpful: +\itemize{ +\item The origin of the data should always be mentioned as "Source: Eurostat". +\item The online dataset codes(s) should also be provided in order to ensure +transparency and facilitate access to the Eurostat data and related +methodological information. For example: +"Source: Eurostat (online data code: namq_10_gdp)" +\item Online publications (e.g. web pages, PDF) should include a clickable +link to the dataset using the bookmark functionality available in the +Eurostat data browser. +} + +It should be avoided to associate different entities (e.g. Eurostat, +National Statistical Offices, other data providers) to the same dataset or +indicator without specifying the role of each of them in the treatment of +data. + +See also section "Eurostat: Copyright notice and free re-use of data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. +} + +\section{Disclaimer: Availability of filtering functionalities}{ +Currently it only possible to download filtered data through API Statistics +(JSON API) when using \code{eurostat} package, although technically filtering +datasets downloaded through the SDMX Dissemination API is also supported by +Eurostat. We may support this feature in the future. In the meantime, if you +are interested in filtering Dissemination API data queries manually, please +consult the following Eurostat documentation: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+data+filtering} +} + +\section{Strategies for handling large datasets more efficiently}{ +Most Eurostat datasets are relatively manageable, at least on a machine +with 16 GB of RAM. The largest dataset in Eurostat database, at the time +of writing this, had 148362539 (148 million) values, which results in an +object with 148 million rows in tidy data (long) format. The test machine +with 16 GB of RAM was able to handle the second largest dataset in the +database with 91 million values (rows). + +There are still some methods to make data fetching +functions perform faster: +\itemize{ +\item turn caching off: \code{get_eurostat(cache = FALSE)} +\item turn cache compression off (may result in rather large cache files!): +\code{get_eurostat(compress_file = FALSE)} +\item if you want faster caching with manageable file sizes, use stringsAsFactors: +\code{get_eurostat(cache = TRUE, compress_file = TRUE, stringsAsFactors = TRUE)} +\item Use faster data.table functions: \code{get_eurostat(use.data.table = TRUE)} +\item Keep column processing to a minimum: +\code{get_eurostat(time_format = "raw", type = "code")} etc. +\item Read \code{get_eurostat()} function documentation carefully so you understand +what different arguments do +\item Filter the dataset so that you fetch only the parts you need! +} +} + \examples{ +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} \dontrun{ k <- get_eurostat("nama_10_lp_ulc") k <- get_eurostat("nama_10_lp_ulc", time_format = "num") k <- get_eurostat("nama_10_lp_ulc", update_cache = TRUE) -dir.create(file.path(tempdir(), "r_cache")) -k <- get_eurostat("nama_10_lp_ulc", - cache_dir = file.path(tempdir(), "r_cache")) + +k <- get_eurostat("nama_10_lp_ulc", + cache_dir = file.path(tempdir(), "r_cache") +) options(eurostat_update = TRUE) k <- get_eurostat("nama_10_lp_ulc") options(eurostat_update = FALSE) -options(eurostat_cache_dir = file.path(tempdir(), "r_cache")) + +set_eurostat_cache_dir(file.path(tempdir(), "r_cache2")) k <- get_eurostat("nama_10_lp_ulc") k <- get_eurostat("nama_10_lp_ulc", cache = FALSE) k <- get_eurostat("avia_gonc", select_time = "Y", cache = FALSE) -dd <- get_eurostat("nama_10_gdp", - filters = list(geo = "FI", - na_item = "B1GQ", - unit = "CLV_I10")) +dd <- get_eurostat("nama_10_gdp", + filters = list( + geo = "FI", + na_item = "B1GQ", + unit = "CLV_I10" + ) +) + +# A dataset with multiple time series in one +dd2 <- get_eurostat("AVIA_GOR_ME", + select_time = c("A", "M", "Q"), + time_format = "date_last" +) + +# An example of downloading whole dataset from JSON API +dd3 <- get_eurostat("AVIA_GOR_ME", + filters = list() +) + +# Filtering a dataset from a local file +dd3_filter <- get_eurostat("AVIA_GOR_ME", + filters = list( + tra_meas = "FRM_BRD" + ) +) + +} +\dontshow{\}) # examplesIf} } +\references{ +See \code{citation("eurostat")}: + +\if{html}{\out{
    }}\preformatted{Kindly cite the eurostat R package as follows: + + Lahti L., Huovari J., Kainu M., and Biecek P. (2017). Retrieval and + analysis of Eurostat open data with the eurostat package. The R + Journal 9(1), pp. 385-392. doi: 10.32614/RJ-2017-019 + +A BibTeX entry for LaTeX users is + + @Article\{10.32614/RJ-2017-019, + title = \{Retrieval and Analysis of Eurostat Open Data with the eurostat Package\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek\}, + journal = \{The R Journal\}, + volume = \{9\}, + number = \{1\}, + pages = \{385--392\}, + year = \{2017\}, + doi = \{10.32614/RJ-2017-019\}, + url = \{https://doi.org/10.32614/RJ-2017-019\}, + \} + + Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., + and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data + [Computer software]. R package version 4.0.0. + https://github.com/rOpenGov/eurostat + +A BibTeX entry for LaTeX users is + + @Misc\{eurostat, + title = \{eurostat: Tools for Eurostat Open Data\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek and Diego Hernangomez and Daniel Antal and Pyry Kantanen\}, + url = \{https://github.com/rOpenGov/eurostat\}, + type = \{Computer software\}, + year = \{2023\}, + note = \{R package version 4.0.0\}, + \} +}\if{html}{\out{
    }} + +When citing data downloaded from Eurostat, see section "Citing Eurostat data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. } \seealso{ -\code{\link{search_eurostat}}, \code{\link{label_eurostat}} +\code{\link[=search_eurostat]{search_eurostat()}}, \code{\link[=label_eurostat]{label_eurostat()}} } \author{ -Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu \email{ropengov-forum@googlegroups.com} \url{http://github.com/ropengov/eurostat} +Przemyslaw Biecek, Leo Lahti, Janne Huovari, Markus Kainu and Pyry Kantanen } diff --git a/man/get_eurostat_dic.Rd b/man/get_eurostat_dic.Rd index c13dd183..c236e422 100755 --- a/man/get_eurostat_dic.Rd +++ b/man/get_eurostat_dic.Rd @@ -9,7 +9,7 @@ get_eurostat_dic(dictname, lang = "en") \arguments{ \item{dictname}{A character, dictionary for the variable to be downloaded.} -\item{lang}{A character, language code. Options: "en" (default) / "fr" / "de".} +\item{lang}{A character, language code. Options: "en" (default), "fr", "de".} } \value{ tibble with two columns: code names and full names. @@ -18,26 +18,69 @@ tibble with two columns: code names and full names. Download a Eurostat dictionary. } \details{ -For given coded variable from Eurostat \url{ec.europa.eu/eurostat}. - The dictionaries link codes with human-readable labels. - To translate codes to labels, use \code{\link{label_eurostat}}. +For given coded variable from Eurostat +\url{https://ec.europa.eu/eurostat/}. The dictionaries link codes with +human-readable labels. To translate codes to labels, use +\code{\link[=label_eurostat]{label_eurostat()}}. } \examples{ -\dontrun{ - tmp <- get_eurostat_dic("crop_pro") - head(tmp) - tmp <- get_eurostat_dic("crop_pro", lang = "fr") - } +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\donttest{ +get_eurostat_dic("crop_pro") + +# Try another language +get_eurostat_dic("crop_pro", lang = "fr") +} +\dontshow{\}) # examplesIf} } \references{ -See citation("eurostat"). +See \code{citation("eurostat")}: + +\if{html}{\out{
    }}\preformatted{# Kindly cite the eurostat R package as follows: +# +# Lahti L., Huovari J., Kainu M., and Biecek P. (2017). Retrieval and +# analysis of Eurostat open data with the eurostat package. The R +# Journal 9(1), pp. 385-392. doi: 10.32614/RJ-2017-019 +# +# A BibTeX entry for LaTeX users is +# +# @Article\{10.32614/RJ-2017-019, +# title = \{Retrieval and Analysis of Eurostat Open Data with the eurostat Package\}, +# author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek\}, +# journal = \{The R Journal\}, +# volume = \{9\}, +# number = \{1\}, +# pages = \{385--392\}, +# year = \{2017\}, +# doi = \{10.32614/RJ-2017-019\}, +# url = \{https://doi.org/10.32614/RJ-2017-019\}, +# \} +# +# Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., +# and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data +# [Computer software]. R package version 4.0.0. +# https://github.com/rOpenGov/eurostat +# +# A BibTeX entry for LaTeX users is +# +# @Misc\{eurostat, +# title = \{eurostat: Tools for Eurostat Open Data\}, +# author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek and Diego Hernangomez and Daniel Antal and Pyry Kantanen\}, +# url = \{https://github.com/rOpenGov/eurostat\}, +# type = \{Computer software\}, +# year = \{2023\}, +# note = \{R package version 4.0.0\}, +# \} +}\if{html}{\out{
    }} } \seealso{ -\code{\link{label_eurostat}}, \code{\link{get_eurostat}}, - \code{\link{search_eurostat}}. +\code{\link[=label_eurostat]{label_eurostat()}}, \code{\link[=get_eurostat]{get_eurostat()}}, +\code{\link[=search_eurostat]{search_eurostat()}}. } \author{ -Przemyslaw Biecek and Leo Lahti \email{leo.lahti@iki.fi}. Thanks to Wietse Dol for contributions. +Przemyslaw Biecek and Leo Lahti \href{mailto:leo.lahti@iki.fi}{leo.lahti@iki.fi}. Thanks to +Wietse Dol for contributions. Updated by Pyry Kantanen to support XML +codelists. } \keyword{database} \keyword{utilities} diff --git a/man/get_eurostat_folder.Rd b/man/get_eurostat_folder.Rd new file mode 100644 index 00000000..4551e0db --- /dev/null +++ b/man/get_eurostat_folder.Rd @@ -0,0 +1,70 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/get_eurostat.R +\name{get_eurostat_folder} +\alias{get_eurostat_folder} +\title{Get all datasets in a folder} +\usage{ +get_eurostat_folder(code, env = .EurostatEnv) +} +\arguments{ +\item{code}{Folder code from Eurostat Table of Contents.} + +\item{env}{Name of the environment where downloaded datasets are assigned. +Default is .EurostatEnv. If NULL, datasets are returned as a list object.} +} +\description{ +Loops over all files in a Eurostat database folder, downloads the data and +assigns the datasets to environment. +} +\details{ +The datasets are assigned into .EurostatEnv by default, using dataset codes +as object names. The datasets are downloaded from SDMX API as TSV files, +meaning that they are returned without filtering. No filters can be +provided using this function. + +Please do not attempt to download too many datasets or the whole database +at once. The number of datasets that can be downloaded at once is hardcoded +to 20. The function also asks the user for confirmation if the number of +datasets in a folder is more than 10. This is by design to discourage +straining Eurostat API. +} +\section{Data source: Eurostat Table of Contents}{ +The Eurostat Table of Contents (TOC) is downloaded from +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=en} +(default) or from French or German language variants: +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=fr} +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=de} + +See Eurostat documentation on TOC items: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+-+Detailed+guidelines+-+Catalogue+API+-+TOC} +} + +\section{Data source: Eurostat SDMX 2.1 Dissemination API}{ +Data is downloaded from Eurostat SDMX 2.1 API endpoint +as compressed TSV files that are transformed into tabular format. +See Eurostat documentation for more information: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+data+query} + +The new dissemination API replaces the old bulk download facility that was +used by Eurostat before October 2023 and by the eurostat R package versions +before 4.0.0. +See Eurostat documentation about the transition from Bulk Download to API +for more information about the differences between the old bulk download +facility and the data provided by the new API connection: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/Transition+-+from+Eurostat+Bulk+Download+to+API} + +See especially the document Migrating_to_API_TSV.pdf that describes the +changes in TSV file format in new applications. + +For more information about SDMX 2.1, see SDMX standards: Section 7: +Guidelines for the use of web services, Version 2.1: +\url{https://sdmx.org/wp-content/uploads/SDMX_2-1_SECTION_7_WebServicesGuidelines.pdf} +} + +\seealso{ +\code{\link[=get_eurostat_toc]{get_eurostat_toc()}} \code{\link[=toc_count_children]{toc_count_children()}} \code{\link[=toc_determine_hierarchy]{toc_determine_hierarchy()}} +\code{\link[=toc_list_children]{toc_list_children()}} \code{\link[=toc_count_whitespace]{toc_count_whitespace()}} +} +\author{ +Pyry Kantanen +} diff --git a/man/get_eurostat_geospatial.Rd b/man/get_eurostat_geospatial.Rd index 5672ab41..8634fa4c 100755 --- a/man/get_eurostat_geospatial.Rd +++ b/man/get_eurostat_geospatial.Rd @@ -2,60 +2,264 @@ % Please edit documentation in R/get_eurostat_geospatial.R \name{get_eurostat_geospatial} \alias{get_eurostat_geospatial} -\title{Download Geospatial Data from GISGO} +\title{Download Geospatial Data from GISCO} +\source{ +Data source: Eurostat + +© EuroGeographics for the administrative boundaries + +Data downloaded using \pkg{giscoR} +} \usage{ -get_eurostat_geospatial(output_class = "sf", resolution = "60", - nuts_level = "all", year = "2016", cache = TRUE, - update_cache = FALSE, cache_dir = NULL) +get_eurostat_geospatial( + output_class = "sf", + resolution = "60", + nuts_level = "all", + year = "2016", + cache = TRUE, + update_cache = FALSE, + cache_dir = NULL, + crs = "4326", + make_valid = "DEPRECATED", + ... +) } \arguments{ -\item{output_class}{A string. Class of object returned, -either \code{sf} \code{simple features}, \code{df} (\code{data_frame}) or - \code{spdf} (\code{SpatialPolygonDataFrame})} +\item{output_class}{Class of object returned, +either \code{sf} \verb{simple features} or \code{df} (\code{data_frame}). \code{spdf} output has +been soft-deprecated, the function would switch to \code{sf}.} \item{resolution}{Resolution of the geospatial data. One of -"60" (1:60million), "20" (1:20million), "10" (1:10million), "03" (1:3million) or "01" (1:1million).} +\itemize{ +\item "60" (1:60million), +\item "20" (1:20million) +\item "10" (1:10million) +\item "03" (1:3million) or +\item "01" (1:1million). +}} \item{nuts_level}{Level of NUTS classification of the geospatial data. One of "0", "1", "2", "3" or "all" (mimics the original behaviour)} \item{year}{NUTS release year. One of -"2003", "2006", "2010", "2013" or "2016"} +"2003", "2006", "2010", "2013", "2016" or "2021"} + +\item{cache}{a logical whether to do caching. Default is \code{TRUE}.} + +\item{update_cache}{a logical whether to update cache. Can be set also with +\code{options(eurostat_update = TRUE)}} -\item{cache}{a logical whether to do caching. Default is \code{TRUE}. Affects -only queries from the bulk download facility.} +\item{cache_dir}{a path to a cache directory. See +\code{\link[=set_eurostat_cache_dir]{set_eurostat_cache_dir()}}. If \code{NULL} and the cache dir has not been set +globally the file would be stored in the \code{\link[=tempdir]{tempdir()}}.} -\item{update_cache}{a locigal whether to update cache. Can be set also with -options(eurostat_update = TRUE)} +\item{crs}{projection of the map: 4-digit +\href{https://spatialreference.org/ref/epsg/}{EPSG code}. One of: +\itemize{ +\item "4326" - WGS84 +\item "3035" - ETRS89 / ETRS-LAEA +\item "3857" - Pseudo-Mercator +}} -\item{cache_dir}{a path to a cache directory. The directory have to exist. -The \code{NULL} (default) uses and creates -'eurostat' directory in the temporary directory from -\code{\link{tempdir}}. Directory can also be set with -\code{option} eurostat_cache_dir.} +\item{make_valid}{Deprecated} + +\item{...}{ + Arguments passed on to \code{\link[giscoR:gisco_get_nuts]{giscoR::gisco_get_nuts}} + \describe{ + \item{\code{verbose}}{Logical, displays information. Useful for debugging, +default is \code{FALSE}.} + \item{\code{spatialtype}}{Type of geometry to be returned: +\itemize{ +\item \strong{"BN"}: Boundaries - \code{LINESTRING} object. +\item \strong{"LB"}: Labels - \code{POINT} object. +\item \strong{"RG"}: Regions - \code{MULTIPOLYGON/POLYGON} object. +}} + \item{\code{country}}{Optional. A character vector of country codes. It could be +either a vector of country names, a vector of ISO3 country codes or a +vector of Eurostat country codes. Mixed types (as \code{c("Turkey","US","FRA")}) +would not work. See also \code{\link[countrycode:countrycode]{countrycode::countrycode()}}.} + \item{\code{nuts_id}}{Optional. A character vector of NUTS IDs.} + }} } \value{ -a sf, data_frame or SpatialPolygonDataFrame. +a sf or data_frame } \description{ -Downloads either a simple features (sf), SpatialPolygonDataFrame or a data_frame preprocessed using - \code{broom::tidy()}. +Downloads either a simple features (sf) or a data_frame +of NUTS regions. This function is a wrapper of \code{\link[giscoR:gisco_get_nuts]{giscoR::gisco_get_nuts()}}. +This function requires to have installed the packages \CRANpkg{sf} and +\CRANpkg{giscoR}. } \details{ -The data source URL is \url{http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units}. +The objects downloaded from GISCO should contain all or some of the +following variable columns: +\itemize{ +\item \strong{id}: JSON id code, the same as \strong{NUTS_ID}. See \strong{NUTS_ID} below for +further clarification. +\item \strong{LEVL_CODE}: NUTS level code: 0 (national level), 1 (major +socio-economic regions), 2 (basic regions for the application of regional +policies) or 3 (small regions). +\item \strong{NUTS_ID}: NUTS ID code, consisting of country code and numbers (1 for +NUTS 1, 2 for NUTS 2 and 3 for NUTS 3) +\item \strong{CNTR_CODE}: Country code: two-letter ISO code (ISO 3166 alpha-2), except +in the case of Greece (EL). +\item \strong{NAME_LATN}: NUTS name in local language, transliterated to Latin script +\item \strong{NUTS_NAME}: NUTS name in local language, in local script. +\item \strong{MOUNT_TYPE}: Mountain typology for NUTS 3 regions. +\itemize{ +\item 1: "where more than 50 \% of the surface is covered by topographic +mountain areas" +\item 2: "in which more than 50 \% of the regional population lives in +topographic mountain areas" +\item 3: "where more than 50 \% of the surface is covered by topographic +mountain areas and where more than 50 \% of the regional population lives +in these mountain areas" +\item 4: non-mountain region / other region +\item 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 and +non-EU countries) +} +\item \strong{URBN_TYPE}: Urban-rural typology for NUTS 3 regions. +\itemize{ +\item 1: predominantly urban region +\item 2: intermediate region +\item 3: predominantly rural region +\item 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 +regions) +} +\item \strong{COAST_TYPE}: Coastal typology for NUTS 3 regions. +\itemize{ +\item 1: coastal (on coast) +\item 2: coastal (>= 50\% of population living within 50km of the coastline) +\item 3: non-coastal region +\item 0: no classification provided (e.g. in the case of NUTS 1 and NUTS 2 +regions) +} +\item \strong{FID}: Same as NUTS_ID. +\item \strong{geo}: Same as NUTS_ID, added for for easier joins with dplyr. Consider +the status of this column "questioning" and use other columns for joins +when possible. +\item \strong{geometry}: geospatial information. +} +} +\section{Eurostat: Copyright notice and free re-use of data}{ +The following copyright notice is provided for end user convenience. +Please check up-to-date copyright information from the eurostat website: +\url{https://ec.europa.eu/eurostat/about-us/policies/copyright} + +"(c) European Union, 1995 - today + +Eurostat has a policy of encouraging free re-use of its data, both for +non-commercial and commercial purposes. All statistical data, metadata, +content of web pages or other dissemination tools, official publications +and other documents published on its website, with the exceptions listed +below, can be reused without any payment or written licence provided that: +\itemize{ +\item the source is indicated as Eurostat; +\item when re-use involves modifications to the data or text, this must be +stated clearly to the end user of the information." +} + +For exceptions to the abovementioned principles see +\href{https://ec.europa.eu/eurostat/about-us/policies/copyright}{Eurostat website} +} + +\section{Data source: GISCO - General Copyright}{ +"Eurostat's general copyright notice and licence policy is applicable and +can be consulted here: +\url{https://ec.europa.eu/eurostat/about-us/policies/copyright} + +Please also be aware of the European Commission's general conditions: +\url{https://commission.europa.eu/legal-notice_en} + +Moreover, there are specific provisions applicable to some of the following +datasets available for downloading. The download and usage of these data +is subject to their acceptance: +\itemize{ +\item Administrative Units / Statistical Units +\item Population distribution / Demography +\item Transport Networks +\item Land Cover +\item Elevation (DEM)" } + +Of the abovementioned datasets, Administrative Units / Statistical Units +is applicable if the user wants to draw maps with borders provided by +GISCO / EuroGeographics. +} + +\section{Data source: GISCO - Administrative Units / Statistical Units}{ +The following copyright notice is provided for end user convenience. +Please check up-to-date copyright information from the GISCO website: +\href{https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units}{GISCO: Geographical information and maps - Administrative units/statistical units} + +"In addition to the \href{https://ec.europa.eu/eurostat/web/main/about/policies/copyright}{general copyright and licence policy} applicable to the whole Eurostat website, the following +specific provisions apply to the datasets you are downloading. The download +and usage of these data is subject to the acceptance of the following +clauses: +\enumerate{ +\item The Commission agrees to grant the non-exclusive and not transferable +right to use and process the Eurostat/GISCO geographical data downloaded +from this page (the "data"). +\item The permission to use the data is granted on condition that: +\enumerate{ +\item the data will not be used for commercial purposes; +\item the source will be acknowledged. A copyright notice, as specified +below, will have to be visible on any printed or electronic publication +using the data downloaded from this page." +} +} +\subsection{Copyright notice}{ + +When data downloaded from this page is used in any printed or electronic +publication, in addition to any other provisions applicable to the whole +Eurostat website, data source will have to be acknowledged in the legend of +the map and in the introductory page of the publication with the following +copyright notice: + +EN: © EuroGeographics for the administrative boundaries + +FR: © EuroGeographics pour les limites administratives + +DE: © EuroGeographics bezüglich der Verwaltungsgrenzen + +For publications in languages other than English, French or German, the +translation of the copyright notice in the language of the publication shall +be used. + +If you intend to use the data commercially, please contact EuroGeographics +for information regarding their licence agreements." +} +} + \examples{ - \dontrun{ - lp <- get_eurostat_geospatial(output_class = "sf", resolution = "60", nuts_level = "all") - lp \%>\% select(NUTS_ID) \%>\% plot() - lp <- get_eurostat_geospatial(output_class = "spdf", resolution = "60", nuts_level = "all") - spplot(lp, "STAT_LEVL_") - # or - lp <- get_eurostat_geospatial(output_class = "df", resolution = "60", nuts_level = "all") - ggplot(lp, aes(x=long,y=lat,group=group,fill=STAT_LEVL_),color="white") + geom_polygon() - } - +\donttest{ +# Uses cached dataset +sf <- get_eurostat_geospatial( + output_class = "sf", + resolution = "60", + nuts_level = "all" +) +# Downloads dataset from server +sf2 <- get_eurostat_geospatial( + output_class = "sf", + resolution = "20", + nuts_level = "all" +) +df <- get_eurostat_geospatial( + output_class = "df", + nuts_level = "0" +) +} + +} +\seealso{ +\code{\link[giscoR:gisco_get_nuts]{giscoR::gisco_get_nuts()}} + +Other geospatial: +\code{\link{eurostat_geodata_60_2016}} } \author{ -Markus Kainu +Markus Kainu \href{mailto:markuskainu@gmail.com}{markuskainu@gmail.com}, Diego Hernangomez +\url{https://github.com/dieghernan/} } +\concept{geospatial} diff --git a/man/get_eurostat_interactive.Rd b/man/get_eurostat_interactive.Rd new file mode 100644 index 00000000..0d5816cb --- /dev/null +++ b/man/get_eurostat_interactive.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/get_eurostat.R +\name{get_eurostat_interactive} +\alias{get_eurostat_interactive} +\title{Get Eurostat data interactive} +\usage{ +get_eurostat_interactive(code = NULL) +} +\arguments{ +\item{code}{A unique identifier / code for the dataset of interest. If code is not +known \code{\link[=search_eurostat]{search_eurostat()}} function can be used to search Eurostat table +of contents.} +} +\description{ +A simple interactive helper function to go through the steps of downloading +and/or finding suitable eurostat datasets. +} +\details{ +This function is intended to enable easy exploration of different eurostat +package functionalities and functions. In order to not drown the end user +in endless menus this function does not allow for setting +all possible \code{\link[=get_eurostat]{get_eurostat()}} function arguments. It is possible to set +\code{time_format}, \code{type}, \code{lang}, \code{stringsAsFactors}, \code{keepFlags}, and +\code{use.data.table} in the interactive menus. + +In some datasets setting these parameters may result in a +"Error in label_eurostat" error, for example: +"labels for XXXXXX includes duplicated labels in the Eurostat dictionary". +In these cases, and with other more complex queries, please +use \code{\link[=get_eurostat]{get_eurostat()}} function directly. +} +\seealso{ +\code{\link[=get_eurostat]{get_eurostat()}} +} diff --git a/man/get_eurostat_json.Rd b/man/get_eurostat_json.Rd index 73d7086e..83583600 100755 --- a/man/get_eurostat_json.Rd +++ b/man/get_eurostat_json.Rd @@ -4,69 +4,340 @@ \alias{get_eurostat_json} \title{Get Data from Eurostat API in JSON} \usage{ -get_eurostat_json(id, filters = NULL, type = c("code", "label", - "both"), lang = c("en", "fr", "de"), - stringsAsFactors = default.stringsAsFactors(), ...) +get_eurostat_json( + id, + filters = NULL, + type = "code", + lang = "en", + stringsAsFactors = FALSE, + proxy = FALSE, + ... +) } \arguments{ -\item{id}{A code name for the dataset of interested. See the table of -contents of eurostat datasets for more details.} +\item{id}{A unique identifier / code for the dataset of interest. If code is not +known \code{\link[=search_eurostat]{search_eurostat()}} function can be used to search Eurostat table +of contents.} \item{filters}{A named list of filters. Names of list objects are Eurostat variable codes and values are vectors of observation codes. If \code{NULL} -(default) the whole dataset is returned. See details for more on filters -and limitations per query.} +(default) the whole dataset is returned. See details for more information +on filters and limitations per query.} -\item{type}{A type of variables, "code" (default), "label" or "both". The -"both" will return a data_frame with named vectors, labels as values and -codes as names.} +\item{type}{A type of variables, "\code{code}" (default), "\code{label}" or "\code{both}". +The parameter "\code{both}" will return a data_frame with named vectors, +labels as values and codes as names.} -\item{lang}{A language used for metadata (en/fr/de).} +\item{lang}{2-letter language code, default is "\code{en}" (English), other +options are "\code{fr}" (French) and "\code{de}" (German). Used for labeling datasets.} -\item{stringsAsFactors}{if \code{TRUE} (the default) variables are converted -to factors in original Eurostat order. If \code{FALSE} they are returned as -a character.} +\item{stringsAsFactors}{if \code{TRUE} (the default) variables are converted to factors in the original +Eurostat order. If \code{FALSE} they are returned as strings.} -\item{...}{Other arguments passed on to \code{\link[httr]{GET}}. For example -a proxy parameters, see details. -.} +\item{proxy}{Use proxy, TRUE or FALSE (default).} + +\item{...}{ + Arguments passed on to \code{\link[httr2:req_proxy]{httr2::req_proxy}} + \describe{ + \item{\code{req}}{A \link[httr2]{request}.} + \item{\code{url,port}}{Location of proxy.} + \item{\code{username,password}}{Login details for proxy, if needed.} + \item{\code{auth}}{Type of HTTP authentication to use. Should be one of the +following: \code{basic}, digest, digest_ie, gssnegotiate, ntlm, any.} + }} } \value{ -A dataset as a data_frame. +A dataset as an object of \code{data.frame} class. } \description{ Retrieve data from Eurostat API in JSON format. } \details{ Data to retrieve from - \href{http://ec.europa.eu/eurostat/web/json-and-unicode-web-services}{The - Eurostat Web Services} can be specified with filters. Normally, it is - better to use JSON query through \code{\link{get_eurostat}}, than to use - \code{\link{get_eurostat_json}} directly. - - Queries are limited to 50 sub-indicators at a time. A time can be - filtered with fixed "time" filter or with "sinceTimePeriod" and - "lastTimePeriod" filters. A \code{sinceTimePeriod = 2000} returns - observations from 2000 to a last available. A \code{lastTimePeriod = 10} - returns a 10 last observations. - - To use a proxy to connect, a \code{\link[httr]{use_proxy}} can be - passed to \code{\link[httr]{GET}}. For example - \code{get_eurostat_json(id, filters, - config = httr::use_proxy(url, port, username, password))}. +\href{https://ec.europa.eu/eurostat/web/main/data/web-services}{The Eurostat Web Services} +can be specified with filters. Normally, it is +better to use JSON query through \code{\link[=get_eurostat]{get_eurostat()}}, than to use +\code{\link[=get_eurostat_json]{get_eurostat_json()}} directly. + +Queries are limited to 50 sub-indicators at a time. A time can be +filtered with fixed "time" filter or with "sinceTimePeriod" and +"lastTimePeriod" filters. A \code{sinceTimePeriod = 2000} returns +observations from 2000 to a last available. A \code{lastTimePeriod = 10} +returns a 10 last observations. See "Filtering datasets" section below +for more detailed information about filters. + +To use a proxy to connect, proxy arguments can be +passed to \code{\link[httr2:req_perform]{httr2::req_perform()}} via \code{\link[httr2:req_proxy]{httr2::req_proxy()}} - see latter +function documentation for parameter names that can be passed with \code{...}. +A non-functional example: +\code{get_eurostat_json(id, filters, proxy = TRUE, url = "127.0.0.1", port = 80)}. + +When retrieving data from Eurostat JSON API the user may encounter errors. +For end user convenience, we have provided a ready-made internal dataset +\code{sdmx_http_errors} that contains descriptive labels and descriptions about +the possible interpretation or cause of each error. These messages are +returned if the API returns a status indicating a HTTP error +(400 or greater). + +The Eurostat implementation seems to be based on SDMX 2.1, which is the +reason we've used SDMX Standards guidelines as a supplementary source +that we have included in the dataset. What this means in practice is that +the dataset contains error codes and their mappings that are not mentioned +in the Eurostat website. We hope you never encounter them. +} +\section{Data source: Eurostat API Statistics (JSON API)}{ +Data is downloaded from Eurostat API Statistics. See Eurostat documentation +for more information about data queries in API Statistics +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query} + +This replaces the old JSON Web Services that was used by Eurostat before +February 2023 and by the eurostat R package versions before 3.7.13. +See Eurostat documentation about the migration from JSON web service to API +Statistics for more information about the differences between the old and +the new service: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+migrating+from+JSON+web+service+to+API+Statistics} + +For easily viewing which filtering options are available - in addition to +the default ones, time and language - Eurostat Web services Query builder +tool may be useful: +\url{https://ec.europa.eu/eurostat/web/query-builder} +} + +\section{Filtering datasets}{ +When using Eurostat API Statistics (JSON API), datasets can be filtered +before they are downloaded and saved in local memory. The general format +for filter parameters is \verb{=}. + +Filter parameters are optional but the used dimension codes must be present +in the data product that is being queried. Dimension codes can +vary between different data products so it may be useful to examine new +datasets in Eurostat data browser beforehand. However, most if not all +Eurostat datasets concern European countries and contain information that +was gathered at some point in time, so \code{geo} and \code{time} dimension codes +can usually be used. + +\verb{} and \verb{} are case-insensitive and they can be written +in lowercase or uppercase in the query. + +Parameters are passed onto the \code{eurostat} package functions \code{\link[=get_eurostat]{get_eurostat()}} +and \code{\link[=get_eurostat_json]{get_eurostat_json()}} as a list item. If an individual item contains +multiple items, as it often can be in the case of \code{geo} parameters and +other optional items, they must be in the form of a vector: \code{c("FI", "SE")}. +For examples on how to use these parameters, see function examples below. +\subsection{Time parameters}{ + +\code{time} and \code{time_period} address the same \code{TIME_PERIOD} dimension in the +dataset and can be used interchangeably. In the Eurostat documentation +it is stated that "Using more than one Time parameter in the same query +is not accepted", but practice has shown that actually Eurostat API allows +multiple \code{time} parameters in the same query. This makes it possible to +use R colon operator when writing queries, so \code{time = c(2015:2018)} +translates to \verb{&time=2015&time=2016&time=2017&time=2018}. + +The only exception +to this is when the queried dataset contains e.g. quarterly data and +\code{TIME_PERIOD} is saved as \code{2015-Q1}, \code{2015-Q2} etc. Then it is possible +to use \code{time=2015-Q1&time=2015-Q2} style in the query URL, but this makes it +unfeasible to use the colon operator and requires a lot of manual typing. + +Because of this, it is useful to know about other time parameters as well: +\itemize{ +\item \code{untilTimePeriod}: return dataset items from the oldest record up until the +set time, for example "all data until 2000": \code{untilTimePeriod = 2000} +\item \code{sinceTimePeriod}: return dataset items starting from set time, for example +"all datastarting from 2008": \code{sinceTimePeriod = 2008} +\item \code{lastTimePeriod}: starting from the most recent time period, how many +preceding time periods should be returned? For example 10 most +recent observations: \code{lastTimePeriod = 10} +} + +Using both \code{untilTimePeriod} and \code{sinceTimePeriod} parameters in the same +query is allowed, making the usage of the R colon operator unnecessary. +In the case of quarterly data, using \code{untilTimePeriod} and \code{sinceTimePeriod} +parameters also works, as opposed to the colon operator, so it is generally +safer to use them as well. +} + +\subsection{Other dimensions}{ + +In \code{\link[=get_eurostat_json]{get_eurostat_json()}} examples \code{nama_10_gdp} dataset is filtered with +two additional filter parameters: +\itemize{ +\item \code{na_item = "B1GQ"} +\item \code{unit = "CLV_I10"} +} + +Filters like these are most likely unique to the \code{nama_10_gdp} dataset +(or other datasets within the same domain) and should +not be used with others dataset without user discretion. +By using \code{\link[=label_eurostat]{label_eurostat()}} we know that \code{"B1GQ"} stands for +"Gross domestic product at market prices" and +\code{"CLV_I10"} means "Chain linked volumes, index 2010=100". + +Different dimension codes can be translated to a natural language by using +the \code{\link[=get_eurostat_dic]{get_eurostat_dic()}} function, which returns labels for individual +dimension items such as \code{na_item} and \code{unit}, as opposed to +\code{\link[=label_eurostat]{label_eurostat()}} which does it for whole datasets. For example, the +parameter \code{na_item} stands for "National accounts indicator (ESA 2010)" and +\code{unit} stands for "Unit of measure". +} + +\subsection{Language}{ + +All datasets have metadata available in English, French and German. If no +parameter is given, the labels are returned in English. + +Example: +\itemize{ +\item \code{lang = "fr"} +} +} + +\subsection{More information}{ + +For more information about data filtering see Eurostat documentation +on API Statistics: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+Statistics+-+data+query#APIStatisticsdataquery-TheparametersdefinedintheRESTrequest} +} +} + +\section{Eurostat: Copyright notice and free re-use of data}{ +The following copyright notice is provided for end user convenience. +Please check up-to-date copyright information from the eurostat website: +\url{https://ec.europa.eu/eurostat/about-us/policies/copyright} + +"(c) European Union, 1995 - today + +Eurostat has a policy of encouraging free re-use of its data, both for +non-commercial and commercial purposes. All statistical data, metadata, +content of web pages or other dissemination tools, official publications +and other documents published on its website, with the exceptions listed +below, can be reused without any payment or written licence provided that: +\itemize{ +\item the source is indicated as Eurostat; +\item when re-use involves modifications to the data or text, this must be +stated clearly to the end user of the information." +} + +For exceptions to the abovementioned principles see +\href{https://ec.europa.eu/eurostat/about-us/policies/copyright}{Eurostat website} +} + +\section{Citing Eurostat data}{ +For citing datasets, use \code{\link[=get_bibentry]{get_bibentry()}} to build a bibliography that +is suitable for your reference manager of choice. + +When using Eurostat data in other contexts than academic publications that +in-text citations or footnotes/endnotes, the following guidelines may be +helpful: +\itemize{ +\item The origin of the data should always be mentioned as "Source: Eurostat". +\item The online dataset codes(s) should also be provided in order to ensure +transparency and facilitate access to the Eurostat data and related +methodological information. For example: +"Source: Eurostat (online data code: namq_10_gdp)" +\item Online publications (e.g. web pages, PDF) should include a clickable +link to the dataset using the bookmark functionality available in the +Eurostat data browser. +} + +It should be avoided to associate different entities (e.g. Eurostat, +National Statistical Offices, other data providers) to the same dataset or +indicator without specifying the role of each of them in the treatment of +data. + +See also section "Eurostat: Copyright notice and free re-use of data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. } + +\section{Disclaimer: Availability of filtering functionalities}{ +Currently it only possible to download filtered data through API Statistics +(JSON API) when using \code{eurostat} package, although technically filtering +datasets downloaded through the SDMX Dissemination API is also supported by +Eurostat. We may support this feature in the future. In the meantime, if you +are interested in filtering Dissemination API data queries manually, please +consult the following Eurostat documentation: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+data+filtering} +} + \examples{ - \dontrun{ - tmp <- get_eurostat_json("cdh_e_fos") - yy <- get_eurostat_json(id = "nama_gdp_c", filters = list(geo=c("EU28", "FI"), - unit="EUR_HAB", - indic_na="B1GM")) +\dontrun{ +# Generally speaking these queries would be done through get_eurostat +tmp <- get_eurostat_json("nama_10_gdp") +yy <- get_eurostat_json("nama_10_gdp", filters = list( + geo = c("FI", "SE", "EU28"), + time = c(2015:2023), + lang = "FR", + na_item = "B1GQ", + unit = "CLV_I10" +)) + +# TIME_PERIOD filter works also with the new JSON API +yy2 <- get_eurostat_json("nama_10_gdp", filters = list( + geo = c("FI", "SE", "EU28"), + TIME_PERIOD = c(2015:2023), + lang = "FR", + na_item = "B1GQ", + unit = "CLV_I10" +)) + +# An example from get_eurostat +dd <- get_eurostat("nama_10_gdp", + filters = list( + geo = "FI", + na_item = "B1GQ", + unit = "CLV_I10" +)) +} +} +\references{ +See \code{citation("eurostat")}: + +\if{html}{\out{
    }}\preformatted{Kindly cite the eurostat R package as follows: + + Lahti L., Huovari J., Kainu M., and Biecek P. (2017). Retrieval and + analysis of Eurostat open data with the eurostat package. The R + Journal 9(1), pp. 385-392. doi: 10.32614/RJ-2017-019 + +A BibTeX entry for LaTeX users is + + @Article\{10.32614/RJ-2017-019, + title = \{Retrieval and Analysis of Eurostat Open Data with the eurostat Package\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek\}, + journal = \{The R Journal\}, + volume = \{9\}, + number = \{1\}, + pages = \{385--392\}, + year = \{2017\}, + doi = \{10.32614/RJ-2017-019\}, + url = \{https://doi.org/10.32614/RJ-2017-019\}, + \} + + Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., + and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data + [Computer software]. R package version 4.0.0. + https://github.com/rOpenGov/eurostat + +A BibTeX entry for LaTeX users is + + @Misc\{eurostat, + title = \{eurostat: Tools for Eurostat Open Data\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek and Diego Hernangomez and Daniel Antal and Pyry Kantanen\}, + url = \{https://github.com/rOpenGov/eurostat\}, + type = \{Computer software\}, + year = \{2023\}, + note = \{R package version 4.0.0\}, + \} +}\if{html}{\out{
    }} + +When citing data downloaded from Eurostat, see section "Citing Eurostat data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. } +\seealso{ +\code{\link[httr2:req_proxy]{httr2::req_proxy()}} } \author{ -Przemyslaw Biecek, Leo Lahti, Janne Huovari and Markus Kainu - \email{ropengov-forum@googlegroups.com} - \url{http://github.com/ropengov/eurostat} +Przemyslaw Biecek, Leo Lahti, Janne Huovari Markus Kainu and Pyry Kantanen } \keyword{database} \keyword{utilities} diff --git a/man/get_eurostat_raw.Rd b/man/get_eurostat_raw.Rd index 8b4c0943..d3aca865 100755 --- a/man/get_eurostat_raw.Rd +++ b/man/get_eurostat_raw.Rd @@ -2,42 +2,162 @@ % Please edit documentation in R/get_eurostat_raw.R \name{get_eurostat_raw} \alias{get_eurostat_raw} -\title{Download Data from Eurostat Database} +\title{Download Data from Eurostat Dissemination API} \usage{ -get_eurostat_raw(id) +get_eurostat_raw(id, use.data.table = FALSE) } \arguments{ -\item{id}{A code name for the dataset of interested. See the table of -contents of eurostat datasets for more details.} +\item{id}{A unique identifier / code for the dataset of interest. If code is not +known \code{\link[=search_eurostat]{search_eurostat()}} function can be used to search Eurostat table +of contents.} + +\item{use.data.table}{Use faster data.table functions? Default is FALSE. +On Windows requires that RTools is installed.} } \value{ -A dataset in tibble format. First column contains comma - separated codes of cases. Other columns usually corresponds to - years and column names are years with preceding X. Data is in - character format as it contains values together with eurostat - flags for data. +A dataset in tibble format. First column contains comma +separated codes of cases. Other columns usually corresponds to +years and column names are years with preceding X. Data is in +character format as it contains values together with eurostat +flags for data. } \description{ -Download data from the eurostat database. +Download data from the eurostat database through the new +dissemination API. } -\details{ -Data is downloaded from - \url{http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing} and transformed into tabular format. +\section{Data source: Eurostat SDMX 2.1 Dissemination API}{ +Data is downloaded from Eurostat SDMX 2.1 API endpoint +as compressed TSV files that are transformed into tabular format. +See Eurostat documentation for more information: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+data+query} + +The new dissemination API replaces the old bulk download facility that was +used by Eurostat before October 2023 and by the eurostat R package versions +before 4.0.0. +See Eurostat documentation about the transition from Bulk Download to API +for more information about the differences between the old bulk download +facility and the data provided by the new API connection: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/Transition+-+from+Eurostat+Bulk+Download+to+API} + +See especially the document Migrating_to_API_TSV.pdf that describes the +changes in TSV file format in new applications. + +For more information about SDMX 2.1, see SDMX standards: Section 7: +Guidelines for the use of web services, Version 2.1: +\url{https://sdmx.org/wp-content/uploads/SDMX_2-1_SECTION_7_WebServicesGuidelines.pdf} } + +\section{Eurostat: Copyright notice and free re-use of data}{ +The following copyright notice is provided for end user convenience. +Please check up-to-date copyright information from the eurostat website: +\url{https://ec.europa.eu/eurostat/about-us/policies/copyright} + +"(c) European Union, 1995 - today + +Eurostat has a policy of encouraging free re-use of its data, both for +non-commercial and commercial purposes. All statistical data, metadata, +content of web pages or other dissemination tools, official publications +and other documents published on its website, with the exceptions listed +below, can be reused without any payment or written licence provided that: +\itemize{ +\item the source is indicated as Eurostat; +\item when re-use involves modifications to the data or text, this must be +stated clearly to the end user of the information." +} + +For exceptions to the abovementioned principles see +\href{https://ec.europa.eu/eurostat/about-us/policies/copyright}{Eurostat website} +} + +\section{Citing Eurostat data}{ +For citing datasets, use \code{\link[=get_bibentry]{get_bibentry()}} to build a bibliography that +is suitable for your reference manager of choice. + +When using Eurostat data in other contexts than academic publications that +in-text citations or footnotes/endnotes, the following guidelines may be +helpful: +\itemize{ +\item The origin of the data should always be mentioned as "Source: Eurostat". +\item The online dataset codes(s) should also be provided in order to ensure +transparency and facilitate access to the Eurostat data and related +methodological information. For example: +"Source: Eurostat (online data code: namq_10_gdp)" +\item Online publications (e.g. web pages, PDF) should include a clickable +link to the dataset using the bookmark functionality available in the +Eurostat data browser. +} + +It should be avoided to associate different entities (e.g. Eurostat, +National Statistical Offices, other data providers) to the same dataset or +indicator without specifying the role of each of them in the treatment of +data. + +See also section "Eurostat: Copyright notice and free re-use of data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. +} + +\section{Disclaimer: Availability of filtering functionalities}{ +Currently it only possible to download filtered data through API Statistics +(JSON API) when using \code{eurostat} package, although technically filtering +datasets downloaded through the SDMX Dissemination API is also supported by +Eurostat. We may support this feature in the future. In the meantime, if you +are interested in filtering Dissemination API data queries manually, please +consult the following Eurostat documentation: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+data+filtering} +} + \examples{ -\dontrun{ - tmp <- eurostat:::get_eurostat_raw("educ_iste") - head(tmp) - } +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\donttest{ +eurostat:::get_eurostat_raw("educ_iste") +} +\dontshow{\}) # examplesIf} } \references{ -see citation("eurostat") +See \code{citation("eurostat")}: + +\if{html}{\out{
    }}\preformatted{# Kindly cite the eurostat R package as follows: +# +# Lahti L., Huovari J., Kainu M., and Biecek P. (2017). Retrieval and +# analysis of Eurostat open data with the eurostat package. The R +# Journal 9(1), pp. 385-392. doi: 10.32614/RJ-2017-019 +# +# A BibTeX entry for LaTeX users is +# +# @Article\{10.32614/RJ-2017-019, +# title = \{Retrieval and Analysis of Eurostat Open Data with the eurostat Package\}, +# author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek\}, +# journal = \{The R Journal\}, +# volume = \{9\}, +# number = \{1\}, +# pages = \{385--392\}, +# year = \{2017\}, +# doi = \{10.32614/RJ-2017-019\}, +# url = \{https://doi.org/10.32614/RJ-2017-019\}, +# \} +# +# Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., +# and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data +# [Computer software]. R package version 4.0.0. +# https://github.com/rOpenGov/eurostat +# +# A BibTeX entry for LaTeX users is +# +# @Misc\{eurostat, +# title = \{eurostat: Tools for Eurostat Open Data\}, +# author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek and Diego Hernangomez and Daniel Antal and Pyry Kantanen\}, +# url = \{https://github.com/rOpenGov/eurostat\}, +# type = \{Computer software\}, +# year = \{2023\}, +# note = \{R package version 4.0.0\}, +# \} +}\if{html}{\out{
    }} } \seealso{ -\code{\link{get_eurostat}}. +\code{\link[=get_eurostat]{get_eurostat()}} } \author{ -Przemyslaw Biecek, Leo Lahti and Janne Huovari \email{ropengov-forum@googlegroups.com} +Przemyslaw Biecek, Leo Lahti, Janne Huovari and Pyry Kantanen } \keyword{database} \keyword{utilities} diff --git a/man/get_eurostat_toc.Rd b/man/get_eurostat_toc.Rd index cc142b58..1efe3545 100755 --- a/man/get_eurostat_toc.Rd +++ b/man/get_eurostat_toc.Rd @@ -4,34 +4,130 @@ \alias{get_eurostat_toc} \title{Download Table of Contents of Eurostat Data Sets} \usage{ -get_eurostat_toc() +get_eurostat_toc(lang = "en") +} +\arguments{ +\item{lang}{2-letter language code, default is "\code{en}" (English), other +options are "\code{fr}" (French) and "\code{de}" (German). Used for labeling datasets.} } \value{ -A tibble with eight columns - \itemize{ - \item{title}{The name of dataset of theme} - \item{code}{The codename of dataset of theme, will be used by the eurostat and get_eurostat_raw functions.} - \item{type}{Is it a dataset, folder or table.} - \item{last.update.of.data, last.table.structure.change, data.start, data.end}{Dates.} - } +A tibble with nine columns: +\describe{ +\item{title}{Dataset title in English (default)} +\item{code}{ Each item (dataset, table and folder) of the TOC has a +unique code which allows it to be identified in the API. Used in the +\code{\link[=get_eurostat]{get_eurostat()}} and \code{\link[=get_eurostat_raw]{get_eurostat_raw()}} functions to retrieve datasets.} +\item{type}{dataset, folder or table} +\item{last.update.of.data}{Date, indicates the last time the +dataset/table was updated (format \code{DD.MM.YYYY} or \verb{\%d.\%m.\%Y})} +\item{last.table.structure.change}{Date, indicates the last time the +dataset/table structure was modified (format \code{DD.MM.YYYY} or \verb{\%d.\%m.\%Y})} +\item{data.start}{Date of the oldest value included in the dataset +(if available) (format usually \code{YYYY} or \verb{\%Y} but can also be \code{YYYY-MM}, +\code{YYYY-MM-DD}, \code{YYYY-SN}, \code{YYYY-QN} etc.)} +\item{data.end}{Date of the most recent value included in the dataset +(if available) (format usually \code{YYYY} or \verb{\%Y} but can also be \code{YYYY-MM}, +\code{YYYY-MM-DD}, \code{YYYY-SN}, \code{YYYY-QN} etc.)} +\item{values}{Number of actual values included in the dataset} +\item{hierarchy}{Hierarchy of the data navigation tree, represented +in the original txt file by a 4-spaces indentation prefix in the title} +} } \description{ Download table of contents (TOC) of eurostat datasets. } \details{ -The TOC is downloaded from \url{http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&file=table_of_contents_en.txt}. The values in column 'code' should be used to download a selected dataset. +In the downloaded Eurostat Table of Contents the 'code' column +values are refer to the function 'id' that is used as an argument in certain +functions when downloading datasets. } +\section{Data source: Eurostat Table of Contents}{ +The Eurostat Table of Contents (TOC) is downloaded from +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=en} +(default) or from French or German language variants: +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=fr} +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=de} + +See Eurostat documentation on TOC items: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+-+Detailed+guidelines+-+Catalogue+API+-+TOC} +} + \examples{ -\dontrun{tmp <- get_eurostat_toc(); head(tmp)} +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\donttest{ +tmp <- get_eurostat_toc() +head(tmp) + +# Convert columns containing dates as character into Date class +# Last update of data +tmp[[4]] <- as.Date(tmp[[4]], format = c("\%d.\%m.\%Y")) +# Last table structure change +tmp[[5]] <- as.Date(tmp[[5]], format = c("\%d.\%m.\%Y")) +# Data start, contains several formats (date, week, month quarter, semester) +# Unfortunately semesters are not directly supported so they need to be +# changed into quarters +tmp$data.start <- gsub("S2", "Q3", tmp$data.start) +tmp$data.start <- lubridate::as_date( + x = tmp$data.start, + format = c("\%Y", "\%Y-Q\%q", "\%Y-W\%W", "\%Y-S\%q", "\%Y-\%m-\%d", "\%Y-\%m") + ) +# Data end, same as data start +tmp$data.end <- gsub("S2", "Q3", tmp$data.end) +tmp$data.end <- lubridate::as_date( + x = tmp$data.end, + format = c("\%Y", "\%Y-Q\%q", "\%Y-W\%W", "\%Y-S\%q", "\%Y-\%m-\%d", "\%Y-\%m") + ) +} +\dontshow{\}) # examplesIf} } \references{ -See citation("eurostat"). +See \code{citation("eurostat")}: + +\if{html}{\out{
    }}\preformatted{Kindly cite the eurostat R package as follows: + + Lahti L., Huovari J., Kainu M., and Biecek P. (2017). Retrieval and + analysis of Eurostat open data with the eurostat package. The R + Journal 9(1), pp. 385-392. doi: 10.32614/RJ-2017-019 + +A BibTeX entry for LaTeX users is + + @Article\{10.32614/RJ-2017-019, + title = \{Retrieval and Analysis of Eurostat Open Data with the eurostat Package\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek\}, + journal = \{The R Journal\}, + volume = \{9\}, + number = \{1\}, + pages = \{385--392\}, + year = \{2017\}, + doi = \{10.32614/RJ-2017-019\}, + url = \{https://doi.org/10.32614/RJ-2017-019\}, + \} + + Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., + and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data + [Computer software]. R package version 4.0.0. + https://github.com/rOpenGov/eurostat + +A BibTeX entry for LaTeX users is + + @Misc\{eurostat, + title = \{eurostat: Tools for Eurostat Open Data\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek and Diego Hernangomez and Daniel Antal and Pyry Kantanen\}, + url = \{https://github.com/rOpenGov/eurostat\}, + type = \{Computer software\}, + year = \{2023\}, + note = \{R package version 4.0.0\}, + \} +}\if{html}{\out{
    }} + +When citing data downloaded from Eurostat, see section "Citing Eurostat data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. } \seealso{ -\code{\link{get_eurostat}}, \code{\link{search_eurostat}}. +\code{\link[=get_eurostat]{get_eurostat()}}, \code{\link[=search_eurostat]{search_eurostat()}} } \author{ -Przemyslaw Biecek and Leo Lahti \email{ropengov-forum@googlegroups.com} +Przemyslaw Biecek, Leo Lahti and Pyry Kantanen \href{mailto:ropengov-forum@googlegroups.com}{ropengov-forum@googlegroups.com} } \keyword{database} \keyword{utilities} diff --git a/man/harmonize_country_code.Rd b/man/harmonize_country_code.Rd index c0c3302c..27e58eb5 100755 --- a/man/harmonize_country_code.Rd +++ b/man/harmonize_country_code.Rd @@ -14,17 +14,28 @@ a vector. } \description{ The European Commission and the Eurostat generally uses ISO - 3166-1 alpha-2 codes with two exceptions: EL (not GR) is used to - represent Greece, and UK (not GB) is used to represent the - United Kingdom. This function turns country codes into to ISO - 3166-1 alpha-2. +3166-1 alpha-2 codes with two exceptions: EL (not GR) is used to +represent Greece, and UK (not GB) is used to represent the +United Kingdom. This function turns country codes into to ISO +3166-1 alpha-2. } \examples{ - \dontrun{ - lp <- get_eurostat("nama_aux_lp") - lp$geo <- harmonize_country_code(lp$geo) - } +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\donttest{ +lp <- get_eurostat("nama_10_lp_ulc") +lp$geo <- harmonize_country_code(lp$geo) +} +\dontshow{\}) # examplesIf} +} +\seealso{ +Other helpers: +\code{\link{cut_to_classes}()}, +\code{\link{dic_order}()}, +\code{\link{eurotime2date}()}, +\code{\link{eurotime2num}()}, +\code{\link{label_eurostat}()} } \author{ -Janne Huovari \email{janne.huovari@ptt.fi} +Janne Huovari \href{mailto:janne.huovari@ptt.fi}{janne.huovari@ptt.fi} } +\concept{helpers} diff --git a/man/harmonize_geo_code.Rd b/man/harmonize_geo_code.Rd new file mode 100755 index 00000000..8c9d99a7 --- /dev/null +++ b/man/harmonize_geo_code.Rd @@ -0,0 +1,43 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/deprecated_regions_functions.R +\name{harmonize_geo_code} +\alias{harmonize_geo_code} +\title{Harmonize NUTS region codes that changed with the \code{NUTS2016} +definition} +\usage{ +harmonize_geo_code(dat) +} +\arguments{ +\item{dat}{A Eurostat data frame downloaded with \code{\link[=get_eurostat]{get_eurostat()}}} +} +\value{ +An augmented data frame that explains potential problems and +possible solutions. +} +\description{ +Eurostat mixes \code{NUTS2013} and \code{NUTS2016} geographic +label codes in the \code{'geo'} column, which creates time-wise comparativity +issues. This deprecated function checked if you data is affected by this +problem and gives information on what to do. + +This function is deprecated, and a more general function was moved to +\code{\link[regions:validate_nuts_regions]{regions::validate_nuts_regions()}}. +} +\examples{ +dat <- eurostat::tgs00026 +regions::validate_nuts_regions(dat) +} +\seealso{ +\code{\link[regions:validate_nuts_regions]{regions::validate_nuts_regions()}} + +Other regions functions: +\code{\link{add_nuts_level}()}, +\code{\link{recode_to_nuts_2013}()}, +\code{\link{recode_to_nuts_2016}()}, +\code{\link{reexports}} +} +\author{ +Daniel Antal +} +\concept{regions functions} +\keyword{internal} diff --git a/man/label_eurostat.Rd b/man/label_eurostat.Rd old mode 100644 new mode 100755 index ab9ba103..21e9d855 --- a/man/label_eurostat.Rd +++ b/man/label_eurostat.Rd @@ -4,13 +4,21 @@ \alias{label_eurostat} \alias{label_eurostat_vars} \alias{label_eurostat_tables} -\title{Get Eurostat Codes} +\title{Get Eurostat Codes for data downloaded from new dissemination API} \usage{ -label_eurostat(x, dic = NULL, code = NULL, eu_order = FALSE, - lang = "en", countrycode = NULL, countrycode_nomatch = NULL, - custom_dic = NULL, fix_duplicated = FALSE) +label_eurostat( + x, + dic = NULL, + code = NULL, + eu_order = FALSE, + lang = "en", + countrycode = NULL, + countrycode_nomatch = NULL, + custom_dic = NULL, + fix_duplicated = FALSE +) -label_eurostat_vars(x, lang = "en") +label_eurostat_vars(x = NULL, id, lang = "en") label_eurostat_tables(x, lang = "en") } @@ -18,7 +26,7 @@ label_eurostat_tables(x, lang = "en") \item{x}{A character or a factor vector or a data_frame.} \item{dic}{A string (vector) naming eurostat dictionary or dictionaries. -If \code{NULL} (default) dictionry names taken from column names of +If \code{NULL} (default) dictionary names taken from column names of the data_frame.} \item{code}{For data_frames names of the column for which also code columns @@ -27,28 +35,33 @@ should be retained. The suffix "_code" is added to code column names.} \item{eu_order}{Logical. Should Eurostat ordering used for label levels. Affects only factors.} -\item{lang}{A character, code for language. Available are "en" (default), -"fr" and "de".} +\item{lang}{2-letter language code, default is "\code{en}" (English), other +options are "\code{fr}" (French) and "\code{de}" (German). Used for labeling datasets.} -\item{countrycode}{A \code{NULL} or a name of the coding scheme for -the \code{\link[countrycode]{countrycode}} +\item{countrycode}{A \code{NULL} or a name of the coding scheme for +the \code{\link[countrycode:countrycode]{countrycode::countrycode()}} to label "geo" variable with countrycode-package. It can be used to convert to short and long country names in many different languages. If \code{NULL} (default) eurostat dictionary is used instead.} -\item{countrycode_nomatch}{What to do when using the countrycode to label a "geo" and -countrycode fails to find a match, for example other than country codes like EU28. -the original code is used with +\item{countrycode_nomatch}{What to do when using the countrycode to label +a "geo" and countrycode fails to find a match, for example other than +country codes like EU28. The original code is used with a \code{NULL} (default), eurostat dictionary label is used with "eurostat", and \code{NA} is used with NA.} -\item{custom_dic}{a named vector or named list of named vectors to give an own dictionary -for (part of) codes. Names of the vector should be codes and values labels. List -can be used to spesify dictonaries and then list names should be dictionary codes.} +\item{custom_dic}{a named vector or named list of named vectors to give an +own dictionary for (part of) codes. Names of the vector should be codes +and values labels. List can be used to specify dictionaries and then +list names should be dictionary codes.} \item{fix_duplicated}{A logical. If TRUE, the code is added to the -duplicated label values. If FALSE (default) error is given if -labelling produce duplicates.} +duplicated label values. If FALSE (default) error is given if +labeling produce duplicates.} + +\item{id}{A unique identifier / code for the dataset of interest. If code is not +known \code{\link[=search_eurostat]{search_eurostat()}} function can be used to search Eurostat table +of contents.} } \value{ a vector or a data_frame. @@ -57,44 +70,71 @@ a vector or a data_frame. Get definitions for Eurostat codes from Eurostat dictionaries. } \details{ -A character or a factor vector of codes returns a corresponding - vector of definitions. \code{label_eurostat} labels also data_frames from - \code{\link{get_eurostat}}. For vectors a dictionary name have to be - supplied. For data_frames dictonary names are taken from column names. - "time" and "values" columns are returned as they were, so you can supply - data_frame from \code{\link{get_eurostat}} and get data_frame with - definitions instead of codes. - - Some Eurostat dictionaries includes dublicated labels. By default - dublicated labels cause an error, but they can be fixed automatically - with \code{fix_duplicated = TRUE}. +A character or a factor vector of codes returns a corresponding +vector of definitions. \code{\link[=label_eurostat]{label_eurostat()}} labels also data_frames from +\code{\link[=get_eurostat]{get_eurostat()}}. For vectors a dictionary name have to be +supplied. For data_frames dictionary names are taken from column names. +"time" and "values" columns are returned as they were, so you can supply +data_frame from \code{\link[=get_eurostat]{get_eurostat()}} and get data_frame with +definitions instead of codes. + +Some Eurostat dictionaries includes duplicated labels. By default +duplicated labels cause an error, but they can be fixed automatically +with \code{fix_duplicated = TRUE}. } \section{Functions}{ \itemize{ -\item \code{label_eurostat_vars}: Get definitions for variable (column) names. For -objects other than characters or factors definitions are get for names. +\item \code{label_eurostat_vars()}: Get definitions for variable (column) names. -\item \code{label_eurostat_tables}: Get definitions for table names -}} +\item \code{label_eurostat_tables()}: Get definitions for table names +}} \examples{ - \dontrun{ - lp <- get_eurostat("nama_10_lp_ulc") - lpl <- label_eurostat(lp) - str(lpl) - lpl_order <- label_eurostat(lp, eu_order = TRUE) - lpl_code <- label_eurostat(lp, code = "unit") - label_eurostat_vars(names(lp)) - label_eurostat_tables("nama_10_lp_ulc") - label_eurostat(c("FI", "DE", "EU28"), dic = "geo") - label_eurostat(c("FI", "DE", "EU28"), dic = "geo", custom_dic = c(DE = "Germany")) - label_eurostat(c("FI", "DE", "EU28"), dic = "geo", countrycode = "country.name", - custom_dic = c(EU28 = "EU")) - label_eurostat(c("FI", "DE", "EU28"), dic = "geo", countrycode = "country.name") - # In Finnish - label_eurostat(c("FI", "DE", "EU28"), dic = "geo", countrycode = "cldr.short.fi") - } +\dontrun{ +lp <- get_eurostat("nama_10_lp_ulc") +lpl <- label_eurostat(lp) +str(lpl) +lpl_order <- label_eurostat(lp, eu_order = TRUE) +lpl_code <- label_eurostat(lp, code = "unit") +# Note that the dataset id must be provided in label_eurostat_vars +label_eurostat_vars(id = "nama_10_lp_ulc", x = "geo", lang = "en") +label_eurostat_tables("nama_10_lp_ulc") +label_eurostat(c("FI", "DE", "EU28"), dic = "geo") +label_eurostat( + c("FI", "DE", "EU28"), + dic = "geo", + custom_dic = c(DE = "Germany") +) +label_eurostat( + c("FI", "DE", "EU28"), + dic = "geo", countrycode = "country.name", + custom_dic = c(EU28 = "EU") +) +label_eurostat( + c("FI", "DE", "EU28"), + dic = "geo", + countrycode = "country.name" +) +# In Finnish +label_eurostat( + c("FI", "DE", "EU28"), + dic = "geo", + countrycode = "cldr.short.fi" +) +} + +} +\seealso{ +\code{\link[countrycode:countrycode]{countrycode::countrycode()}} + +Other helpers: +\code{\link{cut_to_classes}()}, +\code{\link{dic_order}()}, +\code{\link{eurotime2date}()}, +\code{\link{eurotime2num}()}, +\code{\link{harmonize_country_code}()} } \author{ -Janne Huovari \email{janne.huovari@ptt.fi} +Janne Huovari \href{mailto:janne.huovari@ptt.fi}{janne.huovari@ptt.fi} } +\concept{helpers} diff --git a/man/list_eurostat_cache_items.Rd b/man/list_eurostat_cache_items.Rd new file mode 100644 index 00000000..7bf3317f --- /dev/null +++ b/man/list_eurostat_cache_items.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/set_eurostat_cache.R +\name{list_eurostat_cache_items} +\alias{list_eurostat_cache_items} +\title{Output cache information as data.frame} +\usage{ +list_eurostat_cache_items(cache_dir = NULL) +} +\arguments{ +\item{cache_dir}{a path to a cache directory. \code{NULL} (default) uses and creates +'eurostat' directory in the temporary directory defined by base R +\code{\link[=tempdir]{tempdir()}} function. The user can set the cache directory to an existing +directory by using this argument. The cache directory can also be set with +\code{\link[=set_eurostat_cache_dir]{set_eurostat_cache_dir()}} function.} +} +\value{ +A data.frame object with 3 columns: dataset code, download date and +query md5 hash +} +\description{ +Parses cache_list.json file and returns a data.frame +} diff --git a/man/recode_to_nuts_2013.Rd b/man/recode_to_nuts_2013.Rd new file mode 100755 index 00000000..92045a5b --- /dev/null +++ b/man/recode_to_nuts_2013.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/deprecated_regions_functions.R +\name{recode_to_nuts_2013} +\alias{recode_to_nuts_2013} +\title{Recode geo labels and rename regions from NUTS2016 to NUTS2013} +\usage{ +recode_to_nuts_2013(dat) +} +\arguments{ +\item{dat}{A Eurostat data frame downloaded with +\code{\link[=get_eurostat]{get_eurostat()}}.} +} +\value{ +An augmented and potentially relabelled data frame which +contains all formerly \code{'NUTS2013'} definition geo labels in the +\code{'NUTS2016'} vocabulary when only the code changed, but the +boundary did not. It also contains some information on other geo labels +that cannot be brought to the current \code{'NUTS2013'} definition. +Furthermore, when the official name of the region changed, it will use +the new name (if the otherwise the region boundary did not change.) +If not called before, the function will use the helper function +\code{\link[=harmonize_geo_code]{harmonize_geo_code()}} +} +\description{ +Eurostat mixes NUTS2013 and NUTS2016 geographic label codes +in the \code{'geo'} column, which creates time-wise comparativity issues. + +This function is deprecated, and a more general function was moved to +\verb{[regions::recode_nuts()]}. +} +\examples{ +test_regional_codes <- data.frame( + geo = c("FRB", "FRE", "UKN02", "IE022", "FR243", "FRB03"), + time = c(rep(as.Date("2014-01-01"), 5), as.Date("2015-01-01")), + values = c(1:6), + control = c( + "Changed from NUTS2 to NUTS1", + "New region NUTS2016 only", + "Discontinued region NUTS2013", + "Boundary shift NUTS2013", + "Recoded in NUTS2013", + "Recoded in NUTS2016" + ) +) + +recode_to_nuts_2013(test_regional_codes) +} +\seealso{ +\code{\link[regions:recode_nuts]{regions::recode_nuts()}} + +Other regions functions: +\code{\link{add_nuts_level}()}, +\code{\link{harmonize_geo_code}()}, +\code{\link{recode_to_nuts_2016}()}, +\code{\link{reexports}} +} +\author{ +Daniel Antal +} +\concept{regions functions} +\keyword{internal} diff --git a/man/recode_to_nuts_2016.Rd b/man/recode_to_nuts_2016.Rd new file mode 100755 index 00000000..65317ef5 --- /dev/null +++ b/man/recode_to_nuts_2016.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/deprecated_regions_functions.R +\name{recode_to_nuts_2016} +\alias{recode_to_nuts_2016} +\title{Recode geo labels and rename regions from NUTS2013 to NUTS2016} +\usage{ +recode_to_nuts_2016(dat) +} +\arguments{ +\item{dat}{A Eurostat data frame downloaded with +\code{\link[=get_eurostat]{get_eurostat()}}.} +} +\value{ +An augmented and potentially relabelled data frame which +contains all formerly \code{'NUTS2013'} definition geo labels in the +\code{'NUTS2016'} vocabulary when only the code changed, but the +boundary did not. It also contains some information on other geo labels +that cannot be brought to the current \code{'NUTS2016'} definition. +Furthermore, when the official name of the region changed, it will use +the new name (if the otherwise the region boundary did not change.) +If not called before, the function will use the helper function +\code{\link[=harmonize_geo_code]{harmonize_geo_code()}} +} +\description{ +Eurostat mixes NUTS2013 and NUTS2016 geographic label codes +in the \code{'geo'} column, which creates time-wise comparativity issues. + +This function is deprecated, and a more general function was moved to +\verb{[regions::recode_nuts()]}. +} +\examples{ +test_regional_codes <- data.frame( + geo = c("FRB", "FRE", "UKN02", "IE022", "FR243", "FRB03"), + time = c(rep(as.Date("2014-01-01"), 5), as.Date("2015-01-01")), + values = c(1:6), + control = c( + "Changed from NUTS2 to NUTS1", + "New region NUTS2016 only", + "Discontinued region NUTS2013", + "Boundary shift NUTS2013", + "Recoded in NUTS2013", + "Recoded in NUTS2016" + ) +) + +recode_to_nuts_2016(test_regional_codes) +} +\seealso{ +\code{\link[regions:recode_nuts]{regions::recode_nuts()}} + +Other regions functions: +\code{\link{add_nuts_level}()}, +\code{\link{harmonize_geo_code}()}, +\code{\link{recode_to_nuts_2013}()}, +\code{\link{reexports}} +} +\author{ +Daniel Antal +} +\concept{regions functions} +\keyword{internal} diff --git a/man/reexports.Rd b/man/reexports.Rd new file mode 100644 index 00000000..d2b0e04f --- /dev/null +++ b/man/reexports.Rd @@ -0,0 +1,124 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/regional_coding_functions.R +\docType{import} +\name{reexports} +\alias{reexports} +\alias{recode_nuts} +\alias{validate_nuts_regions} +\alias{validate_geo_code} +\title{Recode Region Codes From Source To Target NUTS Typology} +\arguments{ +\item{dat}{A data frame with a 3-5 character \code{geo_var} +variable to be validated.} + +\item{geo_var}{Defaults to \code{"geo"}. The variable that contains +the 3-5 character geo codes to be validated.} + +\item{geo}{A vector of geographical code to validate.} + +\item{nuts_year}{A valid NUTS edition year.} +} +\value{ +The original data frame with a \code{'geo_var'} column is extended +with a \code{'typology'} column that states in which typology is the \code{'geo_var'} +a valid code. For invalid codes, looks up potential reasons of invalidity +and adds them to the \code{'typology_change'} column, and at last it +adds a column of character vector containing the desired codes in the +target typology, for example, in the NUTS2013 typology. + +Returns the original \code{dat} data frame with a column +that specifies the comformity with the NUTS definition of the year +\code{nuts_year}. + +A character list with the valid typology, or 'invalid' in the cases +when the geo coding is not valid. +} +\details{ +While country codes are technically not part of the NUTS typologies, +Eurostat de facto uses a \code{NUTS0} typology to identify countries. +This de facto typology has three exception which are handled by the +\link[regions:validate_nuts_countries]{validate_nuts_countries} function. + +NUTS typologies have different versions, therefore the conformity +is validated with one specific versions, which can be any of these: +\code{1999}, \code{2003}, \code{2006}, \code{2010}, +\code{2013}, the currently used \code{2016} and the already +announced and defined \code{2021}. + +The NUTS typology was codified with the \code{NUTS2003}, and the +pre-1999 NUTS typologies may confuse programmatic data processing, +given that some NUTS1 regions were identified with country codes +in smaller countries that had no \code{NUTS1} divisions. + +Currently the \code{2016} is used by Eurostat, but many datasets +still contain \code{2013} and sometimes earlier metadata. +} +\examples{ +{ +foo <- data.frame ( + geo = c("FR", "DEE32", "UKI3" , + "HU12", "DED", + "FRK"), + values = runif(6, 0, 100 ), + stringsAsFactors = FALSE ) + +recode_nuts(foo, nuts_year = 2013) +} +\donttest{ +my_reg_data <- data.frame( + geo = c( + "BE1", "HU102", "FR1", + "DED", "FR7", "TR", "DED2", + "EL", "XK", "GB" + ), + values = runif(10) +) + +validate_nuts_regions(my_reg_data) + +validate_nuts_regions(my_reg_data, nuts_year = 2013) + +validate_nuts_regions(my_reg_data, nuts_year = 2003) +} +\donttest{ +my_reg_data <- data.frame( + geo = c( + "BE1", "HU102", "FR1", + "DED", "FR7", "TR", "DED2", + "EL", "XK", "GB" + ), + values = runif(10) +) + +validate_geo_code(my_reg_data$geo) +} +} +\seealso{ +Other regions functions: +\code{\link{add_nuts_level}()}, +\code{\link{harmonize_geo_code}()}, +\code{\link{recode_to_nuts_2013}()}, +\code{\link{recode_to_nuts_2016}()} + +Other regions functions: +\code{\link{add_nuts_level}()}, +\code{\link{harmonize_geo_code}()}, +\code{\link{recode_to_nuts_2013}()}, +\code{\link{recode_to_nuts_2016}()} + +Other regions functions: +\code{\link{add_nuts_level}()}, +\code{\link{harmonize_geo_code}()}, +\code{\link{recode_to_nuts_2013}()}, +\code{\link{recode_to_nuts_2016}()} +} +\concept{regions functions} +\keyword{internal} +\description{ +These objects are imported from other packages. Follow the links +below to see their documentation. + +\describe{ + \item{regions}{\code{\link[regions]{recode_nuts}}, \code{\link[regions]{validate_geo_code}}, \code{\link[regions]{validate_nuts_regions}}} +}} + diff --git a/man/search_eurostat.Rd b/man/search_eurostat.Rd index b0bae44e..2f96125b 100755 --- a/man/search_eurostat.Rd +++ b/man/search_eurostat.Rd @@ -2,64 +2,140 @@ % Please edit documentation in R/search_eurostat.R \name{search_eurostat} \alias{search_eurostat} -\alias{grepEurostatTOC} \title{Grep Datasets Titles from Eurostat} \usage{ -search_eurostat(pattern, type = "dataset", fixed = TRUE) - -grepEurostatTOC(pattern, type = "dataset") +search_eurostat( + pattern, + type = "dataset", + column = "title", + fixed = TRUE, + lang = "en" +) } \arguments{ -\item{pattern}{Character, datasets, folder or tables with this pattern in -the description will be returned (depending on the 'type' argument)} +\item{pattern}{Text string that is used to search from dataset, folder or table titles, +depending on the type argument.} + +\item{type}{Selection for types of datasets to be searched. Default is \code{dataset}, other +possible options are \code{table}, \code{folder} and \code{all} for all types.} -\item{type}{Grep the Eurostat table of contents either for -'dataset' (default), 'folder', 'table' or "all" (for all types).} +\item{column}{Selection for the column of TOC where search is done. Default is \code{title}, +other possible option is \code{code}.} -\item{fixed}{logical. If TRUE, pattern is a string to be matched as is. -Change to FALSE if more complex regex matching is needed.} +\item{fixed}{logical. If TRUE (default), pattern is a string to be matched as is. +See \code{grep()} documentation for more information.} + +\item{lang}{2-letter language code, default is "\code{en}" (English), other +options are "\code{fr}" (French) and "\code{de}" (German). Used for labeling datasets.} } \value{ -A tibble with eight columns - \itemize{ - \item{title}{The name of dataset of theme} -\item{code}{The codename of dataset of theme, will be used by the get_eurostat and get_eurostat_raw functions.} -\item{type}{Is it a dataset, folder or table.} -\item{last.update.of.data, last.table.structure.change, data.start, data.end}{Dates.} - } +A tibble with nine columns: +\describe{ +\item{title}{Dataset title in English (default)} +\item{code}{ Each item (dataset, table and folder) of the TOC has a +unique code which allows it to be identified in the API. Used in the +\code{\link[=get_eurostat]{get_eurostat()}} and \code{\link[=get_eurostat_raw]{get_eurostat_raw()}} functions to retrieve datasets.} +\item{type}{dataset, folder or table} +\item{last.update.of.data}{Date, indicates the last time the +dataset/table was updated (format \code{DD.MM.YYYY} or \verb{\%d.\%m.\%Y})} +\item{last.table.structure.change}{Date, indicates the last time the +dataset/table structure was modified (format \code{DD.MM.YYYY} or \verb{\%d.\%m.\%Y})} +\item{data.start}{Date of the oldest value included in the dataset +(if available) (format usually \code{YYYY} or \verb{\%Y} but can also be \code{YYYY-MM}, +\code{YYYY-MM-DD}, \code{YYYY-SN}, \code{YYYY-QN} etc.)} +\item{data.end}{Date of the most recent value included in the dataset +(if available) (format usually \code{YYYY} or \verb{\%Y} but can also be \code{YYYY-MM}, +\code{YYYY-MM-DD}, \code{YYYY-SN}, \code{YYYY-QN} etc.)} +\item{values}{Number of actual values included in the dataset} +\item{hierarchy}{Hierarchy of the data navigation tree, represented +in the original txt file by a 4-spaces indentation prefix in the title} +} } \description{ -Lists names of dataset from eurostat with the particular -pattern in the description. +Lists datasets from eurostat table of contents with the +particular pattern in item titles. } \details{ Downloads list of all datasets available on eurostat and return list of names of datasets that contains particular pattern in the dataset description. E.g. all datasets related to education of teaching. + +If you wish to perform searches on other fields than item title, +you can download the Eurostat Table of Contents manually using +\code{get_eurostat_toc()} function and use \code{grep()} function normally. The data +browser on Eurostat website may also return useful results. +} +\section{Data source: Eurostat Table of Contents}{ +The Eurostat Table of Contents (TOC) is downloaded from +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=en} +(default) or from French or German language variants: +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=fr} +\url{https://ec.europa.eu/eurostat/api/dissemination/catalogue/toc/txt?lang=de} + +See Eurostat documentation on TOC items: +\url{https://wikis.ec.europa.eu/display/EUROSTATHELP/API+-+Detailed+guidelines+-+Catalogue+API+-+TOC} } -\section{Functions}{ -\itemize{ -\item \code{grepEurostatTOC}: Old deprecated version -}} \examples{ -\dontrun{ - tmp <- search_eurostat("education") - head(tmp) - # Use "fixed = TRUE" when pattern has characters that would need escaping. - # Here, parentheses would normally need to be escaped in regex - tmp <- search_eurostat("Live births (total) by NUTS 3 region", fixed = TRUE) +\dontshow{if (check_access_to_data()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\donttest{ +tmp <- search_eurostat("education") +head(tmp) +# Use "fixed = TRUE" when pattern has characters that would need escaping. +# Here, parentheses would normally need to be escaped in regex +tmp <- search_eurostat("Live births (total) by NUTS 3 region", fixed = TRUE) } +\dontshow{\}) # examplesIf} } \references{ -See citation("eurostat") +See \code{citation("eurostat")}: + +\if{html}{\out{
    }}\preformatted{Kindly cite the eurostat R package as follows: + + Lahti L., Huovari J., Kainu M., and Biecek P. (2017). Retrieval and + analysis of Eurostat open data with the eurostat package. The R + Journal 9(1), pp. 385-392. doi: 10.32614/RJ-2017-019 + +A BibTeX entry for LaTeX users is + + @Article\{10.32614/RJ-2017-019, + title = \{Retrieval and Analysis of Eurostat Open Data with the eurostat Package\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek\}, + journal = \{The R Journal\}, + volume = \{9\}, + number = \{1\}, + pages = \{385--392\}, + year = \{2017\}, + doi = \{10.32614/RJ-2017-019\}, + url = \{https://doi.org/10.32614/RJ-2017-019\}, + \} + + Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., + and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data + [Computer software]. R package version 4.0.0. + https://github.com/rOpenGov/eurostat + +A BibTeX entry for LaTeX users is + + @Misc\{eurostat, + title = \{eurostat: Tools for Eurostat Open Data\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek and Diego Hernangomez and Daniel Antal and Pyry Kantanen\}, + url = \{https://github.com/rOpenGov/eurostat\}, + type = \{Computer software\}, + year = \{2023\}, + note = \{R package version 4.0.0\}, + \} +}\if{html}{\out{
    }} + +When citing data downloaded from Eurostat, see section "Citing Eurostat data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. } \seealso{ -\code{\link{get_eurostat}}, \code{\link{get_eurostat_toc}} +\code{\link[=get_eurostat]{get_eurostat()}}, \code{\link[=search_eurostat]{search_eurostat()}} } \author{ -Przemyslaw Biecek and Leo Lahti \email{ropengov-forum@googlegroups.com} +Przemyslaw Biecek and Leo Lahti \href{mailto:ropengov-forum@googlegroups.com}{ropengov-forum@googlegroups.com} } \keyword{database} \keyword{utilities} diff --git a/man/set_eurostat_cache_dir.Rd b/man/set_eurostat_cache_dir.Rd new file mode 100644 index 00000000..df9e50e9 --- /dev/null +++ b/man/set_eurostat_cache_dir.Rd @@ -0,0 +1,71 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/set_eurostat_cache.R +\name{set_eurostat_cache_dir} +\alias{set_eurostat_cache_dir} +\title{Set Eurostat Cache} +\usage{ +set_eurostat_cache_dir( + cache_dir, + overwrite = FALSE, + install = FALSE, + verbose = TRUE +) +} +\arguments{ +\item{cache_dir}{A path to a cache directory. On missing value the function +would store the cached files on a temporary dir (See +\code{\link[base:tempfile]{base::tempdir()}}).} + +\item{overwrite}{If this is set to \code{TRUE}, it will overwrite an existing +\code{EUROSTAT_CACHE_DIR} that you already have in local machine.} + +\item{install}{if \code{TRUE}, will install the key in your local machine for +use in future sessions. Defaults to \code{FALSE}. If \code{cache_dir} is +\code{FALSE} this parameter is set to \code{FALSE} automatically.} + +\item{verbose}{Logical, displays information. Useful for debugging, +default is \code{FALSE}.} +} +\value{ +An (invisible) character with the path to your \code{cache_dir}. +} +\description{ +This function will store your \code{cache_dir} path on your local machine +and would load it for future sessions. Type +\code{Sys.getenv("EUROSTAT_CACHE_DIR")} to +find your cached path. + +Alternatively, you can store the \code{cache_dir} manually with the following +options: +\itemize{ +\item Run \code{Sys.setenv(EUROSTAT_CACHE_DIR = "cache_dir")}. You +would need to run this command on each session +(Similar to \code{install = FALSE}). +\item Set \code{options(eurostat_cache_dir = "cache_dir")}. Similar to +the previous option. This is provided for backwards compatibility +purposes. +\item Write this line on your .Renviron file: +\code{EUROSTAT_CACHE_DIR = "value_for_cache_dir"} (same behavior than +\code{install = TRUE}). This would store your \code{cache_dir} +permanently. +} +} +\examples{ + +# Don't run this! It would modify your current state +\dontrun{ +set_eurostat_cache_dir(verbose = TRUE) +} + +Sys.getenv("EUROSTAT_CACHE_DIR") +} +\seealso{ +\code{\link[rappdirs:user_data_dir]{rappdirs::user_config_dir()}} + +Other cache utilities: +\code{\link{clean_eurostat_cache}()} +} +\author{ +Diego Hernangómez +} +\concept{cache utilities} diff --git a/man/set_eurostat_toc.Rd b/man/set_eurostat_toc.Rd index 4d2a64da..1dd96891 100755 --- a/man/set_eurostat_toc.Rd +++ b/man/set_eurostat_toc.Rd @@ -4,10 +4,11 @@ \alias{set_eurostat_toc} \title{Set Eurostat TOC} \usage{ -set_eurostat_toc(...) +set_eurostat_toc(lang = "en") } \arguments{ -\item{...}{Arguments to be passed} +\item{lang}{2-letter language code, default is "\code{en}" (English), other +options are "\code{fr}" (French) and "\code{de}" (German). Used for labeling datasets.} } \value{ Empty element @@ -18,7 +19,11 @@ Internal function. \references{ see citation("eurostat") } +\seealso{ +\code{\link[=get_eurostat_toc]{get_eurostat_toc()}} \code{\link[=toc_count_children]{toc_count_children()}} \code{\link[=toc_determine_hierarchy]{toc_determine_hierarchy()}} +\code{\link[=toc_list_children]{toc_list_children()}} \code{\link[=toc_count_whitespace]{toc_count_whitespace()}} +} \author{ -Przemyslaw Biecek and Leo Lahti \email{ropengov-forum@googlegroups.com} +Przemyslaw Biecek and Leo Lahti \href{mailto:ropengov-forum@googlegroups.com}{ropengov-forum@googlegroups.com} } \keyword{internal} diff --git a/man/tgs00026.Rd b/man/tgs00026.Rd index 74299d89..548e1dfc 100755 --- a/man/tgs00026.Rd +++ b/man/tgs00026.Rd @@ -4,7 +4,9 @@ \name{tgs00026} \alias{tgs00026} \title{Auxiliary Data} -\format{data_frame} +\format{ +data_frame +} \usage{ tgs00026 } @@ -12,6 +14,14 @@ tgs00026 Auxiliary Data Sets } \details{ +Disposable income of private households by NUTS 2 regions Retrieved with: \code{tgs00026 <- get_eurostat("tgs00026", time_format = "raw") } +Data retrieval date: 2022-06-27 +} +\seealso{ +Other datasets: +\code{\link{eu_countries}}, +\code{\link{eurostat_geodata_60_2016}} } +\concept{datasets} \keyword{datasets} diff --git a/man/tidy_eurostat.Rd b/man/tidy_eurostat.Rd index d542c23e..55156ae6 100755 --- a/man/tidy_eurostat.Rd +++ b/man/tidy_eurostat.Rd @@ -4,47 +4,118 @@ \alias{tidy_eurostat} \title{Transform Data into Row-Column-Value Format} \usage{ -tidy_eurostat(dat, time_format = "date", select_time = NULL, - stringsAsFactors = default.stringsAsFactors(), keepFlags = FALSE) +tidy_eurostat( + dat, + time_format = "date", + select_time = NULL, + stringsAsFactors = FALSE, + keepFlags = FALSE, + use.data.table = FALSE +) } \arguments{ -\item{dat}{a data_frame from \code{\link{get_eurostat_raw}}.} +\item{dat}{a data_frame from \code{\link[=get_eurostat_raw]{get_eurostat_raw()}}.} -\item{time_format}{a string giving a type of the conversion of the - time column from the eurostat format. - A "date" (default) convers to a \code{\link{Date}} - with a first date of the period. A "date_last" - convers to a \code{\link{Date}} with -a last date of the period. A "num" convers to a numeric and "raw" -does not do conversion. See \code{\link{eurotime2date}} and -\code{\link{eurotime2num}}.} +\item{time_format}{a string giving a type of the conversion of the time column from the +eurostat format. The default argument "\code{date}" converts to a \code{\link[=Date]{Date()}} class +with the date being the first day of the period. A "\code{date_last}" argument +converts the dataset date to a \code{\link[=Date]{Date()}} class object with the difference +that the exact date is the last date of the period. Period can be year, +semester (half year), quarter, month, or week (See \code{\link[=eurotime2date]{eurotime2date()}} for +more information). +Argument "\code{num}" converts the date into a numeric (integer) meaning that +the first day of the year 2000 is close to 2000.01 and the last day of the +year is close to 2000.99 (see \code{\link[=eurotime2num]{eurotime2num()}} for more information). +Using the argument "\code{raw}" preserves the dates as they were in the original +Eurostat data.} -\item{select_time}{a character symbol for a time frequence or NULL -(default).} +\item{select_time}{a character symbol for a time frequency or \code{NULL}, +which is used by default as most datasets have just one time +frequency. For datasets with multiple time +frequencies, select one or more of the desired frequencies with: +"Y" (or "A") = annual, "S" = semi-annual / semester, "Q" = quarterly, +"M" = monthly, "W" = weekly. For all frequencies in same data +frame \code{time_format = "raw"} should be used.} -\item{stringsAsFactors}{if \code{TRUE} (the default) variables are -converted to factors in original Eurostat order. If \code{FALSE} -they are returned as strings.} +\item{stringsAsFactors}{if \code{TRUE} (the default) variables are converted to factors in the original +Eurostat order. If \code{FALSE} they are returned as strings.} \item{keepFlags}{a logical whether the flags (e.g. "confidential", "provisional") should be kept in a separate column or if they -can be removed. Default is \code{FALSE}} +can be removed. Default is \code{FALSE}. For flag values see: +\url{https://ec.europa.eu/eurostat/data/database/information}. +Also possible non-real zero "0n" is indicated in flags column. +Flags are not available for eurostat API, so \code{keepFlags} +can not be used with a \code{filters}.} + +\item{use.data.table}{Use faster data.table functions? Default is FALSE. +On Windows requires that RTools is installed.} } \value{ -tibble in the molten format with the last column 'values'. +tibble in the melted format with the last column 'values'. } \description{ -Transform raw Eurostat data table into the row-column-value -format (RCV). +Transform raw Eurostat data table downloaded from the +API into a tidy row-column-value format (RCV). +} +\examples{ +\dontrun{ +# Example of a dataset with multiple time series +get_eurostat("AVIA_GOR_ME", + time_format = "date_last", + cache = F + ) +} + } \references{ -See citation("eurostat"). +See \code{citation("eurostat")}: + +\if{html}{\out{
    }}\preformatted{Kindly cite the eurostat R package as follows: + + Lahti L., Huovari J., Kainu M., and Biecek P. (2017). Retrieval and + analysis of Eurostat open data with the eurostat package. The R + Journal 9(1), pp. 385-392. doi: 10.32614/RJ-2017-019 + +A BibTeX entry for LaTeX users is + + @Article\{10.32614/RJ-2017-019, + title = \{Retrieval and Analysis of Eurostat Open Data with the eurostat Package\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek\}, + journal = \{The R Journal\}, + volume = \{9\}, + number = \{1\}, + pages = \{385--392\}, + year = \{2017\}, + doi = \{10.32614/RJ-2017-019\}, + url = \{https://doi.org/10.32614/RJ-2017-019\}, + \} + + Lahti, L., Huovari J., Kainu M., Biecek P., Hernangomez D., Antal D., + and Kantanen P. (2023). eurostat: Tools for Eurostat Open Data + [Computer software]. R package version 4.0.0. + https://github.com/rOpenGov/eurostat + +A BibTeX entry for LaTeX users is + + @Misc\{eurostat, + title = \{eurostat: Tools for Eurostat Open Data\}, + author = \{Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek and Diego Hernangomez and Daniel Antal and Pyry Kantanen\}, + url = \{https://github.com/rOpenGov/eurostat\}, + type = \{Computer software\}, + year = \{2023\}, + note = \{R package version 4.0.0\}, + \} +}\if{html}{\out{
    }} + +When citing data downloaded from Eurostat, see section "Citing Eurostat data" +in \code{\link[=get_eurostat]{get_eurostat()}} documentation. } \seealso{ -\code{\link{get_eurostat}} +\code{\link[=get_eurostat]{get_eurostat()}}, \code{\link[=convert_time_col]{convert_time_col()}}, \code{\link[=eurotime2date]{eurotime2date()}} } \author{ -Przemyslaw Biecek, Leo Lahti and Janne Huovari \email{ropengov-forum@googlegroups.com} \url{http://github.com/ropengov/eurostat} +Przemyslaw Biecek, Leo Lahti, Janne Huovari and Pyry Kantanen } \keyword{database} \keyword{internal} diff --git a/man/toc_count_children.Rd b/man/toc_count_children.Rd new file mode 100644 index 00000000..2822d9a1 --- /dev/null +++ b/man/toc_count_children.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/set_eurostat_toc.R +\name{toc_count_children} +\alias{toc_count_children} +\title{Count number of children} +\usage{ +toc_count_children(code) +} +\arguments{ +\item{code}{Eurostat TOC item code (folder, dataset, table)} +} +\description{ +Determine how many children a certain TOC item (usually a folder) has. +} +\seealso{ +\code{\link[=get_eurostat_toc]{get_eurostat_toc()}} \code{\link[=toc_count_children]{toc_count_children()}} \code{\link[=toc_determine_hierarchy]{toc_determine_hierarchy()}} +\code{\link[=toc_list_children]{toc_list_children()}} \code{\link[=toc_count_whitespace]{toc_count_whitespace()}} +} +\author{ +Pyry Kantanen +} +\keyword{internal} diff --git a/man/toc_count_whitespace.Rd b/man/toc_count_whitespace.Rd new file mode 100644 index 00000000..ca7f08e1 --- /dev/null +++ b/man/toc_count_whitespace.Rd @@ -0,0 +1,47 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/set_eurostat_toc.R +\name{toc_count_whitespace} +\alias{toc_count_whitespace} +\title{Count white space at the start of the title} +\usage{ +toc_count_whitespace(input_string) +} +\arguments{ +\item{input_string}{A string containing Eurostat TOC titles} +} +\value{ +Numeric (number of white space characters) +} +\description{ +Counts the number of white space characters at the start +of the string. +} +\details{ +Used in toc_determine_hierarchy function to determine hierarchy. +Hierarchy is defined in Eurostat .txt format TOC files by the number of +white space characters at intervals of four. For example, +" Foo" (4 white space characters) is one level higher than +" Bar" (8 white space characters). +"Database by themes" (0 white space characters before the first +alphanumeric character) is highest in the hierarchy. + +The function will return a warning if the input has white space in anything +else than as increments of 4. 0, 4, 8... are acceptable but 3, 6, 10... +are not. +} +\examples{ +strings <- c(" abc", " cdf", "no_spaces") +for (string in strings) { + whitespace_count <- eurostat:::toc_count_whitespace(string) + cat("String:", string, "\tWhitespace Count:", whitespace_count, "\n") +} + +} +\seealso{ +\code{\link[=get_eurostat_toc]{get_eurostat_toc()}} \code{\link[=toc_count_children]{toc_count_children()}} \code{\link[=toc_determine_hierarchy]{toc_determine_hierarchy()}} +\code{\link[=toc_list_children]{toc_list_children()}} \code{\link[=toc_count_whitespace]{toc_count_whitespace()}} +} +\author{ +Pyry Kantanen +} +\keyword{internal} diff --git a/man/toc_determine_hierarchy.Rd b/man/toc_determine_hierarchy.Rd new file mode 100644 index 00000000..584ca2f8 --- /dev/null +++ b/man/toc_determine_hierarchy.Rd @@ -0,0 +1,44 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/set_eurostat_toc.R +\name{toc_determine_hierarchy} +\alias{toc_determine_hierarchy} +\title{Determine level in hierarchy} +\usage{ +toc_determine_hierarchy(input_string) +} +\arguments{ +\item{input_string}{A string containing Eurostat TOC titles} +} +\value{ +Numeric +} +\description{ +Divides the number of spaces before alphanumeric characters +with 4 and uses the result to determine hierarchy. Top level is 0. +} +\details{ +Used in toc_determine_hierarchy function to determine hierarchy. +Hierarchy is defined in Eurostat .txt format TOC files by the number of +white space characters at intervals of four. For example, +" Foo" (4 white space characters) is one level higher than +" Bar" (8 white space characters). +"Database by themes" (0 white space characters before the first +alphanumeric character) is highest in the hierarchy. + +The function will return a warning if the input has white space in anything +else than as increments of 4. 0, 4, 8... are acceptable but 3, 6, 10... +are not. +} +\examples{ +strings <- c(" abc", " cdf", "no_spaces") +eurostat:::toc_determine_hierarchy(strings) + +} +\seealso{ +\code{\link[=get_eurostat_toc]{get_eurostat_toc()}} \code{\link[=toc_count_children]{toc_count_children()}} \code{\link[=toc_determine_hierarchy]{toc_determine_hierarchy()}} +\code{\link[=toc_list_children]{toc_list_children()}} \code{\link[=toc_count_whitespace]{toc_count_whitespace()}} +} +\author{ +Pyry Kantanen +} +\keyword{internal} diff --git a/man/toc_list_children.Rd b/man/toc_list_children.Rd new file mode 100644 index 00000000..69d8c769 --- /dev/null +++ b/man/toc_list_children.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/set_eurostat_toc.R +\name{toc_list_children} +\alias{toc_list_children} +\title{List children} +\usage{ +toc_list_children(code) +} +\arguments{ +\item{code}{Eurostat TOC item code (folder, dataset, table)} +} +\description{ +List children of a specific folder. +} +\seealso{ +\code{\link[=get_eurostat_toc]{get_eurostat_toc()}} \code{\link[=toc_count_children]{toc_count_children()}} \code{\link[=toc_determine_hierarchy]{toc_determine_hierarchy()}} +\code{\link[=toc_list_children]{toc_list_children()}} \code{\link[=toc_count_whitespace]{toc_count_whitespace()}} +} +\author{ +Pyry Kantanen +} +\keyword{internal} diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml new file mode 100644 index 00000000..fd46adc1 --- /dev/null +++ b/pkgdown/_pkgdown.yml @@ -0,0 +1,22 @@ +url: https://ropengov.github.io/eurostat/ +template: + package: rogtemplate +reference: +- title: Get data from Eurostat + contents: + - starts_with("get_") + - starts_with("search_") +- title: Access and Cache management + contents: + - has_concept("cache utilities") + - check_access_to_data + - list_eurostat_cache_items +- title: Additional functions + contents: has_concept("helpers") +- title: Datasets + contents: has_concept("datasets") +- title: About the package + contents: eurostat +- title: Deprecated or defunct + contents: + - eurostat-defunct diff --git a/pkgdown/favicon/apple-touch-icon-120x120.png b/pkgdown/favicon/apple-touch-icon-120x120.png new file mode 100644 index 00000000..28060347 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-120x120.png differ diff --git a/pkgdown/favicon/apple-touch-icon-152x152.png b/pkgdown/favicon/apple-touch-icon-152x152.png new file mode 100644 index 00000000..cb3ea573 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-152x152.png differ diff --git a/pkgdown/favicon/apple-touch-icon-180x180.png 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b/pkgdown/favicon/favicon-16x16.png differ diff --git a/pkgdown/favicon/favicon-32x32.png b/pkgdown/favicon/favicon-32x32.png new file mode 100644 index 00000000..ef5d20de Binary files /dev/null and b/pkgdown/favicon/favicon-32x32.png differ diff --git a/pkgdown/favicon/favicon.ico b/pkgdown/favicon/favicon.ico new file mode 100644 index 00000000..0a4d4605 Binary files /dev/null and b/pkgdown/favicon/favicon.ico differ diff --git a/revdep/.gitignore b/revdep/.gitignore new file mode 100644 index 00000000..31f6c40d --- /dev/null +++ b/revdep/.gitignore @@ -0,0 +1,6 @@ +checks +library +checks.noindex +library.noindex +data.sqlite +*.html diff --git a/revdep/README.md b/revdep/README.md new file mode 100644 index 00000000..5aceec41 --- /dev/null +++ b/revdep/README.md @@ -0,0 +1,85 @@ +# Platform + +|field |value | +|:--------|:------------------------------------------| +|version |R version 4.3.1 (2023-06-16 ucrt) | +|os |Windows 11 x64 (build 22621) | +|system |x86_64, mingw32 | +|ui |RStudio | +|language |(EN) | +|collate |Spanish_Spain.utf8 | +|ctype |Spanish_Spain.utf8 | +|tz |Europe/Madrid | +|date |2023-07-01 | +|rstudio |2023.06.0+421 Mountain Hydrangea (desktop) | +|pandoc |2.19.2 @ C:\PROGRA~1\Pandoc\pandoc.exe | + +# Dependencies + +|package |old |new |Δ | +|:-----------|:------|:----------|:--| +|eurostat |3.8.2 |4.0.0.9002 |* | +|askpass |1.1 |1.1 | | +|assertthat |0.2.1 |0.2.1 | | +|backports |1.4.1 |1.4.1 | | +|bibtex |0.5.1 |0.5.1 | | +|bit |4.0.5 |4.0.5 | | +|bit64 |4.0.5 |4.0.5 | | +|broom |1.0.5 |NA |* | +|cellranger |1.1.0 |1.1.0 | | +|classInt |0.4-9 |0.4-9 | | +|cli |3.6.1 |3.6.1 | | +|clipr |0.8.0 |0.8.0 | | +|countrycode |1.5.0 |1.5.0 | | +|cpp11 |0.4.4 |0.4.4 | | +|crayon |1.5.2 |1.5.2 | | +|curl |5.0.1 |5.0.1 | | +|dplyr |1.1.2 |1.1.2 | | +|e1071 |1.7-13 |1.7-13 | | +|ellipsis |0.3.2 |NA |* | +|fansi |1.0.4 |1.0.4 | | +|generics |0.1.3 |0.1.3 | | +|glue |1.6.2 |1.6.2 | | +|here |1.0.1 |1.0.1 | | +|hms |1.1.3 |1.1.3 | | +|httr |1.4.6 |1.4.6 | | +|ISOweek |0.6-2 |0.6-2 | | +|jsonlite |1.8.7 |1.8.7 | | +|lifecycle |1.0.3 |1.0.3 | | +|lubridate |1.9.2 |1.9.2 | | +|magrittr |2.0.3 |2.0.3 | | +|mime |0.12 |0.12 | | +|openssl |2.0.6 |2.0.6 | | +|pillar |1.9.0 |1.9.0 | | +|pkgconfig |2.0.3 |2.0.3 | | +|plyr |1.8.8 |1.8.8 | | +|prettyunits |1.1.1 |1.1.1 | | +|progress |1.2.2 |1.2.2 | | +|proxy |0.4-27 |0.4-27 | | +|purrr |1.0.1 |1.0.1 | | +|R6 |2.5.1 |2.5.1 | | +|rappdirs |0.3.3 |0.3.3 | | +|Rcpp |1.0.10 |1.0.10 | | +|readr |2.1.4 |2.1.4 | | +|readxl |1.4.2 |1.4.2 | | +|RefManageR |1.4.0 |1.4.0 | | +|regions |0.1.8 |0.1.8 | | +|rematch |1.0.1 |1.0.1 | | +|rlang |1.1.1 |1.1.1 | | +|rprojroot |2.0.3 |2.0.3 | | +|stringi |1.7.12 |1.7.12 | | +|stringr |1.5.0 |1.5.0 | | +|sys |3.4.2 |3.4.2 | | +|tibble |3.2.1 |3.2.1 | | +|tidyr |1.3.0 |1.3.0 | | +|tidyselect |1.2.0 |1.2.0 | | +|timechange |0.2.0 |0.2.0 | | +|tzdb |0.4.0 |0.4.0 | | +|utf8 |1.2.3 |1.2.3 | | +|vctrs |0.6.3 |0.6.3 | | +|vroom |1.6.3 |1.6.3 | | +|withr |2.5.0 |2.5.0 | | +|xml2 |1.3.4 |1.3.4 | | + +# Revdeps + diff --git a/revdep/check.R b/revdep/check.R deleted file mode 100755 index e262948c..00000000 --- a/revdep/check.R +++ /dev/null @@ -1,5 +0,0 @@ -library("devtools") - -revdep_check() -revdep_check_save_summary() -revdep_check_print_problems() diff --git a/revdep/cran.md b/revdep/cran.md new file mode 100644 index 00000000..7ac41de3 --- /dev/null +++ b/revdep/cran.md @@ -0,0 +1,7 @@ +## revdepcheck results + +We checked 9 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package. + + * We saw 0 new problems + * We failed to check 0 packages + diff --git a/revdep/data.sqlite b/revdep/data.sqlite new file mode 100644 index 00000000..fd5dd57d Binary files /dev/null and b/revdep/data.sqlite differ diff --git a/revdep/failures.md b/revdep/failures.md new file mode 100644 index 00000000..9a207363 --- /dev/null +++ b/revdep/failures.md @@ -0,0 +1 @@ +*Wow, no problems at all. :)* \ No newline at end of file diff --git a/revdep/problems.md b/revdep/problems.md new file mode 100644 index 00000000..9a207363 --- /dev/null +++ b/revdep/problems.md @@ -0,0 +1 @@ +*Wow, no problems at all. :)* \ No newline at end of file diff --git a/tests/testthat/_snaps/03_get_eurostat_geospatial.md b/tests/testthat/_snaps/03_get_eurostat_geospatial.md new file mode 100644 index 00000000..93616591 --- /dev/null +++ b/tests/testthat/_snaps/03_get_eurostat_geospatial.md @@ -0,0 +1,22 @@ +# get_eurostat_geospatial cache_dir + + Code + a <- get_eurostat_geospatial(nuts_id = "LU", nuts_level = 0) + Message + Extracting data using giscoR package, please report issues on https://github.com/rOpenGov/giscoR/issues + +--- + + Code + a <- get_eurostat_geospatial(nuts_id = "LU", nuts_level = 0, cache_dir = another_temp) + Message + Extracting data using giscoR package, please report issues on https://github.com/rOpenGov/giscoR/issues + +--- + + Code + a <- get_eurostat_geospatial(nuts_id = "LU", nuts_level = 0) + Message + Extracting data using giscoR package, please report issues on https://github.com/rOpenGov/giscoR/issues + Cache management as per giscoR. see 'giscoR::gisco_get_nuts()' + diff --git a/tests/testthat/test-all.R b/tests/testthat/test-all.R deleted file mode 100755 index 390c25e2..00000000 --- a/tests/testthat/test-all.R +++ /dev/null @@ -1,185 +0,0 @@ -context("Get") - -test_that("get_eurostat includes time and value",{ - skip_on_cran() - expect_true(all(c("time", "values") %in% - names(get_eurostat("road_eqr_trams")))) -}) - -test_that("get_eurostat works with multi-frequency",{ - skip_on_cran() - expect_error(get_eurostat("avia_gonc", cache = FALSE)) - expect_match(as.character(unique(get_eurostat("avia_gonc", select_time = "Y" , - cache = FALSE)$time)), "-01-01") -}) - -test_that("get_eurostat return right classes",{ - skip_on_cran() - expect_true(all(c("factor", "numeric") %in% - sapply(get_eurostat("road_eqr_trams"), class))) - expect_true(all(c("character", "numeric") %in% - sapply(get_eurostat("road_eqr_trams", stringsAsFactors = FALSE), - class))) -}) - -test_that("get_eurostat handles daily data", { - skip_on_cran() - dat1 <- get_eurostat("ert_bil_eur_d", - filters = list(currency = "ARS", statinfo ="AVG", - time = c("2017M03D09", "2017M03D10")), - time_format = "date", cache = FALSE) - expect_equal(as.numeric(difftime(dat1$time[2], dat1$time[1], units = "days")), 1) -}) - - -test_that("get_eurostat get non-normal variable order",{ - skip_on_cran() - expect_gt(nrow(get_eurostat("cens_01rdhh")), 0) -}) - -context("cache") - -test_that("Cache give error if cache dir does not exist", { - skip_on_cran() - expect_error( - get_eurostat("nama_10_lp_ulc", cache_dir = file.path(tempdir(), "r_cache"))) -}) - -test_that("Cache works", { - skip_on_cran() - expect_is({ - t_dir <- file.path(tempdir(), "reurostat") - dir.create(t_dir) - k <- get_eurostat("nama_10_lp_ulc", cache_dir = t_dir) - }, - "data.frame") -}) - - -context("Search") - -test_that("search_eurostat finds",{ - skip_on_cran() - expect_equal(search_eurostat( - "Dwellings by type of housing, building and NUTS 3", type = "dataset")$code[1], - "cens_01rdhh" - ) -}) - - -context("Label") - -test_that("Variable names are labeled",{ - skip_on_cran() - expect_equal(label_eurostat_vars("geo"), "Geopolitical entity (reporting)") - expect_equal(label_eurostat_vars("housing", lang = "fr"), "Habitation") - - expect_true(any(grepl("_code", - names(label_eurostat( - get_eurostat("road_eqr_trams"), code = "geo"))))) -}) - -test_that("Label ordering is ordered", { - skip_on_cran() - expect_equal(c("European Union (current composition)", - "Finland", "United States"), - levels(label_eurostat(factor(c("FI", "US", "EU28")), - dic = "geo", eu_order = TRUE))) -}) - - -test_that("Countrycodes are labelled for factors", { - expect_equal(levels(label_eurostat(factor(c("FI", "DE", "EU28"), c("FI", "DE", "EU28")), dic = "geo", - countrycode = "country.name")), - c("Finland", "Germany", "EU28")) -}) - -test_that("Countrycodes return NA for countrycode_nomatch = NA", { - expect_equal(suppressWarnings(label_eurostat(c("FI", "DE", "EU28"), dic = "geo", - countrycode = "country.name", countrycode_nomatch = NA)), - c("Finland", "Germany", NA)) -}) - -test_that("Countrycodes use eurostat for missing", { - expect_equal(suppressWarnings(label_eurostat(c("FI", "DE", "EU28"), dic = "geo", - countrycode = "country.name", countrycode_nomatch = "eurostat")), - c("Finland", "Germany", "European Union (current composition)")) -}) - -test_that("custom_dic works", { - expect_equal(label_eurostat(c("FI", "DE"), dic = "geo", custom_dic = c(DE = "Germany")), - c("Finland", "Germany")) -}) - -context("Misc") - -test_that("Dic downloading works", { - skip_on_cran() - expect_warning(get_eurostat_dic("na_item"), NA) -}) - -test_that("Factors are retained in data.frame", { - skip_on_cran() - y <- label_eurostat(data.frame(unit = factor("EUR"))) - expect_is(y$unit, "factor") -}) - -test_that("Duplicated gives an error", { - skip_on_cran() - expect_error(label_eurostat(x = factor(c("P5G", "P5")), dic = "na_item")) -}) - -test_that("Duplicated with fix_duplicated does not give an error", { - skip_on_cran() - expect_match(as.character( - label_eurostat(x = factor(c("P5G", "P5")), dic = "na_item", - fix_duplicated = TRUE)), "P5", all = TRUE) -}) - -context("Flags") - -#flag_dat <- get_eurostat("t2020_rk310", type = "label", keepFlags=T, cache = FALSE) -#flag_dat <- get_eurostat("tsdtr210", type = "label", keepFlags=T, cache = FALSE) -flag_dat <- get_eurostat("road_pa_buscoa", type = "label", keepFlags=T, cache = FALSE) - - -test_that("get_eurostat includes flags",{ - skip_on_cran() - expect_true(all(c("flags") %in% - names(get_eurostat("road_eqr_trams", keepFlags = TRUE)))) -}) - -test_that("keepFlags + label as in #61",{ - skip_on_cran() - expect_true(all(c("flags") %in% - names(flag_dat))) -}) - -test_that("flag content",{ - skip_on_cran() - expect_true(all(c("b", "e") %in% - unique(flag_dat$flags))) -}) - -context("json") - -test_that("Get json data",{ - skip_on_cran() - expect_named(get_eurostat_json("nama_10_gdp", filters = list(geo = "FI", - na_item = "B1GQ", - unit = "CLV_I10")), - c("geo", "unit", "na_item", "time", "values"), - ignore.order = TRUE) -}) - -test_that("Handle numbers in filter name",{ - skip_on_cran() - expect_named(get_eurostat(id = "sts_inpr_a", filters = list(geo = "AT", - nace_r2 = "B", - s_adj = "CA", - indic_bt = "PROD", - unit = "I10")), - c("geo", "nace_r2", "s_adj", "indic_bt", "unit", "time", "values"), - ignore.order = TRUE) -}) - diff --git a/tests/testthat/test_00_cache.R b/tests/testthat/test_00_cache.R new file mode 100755 index 00000000..c5bbc23c --- /dev/null +++ b/tests/testthat/test_00_cache.R @@ -0,0 +1,54 @@ +test_that("Save user cache for test", { + skip_on_cran() + skip_if_offline() + + # Get current cache dir + Sys.setenv("TEST_EUROSTAT_CURRENT_CACHE" = eur_helper_detect_cache_dir()) + + cat("User cache dir is ", Sys.getenv("TEST_EUROSTAT_CURRENT_CACHE"), "\n") + + testdir <- file.path(tempdir(), "eurostat", "testthat") + # Set a temp cache dir + expect_message(set_eurostat_cache_dir(testdir)) + + cat("Testing cache dir is ", Sys.getenv("EUROSTAT_CACHE_DIR"), "\n") +}) + +test_that("Cache is ok if cache dir does not exist", { + skip_on_cran() + skip_if_offline() + k <- get_eurostat("nama_10_lp_ulc") + + expect_true(inherits(k, "data.frame")) +}) + +test_that("Cache works", { + skip_on_cran() + skip_if_offline() + + t_dir <- file.path(tempdir(), "reurostat") + k <- get_eurostat("nama_10_lp_ulc", cache_dir = t_dir) + expect_true(inherits(k, "data.frame")) +}) + +test_that("Dataset is filtered from cached bulk file", { + skip_on_cran() + skip_if_offline() + + t_dir <- file.path(tempdir(), "reurostat") + k2 <- get_eurostat("nama_10_lp_ulc", + cache_dir = t_dir, + filters = list()) + expect_message(get_eurostat("nama_10_lp_ulc", + cache_dir = t_dir, + filters = list(geo = "AT")), "and filtering it") + +}) + +test_that("Set cache", { + old <- eur_helper_detect_cache_dir() + new <- file.path(tempdir(), "eurostat", "testthat", "new") + expect_message(set_eurostat_cache_dir(new)) + + set_eurostat_cache_dir(old) +}) diff --git a/tests/testthat/test_01_bibliography.R b/tests/testthat/test_01_bibliography.R new file mode 100755 index 00000000..32374ba3 --- /dev/null +++ b/tests/testthat/test_01_bibliography.R @@ -0,0 +1,65 @@ +test_that("Bibentry gives correct results", { + skip_on_cran() + skip_if_offline() + expect_equal( + class(get_bibentry( + code = c("sts_inpr_a", "nama_10_gdp"), + keywords = list( + c("production", "industry"), + c("GDP") + ), + format = "Biblatex" + )), + "Bibtex" + ) + + expect_equal( + class( + suppressWarnings( + get_bibentry( + code = c("sts_inpr_a"), + lang = "fr", + format = "Bibtex" + ))), + "Bibtex" + ) + + expect_error( + get_bibentry(code = 123456) + ) + + expect_error( + get_bibentry( + code = c("sts_inpr_a"), + lang = "fr", + keywords = "production" + ) + ) + + # None of the codes not found + expect_warning( + get_bibentry( + code = "sts_inpr_b" + ) + ) + + # Some of the codes not found + expect_warning( + get_bibentry( + code = c("sts_inpr_b", "sts_inpr_a") + ) + ) + + expect_warning( + get_bibentry( + code = c("sts_inpr_a", "nama_10_gdp"), + keywords = list( + c("production", "industry"), + c("GDP") + ), + format = "character" + ) + ) + + +}) diff --git a/tests/testthat/test_02_flags.R b/tests/testthat/test_02_flags.R new file mode 100755 index 00000000..4331f7c6 --- /dev/null +++ b/tests/testthat/test_02_flags.R @@ -0,0 +1,44 @@ + +# flag_dat <- get_eurostat("t2020_rk310", type = "label", keepFlags=T, cache = FALSE) +# flag_dat <- get_eurostat("tsdtr210", type = "label", keepFlags=T, cache = FALSE) + +test_that("get_eurostat includes flags", { + skip_on_cran() + skip_if_offline() + expect_true(all(c("flags") %in% + names(get_eurostat("road_eqr_trams", keepFlags = TRUE)))) +}) + +test_that("keepFlags + label as in #61", { + skip_on_cran() + skip_if_offline() + flag_dat <- get_eurostat("road_pa_buscoa", type = "label", keepFlags = TRUE, cache = FALSE) + + expect_true(all(c("flags") %in% + names(flag_dat))) +}) + +test_that("flag content", { + skip_on_cran() + skip_if_offline() + flag_dat <- get_eurostat("road_pa_buscoa", type = "label", keepFlags = TRUE, cache = FALSE) + + expect_true(all(c("b", "e") %in% + unique(flag_dat$flags))) +}) + +test_that("flag content2", { + skip_on_cran() + skip_if_offline() + flag_dat <- get_eurostat("road_pa_buscoa", + type = "label", + keepFlags = TRUE, + cache = FALSE, + legacy_bulk_download = FALSE) + + expect_true(all(c("b", "e") %in% + unique(flag_dat$flags))) + + # sleep for a while to let the API rest + Sys.sleep(5) +}) diff --git a/tests/testthat/test_03_get_eurostat_geospatial.R b/tests/testthat/test_03_get_eurostat_geospatial.R new file mode 100755 index 00000000..7ea873d3 --- /dev/null +++ b/tests/testthat/test_03_get_eurostat_geospatial.R @@ -0,0 +1,640 @@ +test_that("get_eurostat_geospatial errors", { + skip_if_not_installed(pkg = "giscoR") + skip_if_not_installed(pkg = "sf") + skip_on_cran() + skip_if_offline() + skip_if(!giscoR::gisco_check_access(), "No access to GISCO") + + # Testing argument 'output_class' + expect_error(get_eurostat_geospatial(output_class = 0)) + expect_error(get_eurostat_geospatial(output_class = "foo")) + expect_error(get_eurostat_geospatial(output_class = "sf", "df")) + + # Testing argument 'resolution' + expect_error(get_eurostat_geospatial(resolution = 12345)) + expect_error(get_eurostat_geospatial(resolution = 1:2)) + + # Testing argument nuts_level + expect_error(get_eurostat_geospatial(nuts_level = 12345)) + expect_error(get_eurostat_geospatial(nuts_level = 1:2)) + + # Testing argument year + expect_error(get_eurostat_geospatial(year = 1900)) + expect_error(get_eurostat_geospatial(year = c(2003, 2006))) + + # Testing argment cache + expect_error(get_eurostat_geospatial(cache = as.logical(NA), year = 2021)) + expect_error(get_eurostat_geospatial(cache = c(TRUE, FALSE), year = 2021)) + + # Testing argument CRS + expect_error(get_eurostat_geospatial(crs = "north polar stereographic")) + expect_error(get_eurostat_geospatial(crs = c(4326, 3035))) + + # Invalid combinations + expect_error(get_eurostat_geospatial(resolution = 60, year = 2003)) +}) + +test_that("get_eurostat_geospatial messages", { + skip_if_not_installed(pkg = "giscoR") + skip_if_not_installed(pkg = "sf") + skip_on_cran() + skip_if_offline() + skip_if(!giscoR::gisco_check_access(), "No access to GISCO") + + + # Deprecations + expect_message( + get_eurostat_geospatial(make_valid = TRUE), + "'make_valid' argument has been deprecated" + ) + + expect_message( + spdf <- get_eurostat_geospatial(output_class = "spdf"), + "'spdf' output deprecated. Switching to sf output" + ) + expect_s3_class(spdf, "sf") + + # Info about source of data + expect_message(get_eurostat_geospatial(), "from eurostat") + expect_message(get_eurostat_geospatial(nuts_id = "BE"), "giscoR package") +}) + + +test_that("get_eurostat_geospatial nuts levels", { + skip_if_not_installed(pkg = "sf") + # From internal data with default args + expect_message(all <- get_eurostat_geospatial(nuts_level = "all"), "eurostat") + expect_message(n0 <- get_eurostat_geospatial(nuts_level = "0"), "eurostat") + expect_message(n1 <- get_eurostat_geospatial(nuts_level = "1"), "eurostat") + expect_message(n2 <- get_eurostat_geospatial(nuts_level = "2"), "eurostat") + expect_message(n3 <- get_eurostat_geospatial(nuts_level = "3"), "eurostat") + + expect_gt(nrow(all), nrow(n3)) + expect_gt(nrow(n3), nrow(n2)) + expect_gt(nrow(n2), nrow(n1)) + expect_gt(nrow(n1), nrow(n0)) + + expect_s3_class(all, "sf") + expect_s3_class(n3, "sf") + expect_s3_class(n2, "sf") + expect_s3_class(n1, "sf") + + skip_if_not_installed(pkg = "giscoR") + skip_on_cran() + skip_if_offline() + skip_if(!giscoR::gisco_check_access(), "No access to GISCO") + + + # From internal data with default args + expect_message(gall <- get_eurostat_geospatial( + nuts_level = "all", + crs = 3035 + ), "giscoR") + expect_message(gn0 <- get_eurostat_geospatial( + nuts_level = "0", + crs = 3035 + ), "giscoR") + expect_message(gn1 <- get_eurostat_geospatial( + nuts_level = "1", + crs = 3035 + ), "giscoR") + expect_message(gn2 <- get_eurostat_geospatial( + nuts_level = "2", + crs = 3035 + ), "giscoR") + expect_message(gn3 <- get_eurostat_geospatial( + nuts_level = "3", + crs = 3035 + ), "giscoR") + + expect_gt(nrow(gall), nrow(gn3)) + expect_gt(nrow(gn3), nrow(gn2)) + expect_gt(nrow(gn2), nrow(gn1)) + expect_gt(nrow(gn1), nrow(gn0)) + expect_true("geo" %in% names(gall)) + + expect_s3_class(gall, "sf") + expect_s3_class(gn3, "sf") + expect_s3_class(gn2, "sf") + expect_s3_class(gn1, "sf") +}) + +test_that("get_eurostat_geospatial df", { + skip_if_not_installed(pkg = "sf") + # From internal data with default args + expect_message( + all <- get_eurostat_geospatial( + nuts_level = "all", + output_class = "df" + ), + "eurostat" + ) + expect_message(n0 <- get_eurostat_geospatial( + nuts_level = "0", + output_class = "df" + ), "eurostat") + expect_message(n1 <- get_eurostat_geospatial( + nuts_level = "1", + output_class = "df" + ), "eurostat") + expect_message(n2 <- get_eurostat_geospatial( + nuts_level = "2", + output_class = "df" + ), "eurostat") + expect_message(n3 <- get_eurostat_geospatial( + nuts_level = "3", + output_class = "df" + ), "eurostat") + + expect_gt(nrow(all), nrow(n3)) + expect_gt(nrow(n3), nrow(n2)) + expect_gt(nrow(n2), nrow(n1)) + expect_gt(nrow(n1), nrow(n0)) + + expect_s3_class(all, "data.frame") + expect_s3_class(n3, "data.frame") + expect_s3_class(n2, "data.frame") + expect_s3_class(n1, "data.frame") + + skip_if_not_installed(pkg = "giscoR") + skip_on_cran() + skip_if_offline() + skip_if(!giscoR::gisco_check_access(), "No access to GISCO") + + + # From giscoR data + expect_message(gall <- get_eurostat_geospatial( + nuts_level = "all", + output_class = "df", + verbose = TRUE + ), "giscoR") + expect_message(gn0 <- get_eurostat_geospatial( + nuts_level = "0", + crs = 3035, + verbose = TRUE + ), "giscoR") + expect_message(gn1 <- get_eurostat_geospatial( + nuts_level = "1", + crs = 3035, + verbose = TRUE + ), "giscoR") + expect_message(gn2 <- get_eurostat_geospatial( + nuts_level = "2", + crs = 3035, + verbose = TRUE + ), "giscoR") + expect_message(gn3 <- get_eurostat_geospatial( + nuts_level = "3", + crs = 3035, + verbose = TRUE + ), "giscoR") + + expect_gt(nrow(gall), nrow(gn3)) + expect_gt(nrow(gn3), nrow(gn2)) + expect_gt(nrow(gn2), nrow(gn1)) + expect_gt(nrow(gn1), nrow(gn0)) + expect_true("geo" %in% names(gall)) + + expect_s3_class(gall, "data.frame") + expect_s3_class(gn3, "data.frame") + expect_s3_class(gn2, "data.frame") + expect_s3_class(gn1, "data.frame") +}) + + +test_that("get_eurostat_geospatial cache_dir", { + skip_if_not_installed(pkg = "sf") + skip_if_not_installed(pkg = "giscoR") + skip_on_cran() + skip_if_offline() + skip_if(!giscoR::gisco_check_access(), "No access to GISCO") + + # Get initial cache dir + init_cache <- eur_helper_detect_cache_dir() + + # Now mocking as if the user set a specific cache dir + test_cache_dir <- file.path(tempdir(), "eurostat_set_by_user") + suppressMessages(set_eurostat_cache_dir(test_cache_dir)) + + # Check change + new_cache <- eur_helper_detect_cache_dir() + expect_false(identical(init_cache, new_cache)) + + # Check now the messages with snapshot testing + # This would show nothing regarding cache + expect_snapshot(a <- get_eurostat_geospatial( + nuts_id = "LU", + nuts_level = 0 + )) + + # If set by the user no messages on caching + another_temp <- tempdir() + expect_snapshot(a <- get_eurostat_geospatial( + nuts_id = "LU", + nuts_level = 0, + cache_dir = another_temp + )) + + # But if using the default unset cache dir from eurostat, expect a message + # stating that giscoR is managing it + + # Now mocking as if the user set a specific cache dir + default_temp <- file.path(tempdir(), "eurostat") + suppressMessages(set_eurostat_cache_dir(default_temp)) + + # Check change + new_cache <- eur_helper_detect_cache_dir() + expect_false(identical(init_cache, new_cache)) + expect_true(identical(default_temp, new_cache)) + + expect_snapshot(a <- get_eurostat_geospatial( + nuts_id = "LU", + nuts_level = 0 + )) + + + # Finally, restore cache dir set by the user (if any) + suppressMessages(set_eurostat_cache_dir(init_cache)) + expect_identical(init_cache, eur_helper_detect_cache_dir()) +}) + + +test_that("giscoR returns NULL", { + skip_if_not_installed(pkg = "sf") + skip_if_not_installed(pkg = "giscoR") + skip_on_cran() + skip_if_offline() + skip_if(!giscoR::gisco_check_access(), "No access to GISCO") + skip_if(packageVersion("giscoR") < "0.3.5", "Use latest giscoR release") + + options(giscoR_test_offline = TRUE) + expect_message( + n <- get_eurostat_geospatial( + country = "AT", nuts_level = "0", + update_cache = TRUE + ), + "not reachable" + ) + expect_null(n) + options(giscoR_test_offline = FALSE) +}) + + +test_that("Check column names", { + skip_if_not_installed(pkg = "sf") + + # See https://github.com/rOpenGov/eurostat/issues/240 + col_order <- c( + "id", "LEVL_CODE", "NUTS_ID", "CNTR_CODE", "NAME_LATN", + "NUTS_NAME", "MOUNT_TYPE", "URBN_TYPE", "COAST_TYPE", + "FID", "geo", "geometry" + ) + + + cached <- get_eurostat_geospatial() + expect_s3_class(cached, "sf") + expect_identical(names(cached), col_order) + + # df + cached_df <- get_eurostat_geospatial(output_class = "df") + expect_s3_class(cached_df, "data.frame") + expect_identical(names(cached_df), col_order[-length(col_order)]) +}) + + +test_that("Check column names POLYGONS from GISCO", { + skip_if_not_installed(pkg = "sf") + skip_if_not_installed(pkg = "giscoR") + skip_on_cran() + skip_if_offline() + skip_if(!giscoR::gisco_check_access(), "No access to GISCO") + skip_if(packageVersion("giscoR") < "0.3.5", "Use latest giscoR release") + + col_order <- c( + "id", "LEVL_CODE", "NUTS_ID", "CNTR_CODE", "NAME_LATN", + "NUTS_NAME", "MOUNT_TYPE", "URBN_TYPE", "COAST_TYPE", + "FID", "geo", "geometry" + ) + + # Polygons 2003 + poly <- get_eurostat_geospatial(nuts_level = 0, resolution = 20, year = 2003) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 20, year = 2003 + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + # Polygons 2006 + poly <- get_eurostat_geospatial(nuts_level = 0, resolution = 60, year = 2006) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2006 + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + + # Polygons 2010 + poly <- get_eurostat_geospatial(nuts_level = 0, resolution = 60, year = 2010) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2010 + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + # Polygons 2013 + poly <- get_eurostat_geospatial(nuts_level = 0, resolution = 60, year = 2013) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2013 + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + # Polygons 2016 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2016, + update_cache = TRUE + ) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2016, + update_cache = TRUE + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + # Polygons 2021 + poly <- get_eurostat_geospatial(nuts_level = 0, resolution = 60, year = 2021) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2021 + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) +}) + +test_that("Check column names LABELS from GISCO", { + skip_if_not_installed(pkg = "sf") + skip_if_not_installed(pkg = "giscoR") + skip_on_cran() + skip_if_offline() + skip_if(!giscoR::gisco_check_access(), "No access to GISCO") + skip_if(packageVersion("giscoR") < "0.3.5", "Use latest giscoR release") + + col_order <- c( + "id", "LEVL_CODE", "NUTS_ID", "CNTR_CODE", "NAME_LATN", + "NUTS_NAME", "MOUNT_TYPE", "URBN_TYPE", "COAST_TYPE", + "FID", "geo", "geometry" + ) + + # Labels 2003 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 20, year = 2003, + spatialtype = "LB" + ) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 20, year = 2003, + spatialtype = "LB" + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + # Labels 2006 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2006, + spatialtype = "LB" + ) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2006, + spatialtype = "LB" + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + + # Labels 2010 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2010, + spatialtype = "LB" + ) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2010, + spatialtype = "LB" + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + # Labels 2013 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2013, + spatialtype = "LB" + ) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2013, + spatialtype = "LB" + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + # Labels 2016 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2016, + update_cache = TRUE, spatialtype = "LB" + ) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2016, + update_cache = TRUE, + spatialtype = "LB" + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) + + # Labels 2021 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2021, + spatialtype = "LB" + ) + expect_s3_class(poly, "sf") + expect_identical(names(poly), col_order) + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2021, + spatialtype = "LB" + ) + + expect_s3_class(poly_df, "data.frame") + expect_identical(names(poly_df), col_order[-length(col_order)]) +}) + + +test_that("Check column names BORDERS from GISCO", { + skip_if_not_installed(pkg = "sf") + skip_if_not_installed(pkg = "giscoR") + skip_on_cran() + skip_if_offline() + skip_if(!giscoR::gisco_check_access(), "No access to GISCO") + skip_if(packageVersion("giscoR") < "0.3.5", "Use latest giscoR release") + + # BORDERS 2003 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 20, year = 2003, + spatialtype = "BN" + ) + expect_s3_class(poly, "sf") + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 20, year = 2003, + spatialtype = "BN" + ) + + expect_s3_class(poly_df, "data.frame") + + # BORDERS 2006 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2006, + spatialtype = "BN" + ) + expect_s3_class(poly, "sf") + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2006, + spatialtype = "BN" + ) + + expect_s3_class(poly_df, "data.frame") + + + # BORDERS 2010 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2010, + spatialtype = "BN" + ) + expect_s3_class(poly, "sf") + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2010, + spatialtype = "BN" + ) + + expect_s3_class(poly_df, "data.frame") + + # BORDERS 2013 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2013, + spatialtype = "BN" + ) + expect_s3_class(poly, "sf") + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2013, + spatialtype = "BN" + ) + + expect_s3_class(poly_df, "data.frame") + + # BORDERS 2016 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2016, + update_cache = TRUE, spatialtype = "BN" + ) + expect_s3_class(poly, "sf") + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2016, + update_cache = TRUE, + spatialtype = "BN" + ) + + expect_s3_class(poly_df, "data.frame") + + # BORDERS 2021 + poly <- get_eurostat_geospatial( + nuts_level = 0, resolution = 60, year = 2021, + spatialtype = "BN" + ) + expect_s3_class(poly, "sf") + + # df + poly_df <- get_eurostat_geospatial( + output_class = "df", nuts_level = 0, + resolution = 60, year = 2021, + spatialtype = "BN" + ) + + expect_s3_class(poly_df, "data.frame") +}) diff --git a/tests/testthat/test_04_get.R b/tests/testthat/test_04_get.R new file mode 100755 index 00000000..dc525b7d --- /dev/null +++ b/tests/testthat/test_04_get.R @@ -0,0 +1,129 @@ +test_that("get_eurostat (dissemination API) includes TIME_PERIOD and value", { + skip_on_cran() + skip_if_offline() + expect_true(all(c("TIME_PERIOD", "values") %in% + names(get_eurostat(id = "road_eqr_trams", + stringsAsFactors = TRUE, + select_time = "A")))) +}) + +test_that("get_eurostat (dissemination API) includes TIME_PERIOD and value", { + skip_on_cran() + skip_if_offline() + expect_true(all(c("TIME_PERIOD", "values") %in% + names(get_eurostat(id = "road_eqr_trams", + stringsAsFactors = TRUE, + select_time = "Y")))) + # sleep for a while to let the API rest + Sys.sleep(5) +}) + +test_that("get_eurostat (dissemination API) produces a message with multiple select_time", { + skip_on_cran() + skip_if_offline() + expect_message(get_eurostat(id = "avia_gonc", + select_time = c("A", "M", "Q"))) +}) + +test_that("get_eurostat (dissemination API) produces an error with imaginary select_time parameters", { + skip_on_cran() + skip_if_offline() + expect_error(get_eurostat(id = "avia_gonc", + select_time = c("X", "Y", "Z"))) + # sleep for a while to let the API rest + Sys.sleep(5) +}) + +test_that("get_eurostat (dissemination API) works correctly with multi-frequency", { + skip_on_cran() + skip_if_offline() + expect_message(get_eurostat("avia_gonc", + cache = FALSE, + time_format = "date_last") + ) + expect_match(as.character(unique(get_eurostat("avia_gonc", + select_time = NULL, + cache = FALSE)$TIME_PERIOD)), + "-01-01") +}) + +test_that("get_eurostat return right classes", { + skip_on_cran() + skip_if_offline() + expect_true(all(c("character", "numeric", "Date") %in% + sapply(get_eurostat("road_eqr_trams"), class))) + expect_true(all(c("character", "numeric", "Date") %in% + sapply( + get_eurostat("road_eqr_trams", keepFlags = TRUE), + class + ))) +}) + +test_that("bi-annual data contains freq S", { + skip_on_cran() + skip_if_offline() + expect_true("S" %in% get_eurostat("earn_mw_cur")$freq) + # sleep for a while to let the API rest + Sys.sleep(5) +}) + + +test_that("eurostat2num2 (dissemination API) works correctly", { + skip_on_cran() + skip_if_offline() + expect_true( + is.numeric( + get_eurostat(id = "earn_mw_cur", + time_format = "num", + use.data.table = TRUE)$TIME_PERIOD + ) + ) +}) + +test_that("weekly dataset download (dissemination API) works correctly", { + skip_on_cran() + skip_if_offline() + expect_match( + get_eurostat(id = "lfsi_abs_w", + select_time = c("W"), + time_format = "date")$freq[1], "W") +}) + +test_that("get_eurostat get non-normal variable order", { + skip_on_cran() + skip_if_offline() + expect_gt(nrow(get_eurostat("cens_01rdhh")), 0) +}) + +test_that("get_eurostat and eurotime2date work with daily data", { + skip_on_cran() + skip_if_offline() + expect_match(get_eurostat("irt_h_eurcoe_d", + filters = list(lastTimePeriod = 5))$freq[1], "D") +}) + +test_that("get_eurostat and eurotime2num work with daily data", { + skip_on_cran() + skip_if_offline() + # Downloading daily data with time_format = "num" produces a warning + # suppressWarnings is here to suppress that + expect_match(suppressWarnings(get_eurostat("irt_h_eurcoe_d", + filters = list(lastTimePeriod = 5), + time_format = "num"))$freq[1], "D") +}) + +test_that("get_eurostat and eurotime2num work with daily data", { + skip_on_cran() + skip_if_offline() + expect_match(get_eurostat("ei_bsci_m_r2", + filters = list(lastTimePeriod = 5), + time_format = "num")$freq[1], "M") +}) + +test_that("get_eurostat and eurotime2num work with daily data", { + skip_on_cran() + skip_if_offline() + expect_match(get_eurostat("med_ag32", + filters = list(lastTimePeriod = 5), + time_format = "num")$freq[1], "A") +}) diff --git a/tests/testthat/test_05_json.R b/tests/testthat/test_05_json.R new file mode 100755 index 00000000..3e3d75d8 --- /dev/null +++ b/tests/testthat/test_05_json.R @@ -0,0 +1,43 @@ +test_that("Get json data", { + skip_on_cran() + skip_if_offline() + expect_named( + get_eurostat_json("nama_10_gdp", + filters = list( + geo = "FI", + na_item = "B1GQ", + unit = "CLV_I10")), + c("geo", "unit", "na_item", "time", "values", "freq"), + ignore.order = TRUE + ) + + # Unsupported language code used. Using the default language: "en" + expect_message( + get_eurostat_json("nama_10_gdp", + filters = list( + geo = "FI", + na_item = "B1GQ", + unit = "CLV_I10"), + lang = "FI", + type = "label") + ) + + # Using the default language: "en" + expect_message( + get_eurostat_json("nama_10_gdp", + filters = list( + geo = "FI", + na_item = "B1GQ", + unit = "CLV_I10"), + lang = NULL, + type = "both") + ) + + # Error message + expect_error( + get_eurostat_json("invalid_id") + ) + + # sleep for a while to let the API rest + Sys.sleep(5) +}) diff --git a/tests/testthat/test_06_label.R b/tests/testthat/test_06_label.R new file mode 100755 index 00000000..dead5bcd --- /dev/null +++ b/tests/testthat/test_06_label.R @@ -0,0 +1,87 @@ +test_that("Variable names are labeled", { + skip_on_cran() + skip_if_offline() + expect_equal(label_eurostat_vars(id = "nama_10_lp_ulc", x = "geo", lang = "en"), "Geopolitical entity (reporting)") + expect_equal(label_eurostat_tables("nama_10_lp_ulc"), "Labour productivity and unit labour costs") + + expect_true(any(grepl( + "_code", + names(label_eurostat( + get_eurostat("road_eqr_trams"), + code = "geo" + )) + ))) +}) + +test_that("Label ordering is ordered", { + skip_on_cran() + skip_if_offline() + expect_equal( + c( + "European Union - 28 countries (2013-2020)", + "Finland", "United States" + ), + levels(label_eurostat(factor(c("FI", "US", "EU28")), + dic = "geo", eu_order = TRUE + )) + ) +}) + + +test_that("Countrycodes are labelled for factors", { + skip_on_cran() + skip_if_offline() + expect_equal( + levels(label_eurostat(factor(c("FI", "DE", "EU28"), c("FI", "DE", "EU28")), + dic = "geo", + countrycode = "country.name" + )), + c("Finland", "Germany", "EU28") + ) + + # sleep for a while to let the API rest + Sys.sleep(5) +}) + +test_that("Countrycodes return NA for countrycode_nomatch = NA", { + skip_on_cran() + skip_if_offline() + expect_equal( + suppressWarnings(label_eurostat(c("FI", "DE", "EU28"), + dic = "geo", + countrycode = "country.name", countrycode_nomatch = NA + )), + c("Finland", "Germany", NA) + ) +}) + +test_that("Countrycodes use eurostat for missing", { + skip_on_cran() + skip_if_offline() + expect_equal( + suppressWarnings(label_eurostat(c("FI", "DE", "EU28"), + dic = "geo", + countrycode = "country.name", countrycode_nomatch = "eurostat" + )), + c("Finland", "Germany", "European Union - 28 countries (2013-2020)") + ) +}) + +test_that("custom_dic works", { + skip_on_cran() + skip_if_offline() + expect_equal( + label_eurostat(c("FI", "DE"), dic = "geo", custom_dic = c(DE = "Germany")), + c("Finland", "Germany") + ) +}) + +test_that("wrong code (for this dataset) produces an error", { + skip_on_cran() + skip_if_offline() + expect_error( + label_eurostat( + get_eurostat("road_eqr_trams"), code = "coef" + ) + ) +}) \ No newline at end of file diff --git a/tests/testthat/test_07_misc.R b/tests/testthat/test_07_misc.R new file mode 100755 index 00000000..9e638371 --- /dev/null +++ b/tests/testthat/test_07_misc.R @@ -0,0 +1,61 @@ +test_that("Dic downloading works", { + skip_on_cran() + skip_if_offline() + expect_warning(get_eurostat_dic("na_item"), NA) +}) + +test_that("Factors are retained in data.frame", { + skip_on_cran() + skip_if_offline() + y <- label_eurostat(data.frame(unit = factor("EUR"))) + expect_s3_class(y$unit, "factor") +}) + +test_that("Duplicated gives an error", { + skip_on_cran() + skip_if_offline() + expect_error(label_eurostat(x = factor(c("P5G", "P5")), dic = "na_item")) +}) + +test_that("Duplicated with fix_duplicated does not give an error", { + skip_on_cran() + skip_if_offline() + expect_match(as.character( + label_eurostat( + x = factor(c("P5G", "P5")), dic = "na_item", + fix_duplicated = TRUE + ) + ), "P5", all = TRUE) +}) + +# W99 values are now reported in separate Excel files that are published +# in demo_r_mweek3 Reference metadata page so this test no longer +# applies, as of 2025-02-17 +# test_that("Get eurotime2date error message related to W99 values", { +# skip_on_cran() +# skip_if_offline() +# expect_warning( +# get_eurostat( +# "demo_r_mweek3", +# filters = list( +# sex = "F", +# age = "TOTAL", +# geo = "BG" +# ) +# ) +# ) +# }) + +test_that("cut_to_classes works", { + skip_on_cran() + skip_if_offline() + dataset <- get_eurostat( + "nama_10_lp_ulc", + filters = list( + geo = c("AT", "DK", "BG"), + unit = "EUR", + na_item = "D1_SAL_HW")) + dataset <- na.omit(dataset) + + expect_visible(cut_to_classes(dataset$values)) +}) \ No newline at end of file diff --git a/tests/testthat/test_08_regional.R b/tests/testthat/test_08_regional.R new file mode 100755 index 00000000..abb6e510 --- /dev/null +++ b/tests/testthat/test_08_regional.R @@ -0,0 +1,78 @@ +test_regional_codes <- data.frame( + geo = c("FRB", "FRE", "UKN02", "IE022", "FR243", "FRB03"), + time = c(rep(as.Date("2014-01-01"), 5), as.Date("2015-01-01")), + values = c(1:6), + control = c( + "Changed from NUTS2 to NUTS1", + "New region NUTS2016 only", + "Discontinued region NUTS2013", + "Boundary shift NUTS2013", + "Recoded in NUTS2013", + "Recoded in NUTS2016" + ) +) + + + +dat <- data.frame( + geo = c("FR", "IE04", "DEB1C"), + values = c(1000, 23, 12) +) + +test_that("deprecation warning works", { + expect_warning(recode_to_nuts_2013(test_regional_codes)) + expect_warning(recode_to_nuts_2016(test_regional_codes)) + expect_message(add_nuts_level(dat)) +}) + +suppressMessages( + test_that("add_nuts_level works", { + expect_equal(add_nuts_level(dat)$nuts_level, c(0, 2, 3)) + }) +) + +# Deprecated test, needs reworking: +# test_that("Recoding gives correct results",{ +# +# skip_on_cran() +# skip_on_ci() +# +# expect_warning (harmonize_geo_code(test_regional_codes)) +# +# suppressWarnings(test_harmonized <- harmonize_geo_code(test_regional_codes)) +# +# suppressWarnings(try_recode_2013 <- recode_to_nuts_2013(test_harmonized)) +# +# lookup_code16 <- test_harmonized %>% +# filter ( geo == "FR243") %>% +# select ( code16 ) %>% unlist() %>% as.character() +# +# lookup_code13 <- test_harmonized %>% +# filter ( geo == "FRB03") %>% +# select ( code13 ) %>% unlist() %>% as.character() +# +# recode_frb <- try_recode_2013 %>% +# filter ( code16 == "FRB") %>% +# select ( geo ) %>% unlist() %>% as.character() +# +# recode_ukn02 <- try_recode_2016 %>% +# filter ( code13 == "UKN02") %>% +# select ( geo ) %>% unlist() %>% as.character() +# +# expect_equal( lookup_code16, +# "FRB03" +# ) +# expect_equal( lookup_code13, +# "FR243" +# ) +# expect_equal( lookup_code13, +# "FR243" +# ) +# expect_equal( recode_frb, +# NA_character_ +# ) +# expect_equal( recode_ukn02, +# NA_character_ +# ) +# +# }) diff --git a/tests/testthat/test_09_search.R b/tests/testthat/test_09_search.R new file mode 100755 index 00000000..bc0dec65 --- /dev/null +++ b/tests/testthat/test_09_search.R @@ -0,0 +1,32 @@ +test_that("search_eurostat finds", { + skip_on_cran() + skip_if_offline() + expect_equal( + search_eurostat( + "Dwellings by type of housing, building and NUTS 3", + type = "dataset" + )$code[1], + "cens_01rdhh" + ) + + expect_equal( + suppressWarnings( + search_eurostat( + "Volkswirtschaftliche Gesamtrechnungen", + type = "folder", + lang = "de" + ))$code[1], + "ei_qna" + ) + + expect_equal( + suppressWarnings( + search_eurostat( + "sts_os", + type = "folder", + column = "code", + lang = "fr" + ))$title[1], + "Services" + ) +}) diff --git a/tests/testthat/test_99_restore_cache.R b/tests/testthat/test_99_restore_cache.R new file mode 100644 index 00000000..4f09c7a8 --- /dev/null +++ b/tests/testthat/test_99_restore_cache.R @@ -0,0 +1,24 @@ +test_that("Restore user cache dir after testing", { + skip_on_cran() + skip_if_offline() + + # Clear test cache + cat("Test cache was ", eur_helper_detect_cache_dir(), "\n") + expect_message(clean_eurostat_cache(config = FALSE)) + + user_dir <- Sys.getenv("TEST_EUROSTAT_CURRENT_CACHE") + current_env <- Sys.getenv("EUROSTAT_CACHE_DIR") + + expect_false(current_env == user_dir) + + expect_message(expect_message( + set_eurostat_cache_dir(user_dir) + )) + newcache <- suppressMessages(set_eurostat_cache_dir(user_dir)) + expect_equal(newcache, user_dir) + expect_equal(user_dir, Sys.getenv("EUROSTAT_CACHE_DIR")) + + cat("Cache restored to ", Sys.getenv("EUROSTAT_CACHE_DIR"), "\n") + + eurostat:::clean_eurostat_toc() +}) diff --git a/vignettes/.gitignore b/vignettes/.gitignore new file mode 100644 index 00000000..097b2416 --- /dev/null +++ b/vignettes/.gitignore @@ -0,0 +1,2 @@ +*.html +*.R diff --git a/vignettes/articles/.gitignore b/vignettes/articles/.gitignore new file mode 100644 index 00000000..097b2416 --- /dev/null +++ b/vignettes/articles/.gitignore @@ -0,0 +1,2 @@ +*.html +*.R diff --git a/vignettes/articles/Accessing-culture-datasets-with-the-eurostat-package.Rmd b/vignettes/articles/Accessing-culture-datasets-with-the-eurostat-package.Rmd new file mode 100644 index 00000000..8f313937 --- /dev/null +++ b/vignettes/articles/Accessing-culture-datasets-with-the-eurostat-package.Rmd @@ -0,0 +1,270 @@ +--- +title: "Accessing culture datasets with the eurostat package" +date: "`r Sys.Date()`" +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +# Introduction + +The eurostat package can be used to access datasets related to various facets of life. Datasets belonging to the culture sector are scattered across different database tables. Eurostat has collected datasets related to the cultural sector, for example datasets that are related to culture, music, and literature, in a special section in their website: [https://ec.europa.eu/eurostat/web/culture/database/data-domain](https://ec.europa.eu/eurostat/web/culture/database/data-domain). + +Downloading these datasets manually is demonstrated in this article. + +# Loading the eurostat R package + +```{r setup} +library(eurostat) +``` + +# Dataset examples + +## EU Labour Force Survey (EU-LFS) + +Download like this: + +```{r eu-lfs} +# J59: Motion picture, video and television programme production, +# sound recording and music publishing activities +# R90: Creative, arts and entertainment activities +# R91: Libraries, archives, museums and other cultural activities + +stats <- get_eurostat( + "lfsq_egan22d", + filters = + list( + nace_r2 = c("R90", "R91", "J59") + ) + ) +stats_label <- label_eurostat(stats, code = "nace_r2") +``` + +Quick glance at dataset: + +```{r} +head(stats) +``` + +Quick glance at labeled dataset: + +```{r} +head(stats_label) +``` + +Variable names: + +```{r} +label_eurostat_vars(names(stats), id = "lfsq_egan22d") +``` + +## Structured business statistics (SBS) + +First we must ask the question: What are music-related goods and services in the vast sea of structured business statistics? From the [Eurostat website](https://ec.europa.eu/eurostat/web/culture/database/data-domain#Business%20statistics) documentation: + +>"No data collection specifically on music exists. The various EU harmonised surveys and data collections include only a few items of information on the topic. +> +>A difficulty with those is that statistical classifications and variables often do not differentiate music from other cultural activities in broader categories, such as live performances, or artistic creation." + +Two concrete examples are given: Sound recording and music publishing activities (NACE code 59.2) and Manufacture of musical instruments (NACE code 32.2). + +### Sound recording and music publishing activities (NACE code 59.2) + +```{r} +# J592: Sound recording and music publishing activities +music_business1 <- get_eurostat( + id = "sbs_na_1a_se_r2", + filters = + list( + indic_sb = c("V11110", "V12110", "V12120", + "V12150", "12170"), + nace_r2 = c("J592") + ) + ) +head(music_business1) +``` + +### Manufacture of musical instruments (NACE code 32.2) + +```{r} +music_business2 <- get_eurostat( + id = "sbs_na_ind_r2", + filters = + list( + indic_sb = c("V11110", "V12110", "V12120", + "V12130", "12150"), + nace_r2 = c("C322") + ) + ) +head(music_business2) +# Or +# music_business2 <- get_eurostat( +# id = "sbs_na_ind_r2", +# filters = list( +# indic_sb = c("V11110", "V12110", "V12120", +# "V12130", "12150"), +# nace_r2 = c("C3220") +# ) +# ) +``` + +### Music-related goods production + +Also, database on the production of various goods contains information about production of music-related goods, such as instruments and recorded media. + +The code to download the dataset: + +```{r sbs} +stats <- get_eurostat("lfsq_egan22d", + filters = + list(nace_r2 = c("R90", "R91", "J59"))) +stats_label <- label_eurostat(stats, code = "nace_r2") +``` + +## International trade in goods statistics (ITGS) (OM_dataset_sec_eurostat_003) + +What, then, are these specified music-related goods? From [Eurostat website](https://ec.europa.eu/eurostat/web/culture/database/data-domain#International%20trade): + +>"The domain of international trade in goods includes annual data on trade of musical instruments and parts of thereof. +> +>Since 2017, data on recorded media containing only music have not been collected as a separate category. In statistics on international trade in services, music items are included in the existing categories: +> +>- audio-visual services +>- artistic services +>- licences" + +(Source: [https://ec.europa.eu/eurostat/web/culture/database/data-domain#International%20trade](https://ec.europa.eu/eurostat/web/culture/database/data-domain#International%20trade)) + +Download: + +```{r} +stats <- get_eurostat("ext_lt_intertrd") +stats_label <- label_eurostat(stats, code = "sitc06") +``` + +```{r} +# C322: Manufacture of musical instruments +stats <- get_eurostat("ext_tec09", filters = list(nace_r2 = "C322")) +``` + +## Data by domain: Culture + +### Music + +Original information can be found here: [https://ec.europa.eu/eurostat/web/culture/database/data-domain](https://ec.europa.eu/eurostat/web/culture/database/data-domain) + +### Employment + +There is an Excel file that contains the number of persons employed as musicians, singers and composers (ISCO code 2652, main job) in years 2019-2021. + +2 NACE codes could be used to collect data from EU labour force survey (EU-LFS) statistics: + +- sound recording and music publishing activities (59.2) +- manufacture of musical instruments (32.2) + + +## Business statistics + +### Sound recording and music publishing activities (NACE code 59.2) + +```{r} +music_business1 <- get_eurostat( + id = "sbs_na_1a_se_r2", + filters = + list( + indic_sb = c("V11110", "V12110", "V12120", + "V12150", "12170"), + nace_r2 = c("J592") + ) + ) +head(music_business1) +``` + +### Manufacture of musical instruments + +```{r} +music_business2 <- get_eurostat( + id = "sbs_na_ind_r2", + filters = + list( + indic_sb = c("V11110", "V12110", "V12120", + "V12130", "12150"), + nace_r2 = c("C322") + ) + ) +head(music_business2) +# Or +# music_business2 <- get_eurostat( +# id = "sbs_na_ind_r2", +# filters = list( +# indic_sb = c("V11110", "V12110", "V12120", +# "V12130", "12150"), +# nace_r2 = c("C3220") +# ) +# ) +``` + +### Music-related goods production + +Downloading PRODCOM data is is done via different route than the usual datasets and the functionality is currently experimental. The logic of the functions, however, is identical to the currently existing functions. Here is a non-functional example of how the workflow should look: + +```{r remotes-example, eval=FALSE} +remotes::install_github("ropengov/eurostat", ref = "v4.1") +``` + +```{r prodcom-example, eval=FALSE} +prodcom <- get_eurostat_sdmx( + id = "DS-059359", + compressed = FALSE, + agency = "eurostat_comext", + filters = + list( + FREQ = c("A"), + product = c("18121920", "18201010", "18201030", + "18201050", "18201070", "18202050", + "18202070", "32201110", "32201130", + "32201150", "32201200", "32201310", + "32201340", "32201370", "32201400", + "32201510", "32201530", "32201600", + "32202000"), + DECL = c("001", "003", "004", "005", "006", + "007", "008", "009", "010", "011", + "017", "018", "024", "028", "030", + "032", "038", "046", "052", "053", + "054", "055", "060", "061", "063", + "064", "066", "068", "091", "092", + "093", "096", "097", "098", "2027", + "600"), + INDICATORS = c("PRODVAL"), + PRCCODE = c("18121920", "18201010", "18201030", + "18201050", "18201070", "18202050", + "18202070", "32201110", "32201130", + "32201150", "32201200", "32201310", + "32201340", "32201370", "32201400", + "32201510", "32201530", "32201600", + "32202000"))) + +prodcom_labeled <- label_eurostat_sdmx( + x, + agency = "eurostat_comext", + id = "DS-056120" + ) +``` + +The URL to this custom dataset: https://ec.europa.eu/eurostat/databrowser/view/DS-056120__custom_4088056/bookmark/table?lang=en&bookmarkId=a25712df-96d0-445a-95d6-4b807e83be43 + +# Session info + +```{r} +sessionInfo() +``` + + + + + + diff --git a/vignettes/articles/blogposts.Rmd b/vignettes/articles/blogposts.Rmd new file mode 100644 index 00000000..d0e41884 --- /dev/null +++ b/vignettes/articles/blogposts.Rmd @@ -0,0 +1,16 @@ +--- +title: "Blog posts" +date: "2022-02-07" +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +Here are some blog posts where eurostat package has been featured. If you have used eurostat package in your workflow and you would like to be included on this list, please let us know! + + * [Package release](https://rpubs.com/muuankarski/27119) + * [Interactive widgets](https://www.mytinyshinys.com/2017/07/11/eurostat/) by Andrew Clark diff --git a/vignettes/articles/cheatsheet.Rmd b/vignettes/articles/cheatsheet.Rmd new file mode 100644 index 00000000..bec5871d --- /dev/null +++ b/vignettes/articles/cheatsheet.Rmd @@ -0,0 +1,15 @@ +--- +title: "Cheat sheet: eurostat R package" +date: "2022-02-07" +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + + * [eurostat R package: cheat sheet (RStudio release version)](https://github.com/rstudio/cheatsheets/blob/main/eurostat.pdf) + + * [eurostat R package: cheat sheet (rOpenGov development version)](https://github.com/rOpenGov/eurostat/blob/master/inst/extras/cheatsheet/eurostat_cheatsheet.pdf) diff --git a/vignettes/articles/dimlst_vs_allconceptschemes.Rmd b/vignettes/articles/dimlst_vs_allconceptschemes.Rmd new file mode 100644 index 00000000..ecfa452e --- /dev/null +++ b/vignettes/articles/dimlst_vs_allconceptschemes.Rmd @@ -0,0 +1,353 @@ +--- +title: "Differences between dimlst.dic and ALL_CONCEPTSCHEMES.xml" +date: "`r Sys.Date()`" +editor_options: + chunk_output_type: inline +output: + html_document: + toc: yes +--- + +# Introduction + +As Eurostat is in the process of getting rid of Bulk Download facilities, there are inevitably some changes that affect the `eurostat` package as well. One of those is the removal of old `.dic` objects that have been used in translating Eurostat variable codes into cleartext labels in English, French or German. + +All things must pass. We have removed the old `label_eurostat_vars()` function that simply downloaded `dimlst.dic`, sort of 'master file' of all available codes and their cleartext labels, and used that to label all Eurostat datasets. Now labeling is done by downloading a Concept Scheme file for each individual dataset and using that information to give labels to the dataset in the desired language. + +# Comparison of old and new + +An example of the old English version dimlst.dic file (downloaded 2023-12-18 from [here](https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&dir=dic%2Fen)), 10 first rows: + +```{text} +ACCIDENT Accident +ACCOMMOD Mode of accommodation +ACCOMSIZE Size of accommodation by number of bedplaces +ACCOMUNIT Accommodation unit +ACL00 Classification of activities for time use +ACTIVITY Type of activity +ADMINISTR Administration indicator +AFFORD Affordability +AGE Age class +AGECHILD Age of the child +AGEDEF Age definition +``` + +An example of the new Concept Scheme file for dataset `NAMA_10_GDP` (see instructions for downloading [here](https://wikis.ec.europa.eu/display/EUROSTATHELP/API+SDMX+2.1+-+metadata+query#APISDMX2.1metadataquery-SDMX2.1endpoint-REST-SDMX-ML2.1ConceptScheme)): + +```{xml} + + + Time frequency + + + Zeitliche Frequenz + + + Fréquence (relative au temps) + + + + + + + + + + Unit of measure + + + Maßeinheit + + + Unité de mesure + + + + + + + +``` + +The apparent benefit of XML presentation is that all language versions can be found in the same file. This makes the files a bit larger than old .tsv files but for individual datasets the size is still manageable. + +Concept id's or Ref id's ("unit", "UNIT") can be used to look up further classifications in Codelists. In the old .dic metaphor all definition files were "dictionaries" where `dimlst.dic` was a special case, a dictionary of dictionaries, whereas in the new metaphor there is more of a hierarchy of definitions. For example in the case of units all available English labels can be downloaded in JSON-stat and TSV formats: + +https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/codelist/ESTAT/UNIT?format=TSV&lang=en +https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/codelist/ESTAT/UNIT?format=JSON&lang=en + +However, the new TSV files and the old .dic files are virtually identical: + +```{text} +# unit.dic +TOTAL Total +NR Number +NR_HAB Number per inhabitant +THS Thousand +MIO Million +BN Billion +CT Euro cent +EUR Euro +THS_EUR Thousand euro +MIO_EUR Million euro +BN_EUR Billion euro +[...] +[711 lines] + +# ESTAT_UNIT_22.0_EN.tsv +TOTAL Total +NR Number +NR_HAB Number per inhabitant +THS Thousand +MIO Million +BN Billion +CT Euro cent +EUR Euro +THS_EUR Thousand euro +MIO_EUR Million euro +BN_EUR Billion euro +[...] +[711 lines] +``` + +# Replicating old style dimlst.dic with new xml files + +In some cases it may have been useful to access all labels for all datasets from a single file. In theory this is possible with Concept Schemes as well, by downlaoding all concept schemes at once in the form of "ALL_CONCEPTSCHEMES.xml" file, at 29.7 Mb large. We can parse the xml file to create a list that is similar to the old `dimlst.dic` object to see if there are any functional differences. + +```{r eval=FALSE} +library(xml2) +# file downloaded from https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/conceptscheme/ESTAT/?compressed=true and unpacked +xml_object <- xml2::read_xml("ALL_CONCEPTSCHEMES.xml") +number <- length(xml2::xml_find_all(xml_object, ".//s:Concept")) +dic_df <- data.frame( + code_name = rep(NA, times = number), + full_name = rep(NA, times = number) +) +attributes <- xml2::xml_attrs(xml2::xml_find_all(xml_object, ".//s:Concept")) +contents <- xml2::xml_text(xml2::xml_find_all(xml_object, ".//s:Concept/c:Name[@xml:lang='en']")) +for (i in seq_len(number)) { + dic_df$code_name[i] <- unname(attributes[[i]]["id"]) +} +# This is ok because the length of and is the same +dic_df$full_name <- contents +``` + +We can see that there are more unique codes than labels and that some labels are being used in several different codes: + +```{r eval=FALSE} +length(unique(dic_df$full_name)) +# [1] 586 +length(unique(dic_df$code_name)) +# [1] 592 +``` + +To make the data.frame similar to the one that we would get from reading a tab-separated .dic object: + +```{r eval=FALSE} +# Select unique rows +library(dplyr) + +new_df <- dic_df %>% + distinct() + +# codes toupper +new_df$code_name <- toupper(new_df$code_name) + +new_df_sort <- new_df[order(new_df$code_name),] + +# Remove row numbers +rownames(new_df_sort) <- NULL + +head(new_df_sort) +# code_name full_name +# 1 ACCIDENT Accident +# 2 ACCOMMOD Mode of accommodation +# 3 ACCOMSIZE Size of accommodation by number of bedplaces +# 4 ACCOMUNIT Accommodation unit +# 5 ACL00 Classification of activities for time use +# 6 ACTIVITY Type of activity +``` + +As comparison to the `dimlst.dic` object: + +```{r eval=FALSE} +# downloaded from https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=1&dir=dic%2Fen +dimlst_dic <- readr::read_tsv("dimlst.dic", + col_names = c("code_name", "full_name"), + col_types = readr::cols(.default = readr::col_character())) + +head(dimlst_dic) +## A tibble: 6 × 2 +# code_name full_name +# +# 1 ACCIDENT Accident +# 2 ACCOMMOD Mode of accommodation +# 3 ACCOMSIZE Size of accommodation by number of bedplaces +# 4 ACCOMUNIT Accommodation unit +# 5 ACL00 Classification of activities for time use +# 6 ACTIVITY Type of activity +``` + +Clearly, the two objects have different types (the former is a data.frame, the latter is a tibble), but that doesn't stop us from noticing that at least the 6 first rows are similar with each other. However, `dimlst.dic` has 623 rows while `new_df` object has 593 rows (obs.). Let's find out what the differences are: + +```{r eval=FALSE} +setdiff(dimlst_dic$code_name, new_df_sort$code_name) +# [1] "AGR_INP" "CALCMETH" "DIMLST" "ECOSYST" "ECOSYST_C" "FARMSIZE" "HLTH_HLE" +# [8] "HOSPCARE" "HOUS_ANI" "INDIC_AGR" "IND_ACCT" "IND_IMPV" "ISSUER" "LEARNING" +# [15] "LEV_INTRF" "MANSTO" "NRG_FLOW" "NRG_TECH" "OBS_STATUS" "OGA_FAM" "OGA_NRH" +# [22] "OGA_RH" "OGA_TYPE" "PEDS" "PERS_INV" "PRD_ACCT" "PRD_AMO" "RAWMATPR" +# [29] "RAWMATSEC" "REVDATE" "SIZE_TUR" "TABLE_DIC" "TIME" "VOT_CAT" "WEEK" +# [36] "YN_CARE" "YN_DIF" "YN_DIS" +# length: 38 items +length(setdiff(dimlst_dic$code_name, new_df_sort$code_name)) +# [1] 38 +setdiff(new_df_sort$code_name, dimlst_dic$code_name) +# [1] "FIELDID" "INDIC_EU" "OBS_FLAG" "OBS_VALUE" "SIZEN_R2" "TARGET" "TARGET_FLAG" +# [8] "TIME_PERIOD" +length(setdiff(new_df_sort$code_name, dimlst_dic$code_name)) +# [1] 8 +``` + +To summarize, the old `dimlst.dic` file has 38 codes that are not found in the `ALL_CONCEPTSCHEMES.xml` file. The new `ALL_CONCEPTSCHEMES.xml` has 8 codes that are not found in the old `dimlst.dic`. + +For more information about the different fields, let's print the codes and their descriptions. Unique to `dimlst_dic` object: + +```{r eval=FALSE} +print(dimlst_dic[which(dimlst_dic$code_name %in% setdiff(dimlst_dic$code_name, new_df_sort$code_name)),], n = 40) +# A tibble: 38 × 2 +# code_name full_name +# +# 1 AGR_INP Agricultural inputs +# 2 CALCMETH Calculation method +# 3 DIMLST null +# 4 ECOSYST Ecosystem typology +# 5 ECOSYST_C Ecosystem typology - converted +# 6 FARMSIZE Size of farm +# 7 HLTH_HLE Health and life expectancy +# 8 HOSPCARE Hospital care +# 9 HOUS_ANI Animal housing +# 10 INDIC_AGR Agricultural indicators +# 11 IND_ACCT Industries and accounting items +# 12 IND_IMPV Industries, imports and valuations +# 13 ISSUER Type of issuer +# 14 LEARNING Learning form +# 15 LEV_INTRF Level of interference +# 16 MANSTO Manure storage +# 17 NRG_FLOW Energy flows +# 18 NRG_TECH Energy technologies +# 19 OBS_STATUS Observation status (Flag) +# 20 OGA_FAM Other gainful activity of the family members +# 21 OGA_NRH Other gainful activity of the holder (not related to the agricultural holding) +# 22 OGA_RH Other gainful activity of the holder (related to the agricultural holding) +# 23 OGA_TYPE Types of other gainful activity (OGA) related to the agricultural holding +# 24 PEDS Potential Environmentally Damaging Subsidies (ESA transfers) +# 25 PERS_INV Persons involved in the accident +# 26 PRD_ACCT Products and accounting items +# 27 PRD_AMO Products, adjustments and market output +# 28 RAWMATPR Primary raw materials +# 29 RAWMATSEC Secondary raw materials +# 30 REVDATE Revision date +# 31 SIZE_TUR Size classes of turnover +# 32 TABLE_DIC null +# 33 TIME Period of time +# 34 VOT_CAT Category of voters +# 35 WEEK Calendar week +# 36 YN_CARE Use of profesional care - Yes/No +# 37 YN_DIF Difficulties - Yes/No +# 38 YN_DIS Disability - Yes/No +``` + +Unique to new_df or new_df_sort: + +```{r eval=FALSE} +new_df_sort[which(new_df_sort$code_name %in% setdiff(new_df_sort$code_name, dimlst_dic$code_name)),] +# code_name full_name +# 152 FIELDID Agricultural product +# 221 INDIC_EU Indicators for EU2020 project +# 364 OBS_FLAG Observation status (Flag) +# 365 OBS_VALUE Observation value +# 464 SIZEN_R2 Enterprise size and Nace Rev. 2 +# 502 TARGET TARGET Observation value +# 503 TARGET_FLAG TARGET Observation status (Flag) +# 510 TIME_PERIOD Time +``` + +Of these, "FIELDID / Agricultural product" seems almost like an input error as there are also "prod_apr / Agricultural product (old codes)" and "agriprod / Agricultural products" on the list. If that were the case, the correct course of action is to of course give feedback to Eurostat. + +# Types of duplicates + +There are also some duplicates in the list created from the XML file. Duplicates exist both in the `code_name` column or in the `full_name` column. + +```{r eval=FALSE} +new_df_sort[duplicated(new_df_sort$code_name),] +# code_name full_name +# 471 SO_EUR Standardoutput in Euros + +new_df_sort[duplicated(new_df_sort$full_name),] +# code_name full_name +# 35 ASYL_APP Applicant type +# 179 HHTYPE Type of household +# 227 INDIC_INN Innovation indicator +# 233 INDIC_NRG Energy indicator +# 237 INDIC_SBS Economical indicator for structural business statistics +# 452 SECTPART Sector (ESA 2010) +# 528 TY Type of expenditure +``` + +The differences can be due to typos (probably ?): + +```{r eval=FALSE} + +new_df_sort[which(new_df_sort$code_name == "SO_EUR"),] +# code_name full_name +# 470 SO_EUR Standard output in Euros +# 471 SO_EUR Standardoutput in Euros + +new_df_sort[which(new_df_sort$full_name == "Type of household"),] +# code_name full_name +# 178 HHTYP Type of household +# 179 HHTYPE Type of household + +new_df_sort[which(new_df_sort$full_name == "Innovation indicator"),] +# code_name full_name +# 226 INDIC_IN Innovation indicator +# 227 INDIC_INN Innovation indicator + +new_df_sort[which(new_df_sort$full_name == "Economical indicator for structural business statistics"),] +# code_name full_name +# 236 INDIC_SB Economical indicator for structural business statistics +# 237 INDIC_SBS Economical indicator for structural business statistics +``` + +...or due to actual differences (?) in the fields, although there could also be some kind of a misunderstanding: + +```{r eval=FALSE} +# Different types of applicants +new_df_sort[which(new_df_sort$full_name == "Applicant type"),] +# code_name full_name +# 24 APPLICANT Applicant type +# 35 ASYL_APP Applicant type + +new_df_sort[which(new_df_sort$full_name == "Energy indicator"),] +# code_name full_name +# 218 INDIC_EN Energy indicator +# 233 INDIC_NRG Energy indicator + +new_df_sort[which(new_df_sort$full_name == "Sector (ESA 2010)"),] +# code_name full_name +# 450 SECTOR10 Sector (ESA 2010) +# 452 SECTPART Sector (ESA 2010) + +new_df_sort[which(new_df_sort$full_name == "Type of expenditure"),] +# code_name full_name +# 144 EXPEN Type of expenditure +# 528 TY Type of expenditure +``` + +# Conclusion + +Changes in methods of delivering metadata affect, naturally, all end users of Eurostat data. With the eurostat package version 4.0.0 we have aimed at retaining the user-facing functionalities and the expected outputs. The interesting world of XML parsing is kept under the hood and should not be of concern for users. + +This document is written mainly as a future reference for ourselves if we might at some point be wondering why something that was possible before is not possible anymore, or for users who have had scripts relying on functions like `label_eurostat_vars()`. + +Feel free to open an issue or a pull request in Github if there are any suggestions or corrections you would like to make. \ No newline at end of file diff --git a/vignettes/articles/eurostat_tutorial.Rmd b/vignettes/articles/eurostat_tutorial.Rmd new file mode 100644 index 00000000..79218a7d --- /dev/null +++ b/vignettes/articles/eurostat_tutorial.Rmd @@ -0,0 +1,399 @@ +--- +title: "Tutorial for the eurostat R package" +date: "`r Sys.Date()`" +editor_options: + chunk_output_type: inline +output: + html_document: + toc: yes +--- + +# R Tools for Eurostat Open Data + +This [rOpenGov](http://ropengov.github.io) R package provides tools to access [Eurostat database](http://ec.europa.eu/eurostat/data/database), which you can also browse on-line for the data sets and documentation. For contact information and source code, see the [package website](http://ropengov.github.io/eurostat/). + +```{r setup, include=FALSE} +# Global options +library(knitr) +opts_chunk$set(fig.path = "fig/") +options(citation.bibtex.max=999) +``` + +# Installation + +Release version [(CRAN)](https://CRAN.R-project.org/package=eurostat): + +```{r install, eval=FALSE} +install.packages("eurostat") +``` + +Development version [(Github)](https://github.com/rOpenGov/eurostat): + +```{r install_devel, eval=FALSE} +library(remotes) +remotes::install_github("ropengov/eurostat") +``` + +Alternatively, development versions (more specifically: development versions in the master branch of eurostat GitHub repository) can be installed with the help of R-universe: + +```{r install_runiverse, eval=FALSE} +# Enable this universe +options(repos = c( + ropengov = "https://ropengov.r-universe.dev", + CRAN = "https://cloud.r-project.org" +)) + +install.packages("eurostat") +``` + +The package is loaded with the library function. + +```{r, echo=FALSE} +library(eurostat) +``` + +Overall, the eurostat package includes the following user-facing functions: + +```{r, echo=FALSE,comment=NA} +cat(paste0(library(help = "eurostat")$info[[2]], collapse = "\n")) +``` + +```{r check_internet} +evaluate <- curl::has_internet() +``` + + +# Finding data + +First stop for most researchers would be to browse the [Eurostat Data Browser website](https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes) or other thematically arranged sections in the Eurostat website. However, the eurostat R package offers some ways to search for datasets without leaving the R interface. + +Function `get_eurostat_toc()` downloads a table of contents (TOC) of eurostat datasets. + +```{r get_eurostat_toc, warning=FALSE, message=FALSE, eval = evaluate} +# Load the package +library(eurostat) + +# Get Eurostat data listing +toc <- get_eurostat_toc() + +# Check the first items +library(knitr) +kable(tail(toc)) +``` + +The values in column 'code' are unique identifiers for each dataset that have to be used when downloading specific datasets. In the `get_eurostat()` function the dataset code is put into the first argument of the function, `id`. + +From eurostat version 4.0.0 onwards the returned TOC object has had an additional column, `hierarchy`. It is used to determine which dataset belongs to which folder. This is helpful for example when downloading datasets in a single folder all at once. + +From eurostat version 4.0.0 onwards it is possible to download TOC objects in French and German as well, in addition to English, which remains the default option. This enables new functionalities in other eurostat functions that have used the TOC object internally but retains backwards-compatibility with old code as the `lang` argument is not mandatory and queries without it continue to deliver the English version of the TOC object. + +```{r get_eurostat_toc2, warning=FALSE, message=FALSE, eval = evaluate} +kable(head(get_eurostat_toc(lang = "fr"))) +``` + +With `search_eurostat()` you can search the table of contents for particular patterns, e.g. all datasets related to *passenger transport*. With the `type` argument the user can choose which types of datasets the search should return: datasets, tables, folders or all types (the default). + +According to [Eurostat database basic terminology](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Eurostat_database_-_basic_terminology) "tables (predefined tables) are used to provide easy access to the main statistical indicators. They are based in general on datasets and are derived from them. They are predefined, non-modifiable and presented as two or three dimensional tables." The more general purpose datasets are, on the other hand, described to be "multi-dimensional tables" that have "up to 8 dimensions" and are used "store the base data, more appropriate for use by statistical and other experts via special applications". + +```{r search_eurostat, warning=FALSE, message=FALSE, eval = evaluate} +# info about passengers +kable(head(search_eurostat("passenger transport"))) +``` + +From eurostat version 4.0.0 onwards it possible to perform searches also from dataset codes. This is done by specifying the search column by setting the `column` attribute to `"code"`. Searching for codes can be useful in finding datasets that belong to the same folder or are part of a larger theme that shares similar dataset code patterns, such as "migr" for migration related statistics and "tran" in the case of (multimodal) transport statistics. + +```{r search_eurostat2, warning=FALSE, message=FALSE, eval = evaluate} +kable(head(search_eurostat("migr", column = "code"))) +``` + +Another new addition in version 4.0.0 is the option to perform searches from French and German language TOC versions as well by setting the `lang` argument to `"fr"` or `"de"`. Naturally, dataset codes are shared between language versions so French and German language searches should be conducted only on the title column. + +```{r search_eurostat3, warning=FALSE, message=FALSE, eval = evaluate} +kable(head(search_eurostat("flughafen", column = "title", lang = "de"))) +``` + +As mentioned in the beginning, codes for different dataset can be found also from the [Eurostat database](http://ec.europa.eu/eurostat/data/database). The Eurostat database gives codes in the Data Navigation Tree in parenthesis after the full name of the dataset, folder, or table. + +# Downloading data + +The package supports two of the Eurostats download methods: the SDMX 2.1 REST API and the API Statistics ("JSON API"). The bulk download facility is the fastest method to download whole datasets. To download only a small section of the dataset the JSON API is faster, as it allows to make a data selection before downloading. + +The end user does not usually have to bother where original data is downloaded, as both data sources are accessed via the main download function `get_eurostat()`. If only the table id is given, the whole table is downloaded from the SDMX 2.1 REST API. If any filters are given the JSON API is used instead. However, the `get_eurostat_json()` function used internally is also a user-facing function so that can be used as well. + +We will use the dataset 'Modal split of air, sea and inland passenger transport' as an example dataset in this vignette. This is the percentage share of each mode of transport in total inland transport, expressed in passenger-kilometres (pkm) based on transport by passenger cars, buses and coaches, trains, aircraft, and seagoing vessels. All data should be based on movements on national territory, regardless of the nationality of the vehicle. However, the data collection is not harmonized at the EU level. For more detailed information about the dataset, see [Reference Metadata](https://ec.europa.eu/eurostat/cache/metadata/en/tran_hv_ms_esms.htm). + +Pick and print the id of the data set to download: +```{r get_id, warning=FALSE, message=FALSE, results='asis', eval = evaluate} +# Perform search, the output is a table of search results +search_results <- search_eurostat("Modal split of air, sea and inland passenger transport", + type = "dataset" +) + +# Since our search term was so detailed, we should have only 1 result / 1 row +kable(head(search_results)) + +# Get the id from the table +id <- search_results$code[1] + +# Check the id +print(id) +``` + + +```{r reevaluate, include = FALSE, eval = evaluate} +if (!is.na(id)) evaluate <- TRUE else evaluate <- FALSE +``` + +Get the whole corresponding table. As the table is annual data, it is more +convenient to use a numeric time variable than use the default date format, where yearly data would be coerced to be on the first day of the year (e.g. 2000-01-01). + +```{r get_eurostat, warning=FALSE, message=FALSE, results='asis', eval = evaluate} +dat <- get_eurostat(id, time_format = "num", stringsAsFactors = TRUE) +``` + +The structure of the downloaded data set can be investigated by using the base R `str()` function: + +```{r str_dat, warning=FALSE, message=FALSE, eval = evaluate} +str(dat) +``` + +```{r head_dat, warning=FALSE, message=FALSE, results='asis', eval = evaluate} +kable(head(dat)) +``` + +You can get only a part of the dataset by defining `filters` argument. It +should be named list, where names corresponds to variable names (lower case) and +values are vectors of codes corresponding desired series (upper case). For +time variable, in addition to a `time` or `TIME_PERIOD` , also `sinceTimePeriod`, `untilTimePeriod` and a `lastTimePeriod` can be used. + +More information about filtering can be found in `get_eurostat()` and `get_eurostat_json()` function documentation. + +```{r get_eurostat_json, warning=FALSE, message=FALSE, results='asis', eval=evaluate} +dat2 <- get_eurostat(id, filters = list(geo = c("EU27_2020", "FI"), lastTimePeriod = 1), time_format = "num") +kable(dat2) +``` + +## Replacing codes with labels + +By default variables are returned as Eurostat codes, but to get human-readable +labels instead, use a `type = "label"` argument in `get_eurostat()`. + +```{r json_labels, warning=FALSE, message=FALSE, results='asis', eval = evaluate} +dat_labeled2 <- get_eurostat(id, + filters = list( + geo = c("EU27_2020", "FI"), + lastTimePeriod = 1 + ), + type = "label", time_format = "num" +) +kable(head(dat_labeled2)) +``` + +Eurostat codes in the downloaded data set can be replaced with +human-readable labels from the Eurostat dictionaries with the +`label_eurostat()` function. + +```{r labels, warning=FALSE, message=FALSE, results='asis', eval = evaluate} +dat_labeled <- label_eurostat(dat) +kable(head(dat_labeled)) +``` + +The `label_eurostat_vars()` allows conversion of variable names as well. + +```{r name_labels, eval = evaluate} +print(label_eurostat_vars(id = "tran_hv_ms_psmod", names(dat_labeled))) +``` + + +Vehicle information has 5 levels. You can check them now with: + +```{r vehicle_levels, eval = evaluate} +levels(dat_labeled$vehicle) +``` + +## Downloading data interactively + +New function in the eurostat package version 4.0.0 is the `get_eurostat_interactive()` function that allows users to search and download datasets with the help of interactive menus. If the user already knows which dataset they want to download, the `get_eurostat_interactive()` function can also take a dataset code as a parameter, skipping the search part of the interactive menu. Below we will demonstrate the whole process from search to download to printing a citation for the dataset, utilizing several different eurostat package functions at once. + +```{text} +> get_eurostat_interactive() +Select language + +1: English +2: French +3: German + +Selection: 1 +Enter search term for data: aviation +Which dataset would you like to download? + +1: [tran_sf_aviagah] Air accident victims in general aviation, by country of occurrence and country of registration of aircraft - maximum take-off mass above 2250 kg (source: EASA) +2: [tran_sf_aviagal] Air accident victims in general aviation by country of occurrence and country of registration of aircraft - maximum take-off mass under 2250 kg (source: EASA) +3: [avia_ec_enterp] Number of aviation and airport enterprises +4: [avia_ec_emp_ent] Employment in aviation and airport enterprises by sex + + +Selection: 4 +Download the dataset? + +1: Yes +2: No + +Selection: 1 +Would you like to use default download arguments or set them manually? + +1: Default +2: Manually selected + +Selection: 1 +trying URL 'https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/avia_ec_emp_ent?format=TSV&compressed=true' +Content type 'text/tab-separated-values; charset=UTF-8' length 1354 bytes +================================================== +downloaded 1354 bytes + +Table avia_ec_emp_ent cached at /var/folders/f4/h_r3y60n0nn0qm6qx5hnx1s00000gn/T//RtmpDJ1gUA/eurostat/60ee371bcdcc9b130a20514d1e0d574d.rds +Print dataset citation? + +1: Yes +2: No + +Selection: 1 +Print code for downloading dataset? + +1: Yes +2: No + +Selection: 1 +Print dataset fixity checksum? + +1: Yes +2: No + +Selection: 1 +##### DATASET CITATION: + +@Misc{avia-ec-emp-ent-2016-4-20, + title = {Employment in aviation and airport enterprises by sex (avia\_ec\_emp\_ent)}, + url = {https://ec.europa.eu/eurostat/web/products-datasets/product?code=avia_ec_emp_ent}, + language = {english}, + year = {2016}, + author = {{Eurostat}}, + urldate = {2023-12-19}, + type = {Dataset}, + note = {Accessed 2023-12-19, dataset last updated 2016-04-20}, +} + +##### DOWNLOAD PARAMETERS: + +get_eurostat(id = 'avia_ec_emp_ent') + +##### FIXITY CHECKSUM: + +Fixity checksum (md5) for dataset avia_ec_emp_ent: 36975282eaaea50a6e5f0e6cd64ef4d2 + +# A tibble: 450 × 6 + freq enterpr sex geo TIME_PERIOD values + + 1 A AIRP F CY 2006-01-01 192 + 2 A AIRP F CY 2007-01-01 240 + 3 A AIRP F CY 2008-01-01 514 + 4 A AIRP F CY 2009-01-01 3278 + 5 A AIRP F CY 2010-01-01 2587 + 6 A AIRP F CY 2011-01-01 2255 + 7 A AIRP F CY 2012-01-01 2954 + 8 A AIRP F CZ 2001-01-01 0 + 9 A AIRP F CZ 2002-01-01 0 +10 A AIRP F CZ 2003-01-01 0 +# ℹ 440 more rows +# ℹ Use `print(n = ...)` to see more rows +``` + +# Selecting and modifying data + +## EFTA, Eurozone, EU and EU candidate countries + +To facilitate smooth visualization of standard European geographic areas, the package provides ready-made lists of the country codes used in the eurostat database for EFTA (efta\_countries), Euro area (ea\_countries), EU (eu\_countries) and EU candidate countries (eu\_candidate\_countries). These can be used to select specific groups of countries for closer investigation. For conversions with other standard country coding systems, see the [countrycode](https://CRAN.R-project.org/package=countrycode) R package. To retrieve the country code list for EFTA, for instance, use: + +```{r, echo=TRUE, eval = evaluate} +data(efta_countries) +kable(efta_countries) +``` + + +## EU data from 2012 in all vehicles: + +```{r eu_12, eval = evaluate} +dat_eu12 <- subset(dat_labeled, geo == "European Union - 27 countries (from 2020)" & TIME_PERIOD == 2012) +kable(dat_eu12, row.names = FALSE) +``` + +## EU data from 2008 - 2020 with vehicle types as variables: + +Reshaping the data is best done with `spread()` in `tidyr`. +```{r eu_vehicles_table, eval = evaluate} +library("tidyr") +dat_eu_0012 <- subset(dat, geo == "EU27_2020" & TIME_PERIOD %in% c(2008:2020)) +dat_eu_0012_wide <- spread(dat_eu_0012, vehicle, values) +kable(subset(dat_eu_0012_wide, select = -geo), row.names = FALSE) +``` + +## Train passengers for selected EU countries in 2008 - 2020 + +```{r trains_table, eval = evaluate} +dat_trains <- subset(dat_labeled, geo %in% c("Austria", "Belgium", "Finland", "Sweden") & + TIME_PERIOD %in% c(2008:2020) & + vehicle == "Trains") +dat_trains_wide <- spread(dat_trains, geo, values) +kable(subset(dat_trains_wide, select = -vehicle), row.names = FALSE) +``` + + +## Other packages + +### Strongly recommended + +The `giscoR` ([package homepage](https://ropengov.github.io/giscoR/)) package used to be only suggested but starting from `eurostat` version 4.0.0 it has become a dependency of eurostat and required for using geospatial data functions. In addition to using `get_eurostat_geospatial()` from the `eurostat` package, it is highly recommended to study `giscoR` package functions and vignettes for creating more sophisticated visualisations to support geospatial analyses. + +### Packages with similar functionalities + +The [restatapi](https://CRAN.R-project.org/package=restatapi) R package has similar functionalities and some familiar function names for seasoned eurostat R package users. The `restatapi` package focuses more on statistical data and retrieving and returning data in a non-tidy data format. + +The [rsdmx](https://CRAN.R-project.org/package=rsdmx) and [rjsdmx](https://CRAN.R-project.org/package=RJSDMX) R packages provide a more generic method to download data from a wide variety of statistical data providers that utilize the Statistical Data and Metadata eXchange ([SDMX](https://sdmx.org)) standards. + +# Further examples + +For further examples, see articles in the [package homepage](http://ropengov.github.io/eurostat/). + +# Citations and related work + +### Citing the data sources + +Eurostat data: cite [Eurostat](http://ec.europa.eu/eurostat/). + +Administrative boundaries: cite EuroGeographics + + +### Citing the eurostat R package + +For main developers and contributors, see the [package homepage](http://ropengov.github.io/eurostat). + +This work can be freely used, modified and distributed under the +BSD-2-clause (modified FreeBSD) license: + +```{r citation, message=FALSE, eval=TRUE, echo=TRUE} +citation("eurostat") +``` + + +### Contact + +For contact information, see the [package homepage](http://ropengov.github.io/eurostat). + + +# Version info + +This tutorial was created with + +```{r sessioninfo, message=FALSE, warning=FALSE} +sessioninfo::session_info() +``` diff --git a/vignettes/articles/mapping.Rmd b/vignettes/articles/mapping.Rmd new file mode 100755 index 00000000..48912477 --- /dev/null +++ b/vignettes/articles/mapping.Rmd @@ -0,0 +1,399 @@ +--- +title: "Mapping Regional Data, Mapping Metadata Problems" +author: Daniel Antal (CFA) +date: "`r Sys.Date()`" +output: + html_document: + toc: yes +editor_options: + markdown: + wrap: 80 + canonical: true +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + tidy = "styler", + dev = "ragg_png", + warning = FALSE, + dpi = 300, + out.width = "100%" +) +``` + +The [regions](https://regions.dataobservatory.eu/) package offers tools two work +with regional statistics. It is an offspring of the +[eurostat](https://ropengov.github.io/eurostat/) package of +[rOpenGov](https://ropengov.org/), which offers data search, download, +manipulation and visualization for Eurostat's [European +statistics](https://ec.europa.eu/eurostat). While you can use +[regions](https://regions.dataobservatory.eu/) for any European regional +statistics, and with a limited functionality, any regional statistics from other +parts of the world, this article provides a combined use case for the two +packages. + +```{r setup} +library(regions) +library(eurostat) +library(dplyr) +``` + +Eurostat's main function for data access is `get_eurostat()`, but in this case +we will use the more specific `get_eurostat_json()` to avoid downloading +unnecessary aspects of this data product. Let us get a long-established regional +dataset, the full-time equivalent (FTE) R&D workforce, in both sexes, in all +sectors and all professional positions, and limit our data to two years only: + +```{r rd-workforce-get, eval=FALSE} +regional_rd_personnel <- get_eurostat_json( + id = "rd_p_persreg", + filters = list( + sex = "T", + prof_pos = "TOTAL", + sectperf = "TOTAL", + unit = "FTE" + ) +) + +regional_rd_personnel <- regional_rd_personnel %>% + filter(time %in% c("2009", "2018")) +``` + +We have saved this filtered datasets as `regions::regional_rd_personnel` in the +[regions](https://regions.dataobservatory.eu/) package. + +```{r rd-presaved} +data("regional_rd_personnel") +``` + +We have quiet a few missing cases: + +```{r missing-cases} +summary(is.na(regional_rd_personnel$values)) +``` + +But this is not the only problem with the dataset. + +## Choropleth Map + +Let us try to place the data on a `ggplot2` map. + +```{r} +library(ggplot2) +``` + +Let us download a map with `get_eurostat_geospatial()`. We will use the +`NUTS2016`, i.e., `year = 2016`, which is the regional boundary definition set +in 2016 and used in the period 2018-2020. This is the most used definition in +2021. + +```{r get-map} +# Need to load sf for using dplyr methods with sf objects +library(sf) + +map_nuts_2 <- get_eurostat_geospatial( + resolution = "60", nuts_level = "2", + year = 2016 +) +``` + +You should always join your data with the geometric information of the regions +starting from left with the map: + +```{r} +indicator_with_map <- map_nuts_2 %>% + left_join(regional_rd_personnel, by = "geo") +``` + +Huge parts of Europe are not covered, but the missing values are not randomly +missing. France went under a regional reform; Turkey and Albania did not provide +this data earlier. Ireland has no regional statistics available. + +```{r choro1} +indicator_with_map %>% + ggplot() + + geom_sf(aes(fill = values), + color = "dim grey", linewidth = .1 + ) + + scale_fill_gradient(low = "#FAE000", high = "#00843A") + + facet_wrap(facets = "time") + + labs( + title = "R&D Personnel & Researchers", + subtitle = "In all sectors, both sexes by NUTS 2 regions", + caption = "\ua9 EuroGeographics for the administrative boundaries + \ua9 Tutorial and ready-to-use data on economy.dataobservatory.eu", + fill = NULL + ) + + theme_light() + + theme(legend.position = "none") + + coord_sf( + xlim = c(2377294, 7453440), + ylim = c(1313597, 5628510), + crs = 3035 + ) +``` + +## Missing Values and Seemingly Missing Values + +Some of these problems are real missing data problems, but some of them are +coding problem. In other words, the data is there, but it is not conforming the +boundaries that you have on the `NUTS2016` map. First we need to validate the +geographical coding of the dataset. This is the task of +`regions::validate_nuts_regions()`. + +```{r} +validated_indicator <- regions::validate_nuts_regions(regional_rd_personnel) +``` + +If we validate the dataset, we will see many interesting metadata observations. + +```{r validation-summary, message=FALSE} +library(dplyr) +validation_summary_2016 <- validated_indicator %>% + group_by(time, typology) %>% + summarize( + observations = n(), + values_missing = sum(is.na(values)), + values_present = sum(!is.na(values)), + valid_present = values_present / observations + ) +``` + +Even though the dataset is called [R&D personnel and researchers by sector of +performance, sex and NUTS 2 regions +(rd_p_persreg)](https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=rd_p_persreg&lang=en), +in fact, it contains data on country and `NUTS1` levels. And it has data on +non-EU countries that in 2009 were not part of the NUTS system. + +```{r} +validation_summary_2016 %>% + ungroup() %>% + filter(time == "2009") +``` + +The situation is not better in 2018: + +```{r} +validation_summary_2016 %>% + ungroup() %>% + filter(time == "2018") +``` + +The dataset is plagued with data that has no place in the `NUTS2016` boundary +definition, and therefore on a `NUTS2016` map! + +What are the non-conforming bits? + +```{r non-conforming-geo} +validated_indicator %>% + filter(!valid_2016) %>% + pull(geo) +``` + +- Plenty of French units. France went under a regional administrative reform, + and we have data about its past, but not in the current boundaries and + coding. +- To a lesser extent, we have the same problem with Poland and the UK. +- We have comparative data from Asia on country level, which ended up in a + regional dataset. +- We have Norway, which is a member of the EEA, and from 2021 it is officially + part of the NUTS2021 system. They were nice to provide their data + consistently for the past. +- We have aggregates like the entire EU or the eurozone. + +## Recoding and Renaming + +The question is, can we save some of the French data? If the boundaries of +regions changed, then we cannot: somebody must reaggregate the number of +researchers who used to work in the newly defined region back then, before the +reform. + +But in some cases, the regional boundaries did not change, only the name and the +code of the region, which is the task performed by `regions::recode_nuts()`: + +```{r recoding} +recoded_indicator <- regional_rd_personnel %>% + regions::recode_nuts( + geo_var = "geo", # your geograhical ID variable name + nuts_year = 2016 # change this for other definitions + ) +``` + +```{r recode-summary, message=FALSE} +recoding_summary <- recoded_indicator %>% + mutate(observations = nrow(.data)) %>% + mutate(typology_change = ifelse(grepl("Recoded", typology_change), + yes = "Recoded", + no = typology_change + )) %>% + group_by(typology_change, time) %>% + summarize( + values_missing = sum(is.na(values)), + values_present = sum(!is.na(values)), + pct = values_present / (values_present + values_missing) + ) +``` + +Let us take a look at the problems identified by `regions::recode_nuts()`: + +```{r} +recoding_summary +``` + +- We were able to recode quite a few data points to the `NUTS2016` definition + for the time of observation 2009 as well as 2018. Sometimes we are recoding + rows that have missing values, which does not help that much: we know where + the data should be, but it is missing anyway. But particularly for the year + 2009 we can save plenty of data by recorded the obsolete coding. + +- We identify further problems. We have coding the that was used in various + time periods, but there is no clear recoding possibility, because the + regions boundaries have changed. To have the history of the data, we would + need to recalculate them, say, by adding up the R&D personnel from each + settlement in the new regional boundary. + +The following non-empty cases were present in the dataset, just not with the +coding that we used in the 2018-2020 period (i.e., the `NUTS2016` coding.) We +are able to save 27 observations just by fixing the regional codes! + +```{r geocode-changes} +recoded_indicator %>% + filter(typology == "nuts_level_2") %>% + filter(!is.na(typology_change)) %>% + filter( + # Keep only pairs where we actually save + # non-missing observations + !is.na(values) + ) %>% + distinct(geo, code_2016) %>% + filter( + # We filter out cases of countries who + # joined the NUTS system later + geo != code_2016 + ) +``` + +So, let us do the trick: change the `geo` variable to `code_2016`, which is, +whenever there is an equivalent `geo` code in the `NUTS2016` definition, the +data that you should have. Your original `geo` variable contains codes that were +used, for example, in the `NUTS2010` or `NUTS2013` boundary definitions. + +```{r change-to-nuts2016, eval=TRUE} +recoded_with_map <- map_nuts_2 %>% + left_join( + recoded_indicator %>% + mutate(geo = code_2016), + by = "geo" + ) +``` + +Let us make our work visible by creating three observation `type` variables: + +- `missing` which is not present in the dataset; +- `before` which were correctly coded before our recoding; +- `after` which became visible after recoding. + +```{r} +regional_rd_personnel_recoded <- recoded_indicator %>% + mutate(geo = code_2016) %>% + rename(values_2016 = values) %>% + select(-typology, -typology_change, -code_2016) %>% + full_join( + regional_rd_personnel, + by = c("prof_pos", "sex", "sectperf", "unit", "geo", "time") + ) %>% + mutate(type = case_when( + is.na(values_2016) & is.na(values) ~ "missing", + is.na(values) ~ "after", + TRUE ~ "before" + )) +``` + +And let's place it now on the map: + +```{r, choro2} +map_nuts_2 %>% + left_join(regional_rd_personnel_recoded, by = "geo") %>% + # remove completely missing cases + filter(!is.na(time)) %>% + ggplot() + + geom_sf(aes(fill = type), + color = "dim grey", linewidth = .1 + ) + + scale_fill_manual(values = c("#FAE000", "#007CBB", "grey70")) + + guides(fill = guide_legend(reverse = T, title = NULL)) + + facet_wrap(facets = "time") + + labs( + title = "R&D Personnel & Researchers", + subtitle = "In all sectors, both sexes by NUTS 2 regions", + caption = "\ua9 EuroGeographics for the administrative boundaries + \ua9 Daniel Antal, rOpenGov", + fill = NULL + ) + + theme_light() + + theme(legend.position = c(.93, .7)) + + coord_sf( + xlim = c(2377294, 7453440), + ylim = c(1313597, 5628510), + crs = 3035 + ) +``` + +## Conclusion + +We did improve our dataset, and this improvement would not have worked with +traditional imputation techniques very well. For example, replacing the missing +French data with the median value of Europe would have created a huge bias in +our dataset. + +This example is a simplification. There are many territorial typologies in use +in Europe and globally, but the main takeaway is clear: sub-national boundaries +are changing very fast, and you must make sure that you join datasets, or data +with a map with the same boundary definitions. + +# Citations and related work + +### Citing the data sources + +Eurostat data: cite [Eurostat](https://ec.europa.eu/eurostat/). + +Administrative boundaries: cite +[EuroGeographics](https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units). + +### Citing the eurostat R package + +For main developers and contributors, see the [package +homepage](https://ropengov.github.io/eurostat). + +This work can be freely used, modified and distributed under the BSD-2-clause +(modified FreeBSD) license: + +```{r citation-eurostat, message=FALSE} +citation("eurostat") +``` + +### Citing the regions R package + +For main developer and contributors, see the +[package](https://regions.dataobservatory.eu/). + +This work can be freely used, modified and distributed under the GPL-3 license: + +```{r citation-regions, message=FALSE} +citation("regions") +``` + +### Contact + +For contact information, see the [package +homepage](https://ropengov.github.io/eurostat). + +# Version info + +This tutorial was created with + +```{r sessioninfo, message=FALSE} +sessioninfo::session_info() +``` diff --git a/vignettes/articles/maps.Rmd b/vignettes/articles/maps.Rmd new file mode 100644 index 00000000..11d6d80f --- /dev/null +++ b/vignettes/articles/maps.Rmd @@ -0,0 +1,222 @@ +--- +title: "Map examples for the eurostat R package" +date: "`r Sys.Date()`" +output: + html_document: + toc: yes +editor_options: + markdown: + wrap: 80 + canonical: true +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + tidy = "styler", + dev = "ragg_png", + warning = FALSE, + dpi = 300, + out.width = "100%" +) +``` + +```{r setup} +library(eurostat) +``` + +# R Tools for Eurostat Open Data: maps + +This [rOpenGov](http://ropengov.github.io) R package provides tools to access +[Eurostat database](http://ec.europa.eu/eurostat/data/database), which you can +also browse on-line for the data sets and documentation. For contact information +and source code, see the [package website](http://ropengov.github.io/eurostat/). + +See the vignette of eurostat (`vignette(package = "eurostat")`) for installation +and basic use. + +## Maps + +> NOTE: we recommend to check also the `giscoR` package +> (). This is another API package that +> provides R tools for Eurostat geographic data to support geospatial analysis +> and visualization. + +### Disposable income of private households by NUTS 2 regions at 1:60mln resolution using tmap + +The mapping examples below use [`tmap`](https://github.com/mtennekes/tmap) +package. + +```{r maps1-1} +library(dplyr) +library(eurostat) +library(sf) +library(tmap) + +# Download attribute data from Eurostat +sp_data <- eurostat::get_eurostat("tgs00026", time_format = "raw") %>% + # subset to have only a single row per geo + filter(TIME_PERIOD == 2016, nchar(geo) == 4) %>% + # categorise + mutate(income = cut_to_classes(values, n = 5)) + +# Download geospatial data from GISCO +geodata <- get_eurostat_geospatial(nuts_level = 2, year = 2016) + +# merge with attribute data with geodata +map_data <- inner_join(geodata, sp_data, by = "geo") +``` + +Construct the map + +```{r map1ex} +# Create and plot the map +map1 <- tm_shape(geodata, + projection = "EPSG:3035", + xlim = c(2400000, 7800000), + ylim = c(1320000, 5650000) +) + + tm_fill("lightgrey") + + tm_shape(map_data) + + tm_polygons("income", + title = "Disposable household\nincomes in 2016", + palette = "Oranges" + ) + +print(map1) +``` + +Interactive maps can be generated as well + +```{r maps1-inter, out.height="420px"} +# Interactive +tmap_mode("view") +map1 +``` + +```{r maps1-2} +# Set the mode back to normal plotting +tmap_mode("plot") +print(map1) +``` + +### Disposable income of private households by NUTS 2 regions in Poland with labels at 1:1mln resolution using tmap + +```{r maps2} +library(eurostat) +library(dplyr) +library(sf) + +# Downloading and manipulating the tabular data +print("Let us focus on year 2016 and NUTS-3 level") +euro_sf2 <- get_eurostat("tgs00026", + time_format = "raw", + filter = list(time = "2016") +) %>% + # Subset to NUTS-3 level + dplyr::filter(grepl("PL", geo)) %>% + # label the single geo column + mutate( + label = paste0(label_eurostat(.)[["geo"]], "\n", values, "€"), + income = cut_to_classes(values) + ) + +print("Download geospatial data from GISCO") +geodata <- get_eurostat_geospatial( + resolution = "01", nuts_level = 2, + year = 2016, country = "PL" +) + +# Merge with attribute data with geodata +map_data <- inner_join(geodata, euro_sf2, by = "geo") + +# plot map +library(tmap) + +map2 <- tm_shape(geodata) + + tm_fill("lightgrey") + + tm_shape(map_data, is.master = TRUE) + + tm_polygons("income", + title = "Disposable household incomes in 2014", + palette = "Oranges", border.col = "white" + ) + + tm_text("NUTS_NAME", just = "center") + + tm_scale_bar() + + tm_layout(legend.outside = TRUE) +map2 +``` + +### Disposable income of private households by NUTS 2 regions at 1:10mln resolution using ggplot2 + +```{r maps4} +# Disposable income of private households by NUTS 2 regions at 1:1mln res +library(eurostat) +library(dplyr) +library(ggplot2) +data_eurostat <- get_eurostat("tgs00026", time_format = "raw") %>% + filter(TIME_PERIOD == 2018, nchar(geo) == 4) %>% + # classifying the values the variable + dplyr::mutate(cat = cut_to_classes(values)) + +# Download geospatial data from GISCO +data_geo <- get_eurostat_geospatial( + resolution = "01", nuts_level = "2", + year = 2016 +) + +# merge with attribute data with geodata +data <- left_join(data_geo, data_eurostat, by = "geo") + +ggplot(data) + + # Base layer + geom_sf(fill = "lightgrey", color = "lightgrey") + + # Choropleth layer + geom_sf(aes(fill = cat), color = "lightgrey", linewidth = 0.1, na.rm = TRUE) + + scale_fill_brewer(palette = "Oranges", na.translate = FALSE) + + guides(fill = guide_legend(reverse = TRUE, title = "euro")) + + labs( + title = "Disposable household income in 2018", + caption = "© EuroGeographics for the administrative boundaries + Map produced in R with data from Eurostat-package http://ropengov.github.io/eurostat" + ) + + theme_light() + + coord_sf( + xlim = c(2377294, 7453440), + ylim = c(1313597, 5628510), + crs = 3035 + ) +``` + +# Citations and related work + +### Citing the data sources + +Eurostat data: cite [Eurostat](http://ec.europa.eu/eurostat/). + +Administrative boundaries: cite EuroGeographics + +### Citing the eurostat R package + +For main developers and contributors, see the [package +homepage](http://ropengov.github.io/eurostat). + +This work can be freely used, modified and distributed under the BSD-2-clause +(modified FreeBSD) license: + +```{r citation, message=FALSE} +citation("eurostat") +``` + +### Contact + +For contact information, see the [package +homepage](http://ropengov.github.io/eurostat). + +# Version info + +This tutorial was created with + +```{r sessioninfo, message=FALSE} +sessioninfo::session_info() +``` diff --git a/vignettes/articles/publications.Rmd b/vignettes/articles/publications.Rmd new file mode 100644 index 00000000..09686a77 --- /dev/null +++ b/vignettes/articles/publications.Rmd @@ -0,0 +1,35 @@ +--- +title: "Publications using the eurostat R package" +date: "2022-02-07" +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +You can send us a note via the [issue tracker](https://github.com/rOpenGov/eurostat/issues) if you like to add a publication using the eurostat R package on this list: + +- Retrieval and analysis of Eurostat open data with the eurostat package. R Journal 2017. ([manuscript](https://journal.r-project.org/archive/2017/RJ-2017-019/index.html); [source code](https://github.com/rOpenGov/eurostat/tree/master/inst/extras/2017_RJournal_manuscript)). + +External publications using the eurostat package: + +- Jungnickel, T., von Ute, J., Engeli, S. & Albrecht, U-V. (2022). Exploring the weight bias of professionals working in the field of obesity with a mobile IAT: a pilot study. *Therapeutic Advances in Endocrinology and Metabolism*, 13. + +- Balogh, A., Harman, A. & Kreuter, F. (2022). Real-time analysis of predictors of COVID-19 infection spread in the countries of the European Union: User's Manual to the Covid-Predictor-Tracker. URL: + +- Fusco, E., Maggi, B. & Rizzuto, L. (2022). Alternative indicators for the evaluation of renewables in Europe: An efficiency approach. *Renewable Energy*, 190, 48-65. + +- Hashim, D., Carioli, G., Malvezzi, M., Bertuccio, P., Waxman, S., Negri, E., La Vecchia, C., & Boffetta, P. (2020). Cancer mortality in the oldest old: a global overview. *Aging*, 12(17), 16744--16758. + +- Patias, N. (2020). Exploring long-term youth unemployment in Europe using sequence analysis: A reproducible notebook approach. *REGION*, 6(3), 53-69. + +- Belmonte, M., McMahon, S., Scipioni, M. & Tintori, G. (2019). What relationship is there between irregular and regular migration? An exploratory study of EU residence permits, asylum applications, and orders to leave. JRC, Ispra, JRC116674. + +- Galjak, M. (2018). East-West Demographic Divide in the EU: A Regional Overview. Stanovništvo, 56(2), 1-21. + +- Sehor, T. (2018). Terorismus a migrace. Diplomová práce. Brno: Masarykova Univerzita, Ekonomicko-správní fakulta. + +- Kenett, R. S., & Shmueli, G. (2016). Information quality: The potential of data and analytics to generate knowledge. John Wiley & Sons. diff --git a/vignettes/blogposts.Rmd b/vignettes/blogposts.Rmd deleted file mode 100755 index 1cfcb028..00000000 --- a/vignettes/blogposts.Rmd +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: "Blog posts" -date: "`r Sys.Date()`" -output: - rmarkdown::html_vignette: - toc: true -vignette: > - %\VignetteIndexEntry{eurostat blog posts} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteDepends{Cairo} - %\VignetteEncoding{UTF-8} - \usepackage[utf8]{inputenc} ---- - - - * [Package release](https://rpubs.com/muuankarski/27120) - * [Interactive widgets](https://www.mytinyshinys.com/2017/07/11/eurostat/) by Andrew Clark - * [Case studies](http://ropengov.github.io/r/2015/05/01/eurostat-package-examples/) - \ No newline at end of file diff --git a/vignettes/cheatsheet.Rmd b/vignettes/cheatsheet.Rmd deleted file mode 100755 index 477b8b25..00000000 --- a/vignettes/cheatsheet.Rmd +++ /dev/null @@ -1,22 +0,0 @@ ---- -title: "Cheat sheet: eurostat R package" -date: "`r Sys.Date()`" -output: - rmarkdown::html_vignette: - toc: true -vignette: > - %\VignetteIndexEntry{eurostat R package: cheat sheet} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteDepends{Cairo} - %\VignetteEncoding{UTF-8} - \usepackage[utf8]{inputenc} ---- - - * [eurostat R package: cheat sheet (RStudio release version)](https://github.com/rstudio/cheatsheets/raw/master/eurostat.pdf) - - * [eurostat R package: cheat sheet (rOpenGov development version)](https://github.com/rOpenGov/eurostat/blob/master/inst/extras/cheatsheet/eurostat_cheatsheet.pdf) - - - - - diff --git a/vignettes/eurostat_tutorial.Rmd b/vignettes/eurostat_tutorial.Rmd deleted file mode 100755 index 7e05f17c..00000000 --- a/vignettes/eurostat_tutorial.Rmd +++ /dev/null @@ -1,509 +0,0 @@ ---- -title: "Tutorial (vignette) for the eurostat R package" -date: "`r Sys.Date()`" -output: - rmarkdown::html_vignette: - toc: true -vignette: > - %\VignetteIndexEntry{eurostat tutorial} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteDepends{Cairo} - %\VignetteEncoding{UTF-8} - \usepackage[utf8]{inputenc} ---- - -# R Tools for Eurostat Open Data - -This [rOpenGov](http://ropengov.github.io) R package provides tools to access [Eurostat database](http://ec.europa.eu/eurostat/data/database), which you can also browse on-line for the data sets and documentation. For contact information and source code, see the [package website](http://ropengov.github.io/eurostat/). - - -```{r setup, include=FALSE} -# Global options -library(knitr) -opts_chunk$set(fig.path="fig/") -``` - -# Installation - -Release version [(CRAN)](https://cran.r-project.org/web/packages/eurostat/index.html): - -```{r install, eval=FALSE} -install.packages("eurostat") -``` - -Development version [(Github)](https://github.com/rOpenGov/eurostat): - -```{r install2, eval=FALSE} -library(devtools) -install_github("ropengov/eurostat") -``` - -```{r, echo=FALSE} -library(eurostat) -``` - - -Overall, the eurostat package includes the following functions: - -```{r, echo=FALSE,comment=NA} -cat(paste0(library(help = "eurostat")$info[[2]], collapse = "\n")) -``` - - -# Finding data - -Function `get_eurostat_toc()` downloads a table of contents of eurostat datasets. The values in column 'code' should be used to download a selected dataset. - -```{r get_eurostat_toc, warning=FALSE, message=FALSE} -# Load the package -library(eurostat) -library(rvest) - -# Get Eurostat data listing -toc <- get_eurostat_toc() - -# Check the first items -library(knitr) -kable(head(toc)) -``` - -With `search_eurostat()` you can search the table of contents for particular patterns, e.g. all datasets related to *passenger transport*. The kable function to produces nice markdown output. Note that with the `type` argument of this function you could restrict the search to for instance datasets or tables. - -```{r search_eurostat, warning=FALSE, message=FALSE} -# info about passengers -kable(head(search_eurostat("passenger transport"))) -``` - -Codes for the dataset can be searched also from the [Eurostat -database](http://ec.europa.eu/eurostat/data/database). The Eurostat -database gives codes in the Data Navigation Tree after every dataset -in parenthesis. - -# Downloading data - -The package supports two of the Eurostats download methods: the bulk download -facility and the Web Services' JSON API. The bulk download facility is the -fastest method to download whole datasets. It is also often the only way as -the JSON API has limitation of maximum 50 sub-indicators at a time and -whole datasets usually exceeds that. To download only a small section of the -dataset the JSON API is faster, as it allows to make a data selection -before downloading. - -A user does not usually have to bother with methods, as both are used via main -function `get_eurostat()`. If only the table id is given, the whole table is -downloaded from the bulk download facility. If also filters are defined -the JSON API is used. - -Here an example of indicator 'Modal split of passenger transport'. This is the percentage share of each mode of transport in total inland transport, expressed in passenger-kilometres (pkm) based on transport by passenger cars, buses and coaches, and trains. All data should be based on movements on national territory, regardless of the nationality of the vehicle. However, the data collection is not harmonized at the EU level. - -Pick and print the id of the data set to download: -```{r get_id, warning=FALSE, message=FALSE, results='asis'} -# For the original data, see -# http://ec.europa.eu/eurostat/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tsdtr210 -id <- search_eurostat("Modal split of passenger transport", - type = "table")$code[1] -print(id) -``` - -Get the whole corresponding table. As the table is annual data, it is more -convient to use a numeric time variable than use the default date format: - -```{r get_eurostat, warning=FALSE, message=FALSE, results='asis'} -dat <- get_eurostat(id, time_format = "num") -``` - -Investigate the structure of the downloaded data set: -```{r str_dat, warning=FALSE, message=FALSE} -str(dat) -``` - -```{r head_dat, warning=FALSE, message=FALSE, results='asis'} -kable(head(dat)) -``` - -Or you can get only a part of the dataset by defining `filters` argument. It -should be named list, where names corresponds to variable names (lower case) and -values are vectors of codes corresponding desidered series (upper case). For -time variable, in addition to a `time`, also a `sinceTimePeriod` -and a `lastTimePeriod` can be used. - -```{r get_eurostat_json, warning=FALSE, message=FALSE, results='asis', eval=FALSE} -dat2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), lastTimePeriod=1), time_format = "num") -kable(dat2) -``` - -## Replacing codes with labels - -By default variables are returned as Eurostat codes, but to get human-readable -labels instead, use a `type = "label"` argument. - -```{r json_labels, warning=FALSE, message=FALSE, results='asis', eval=FALSE} -datl2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), - lastTimePeriod = 1), - type = "label", time_format = "num") -kable(head(datl2)) -``` - -Eurostat codes in the downloaded data set can be replaced with -human-readable labels from the Eurostat dictionaries with the -`label_eurostat()` function. - -```{r labels, warning=FALSE, message=FALSE, results='asis'} -datl <- label_eurostat(dat) -kable(head(datl)) -``` - -The `label_eurostat()` allows conversion of individual variable -vectors or variable names as well. - -```{r name_labels, eval = FALSE} -label_eurostat_vars(names(datl)) -``` - - -Vehicle information has 3 levels. You can check them now with: - -```{r vehicle_levels, eval = FALSE} -levels(datl$vehicle) -``` - - - -# Selecting and modifying data - -## EFTA, Eurozone, EU and EU candidate countries - -To facilitate smooth visualization of standard European geographic areas, the package provides ready-made lists of the country codes used in the eurostat database for EFTA (efta\_countries), Euro area (ea\_countries), EU (eu\_countries) and EU candidate countries (eu\_candidate\_countries). These can be used to select specific groups of countries for closer investigation. For conversions with other standard country coding systems, see the [countrycode](...) R package. To retrieve the country code list for EFTA, for instance, use: - -```{r, echo=TRUE, eval=TRUE} -data(efta_countries) -kable(efta_countries) -``` - - -## EU data from 2012 in all vehicles: - -```{r eu_12} -dat_eu12 <- subset(datl, geo == "European Union (current composition)" & time == 2012) -kable(dat_eu12, row.names = FALSE) -``` - -## EU data from 2000 - 2012 with vehicle types as variables: - -Reshaping the data is best done with `spread()` in `tidyr`. -```{r eu_vehicles_table} -library("tidyr") -dat_eu_0012 <- subset(dat, geo == "EU28" & time %in% 2000:2012) -dat_eu_0012_wide <- spread(dat_eu_0012, vehicle, values) -kable(subset(dat_eu_0012_wide, select = -geo), row.names = FALSE) -``` - -## Train passengers for selected EU countries in 2000 - 2012 - -```{r trains_table} -dat_trains <- subset(datl, geo %in% c("Austria", "Belgium", "Finland", "Sweden") - & time %in% 2000:2012 - & vehicle == "Trains") - -dat_trains_wide <- spread(dat_trains, geo, values) -kable(subset(dat_trains_wide, select = -vehicle), row.names = FALSE) -``` - - - -# Visualization - -Visualizing train passenger data with `ggplot2`: - -```{r trains_plot, fig.width=6, fig.height=3} -library(ggplot2) -p <- ggplot(dat_trains, aes(x = time, y = values, colour = geo)) -p <- p + geom_line() -print(p) -``` - -**Triangle plot** - -Triangle plot is handy for visualizing data sets with three variables. - -```{r plotGallery, warning=FALSE, message=FALSE, fig.width=6, fig.height=6} -library(tidyr) -library(plotrix) -library(eurostat) -library(dplyr) -library(tidyr) - -# All sources of renewable energy are to be grouped into three sets - dict <- c("Solid biofuels (excluding charcoal)" = "Biofuels", - "Biogasoline" = "Biofuels", - "Other liquid biofuels" = "Biofuels", - "Biodiesels" = "Biofuels", - "Biogas" = "Biofuels", - "Hydro power" = "Hydro power", - "Tide, Wave and Ocean" = "Hydro power", - "Solar thermal" = "Wind, solar, waste and Other", - "Geothermal Energy" = "Wind, solar, waste and Other", - "Solar photovoltaic" = "Wind, solar, waste and Other", - "Municipal waste (renewable)" = "Wind, solar, waste and Other", - "Wind power" = "Wind, solar, waste and Other", - "Bio jet kerosene" = "Wind, solar, waste and Other") -# Some cleaning of the data is required - energy3 <- get_eurostat("ten00081") %>% - label_eurostat(dat) %>% - filter(time == "2013-01-01", - product != "Renewable energies") %>% - mutate(nproduct = dict[as.character(product)], # just three categories - geo = gsub(geo, pattern=" \\(.*", replacement="")) %>% - select(nproduct, geo, values) %>% - group_by(nproduct, geo) %>% - summarise(svalue = sum(values)) %>% - group_by(geo) %>% - mutate(tvalue = sum(svalue), - svalue = svalue/sum(svalue)) %>% - filter(tvalue > 1000) %>% # only large countries - spread(nproduct, svalue) - -# Triangle plot - par(cex=0.75, mar=c(0,0,0,0)) - positions <- plotrix::triax.plot(as.matrix(energy3[, c(3,5,4)]), - show.grid = TRUE, - label.points= FALSE, point.labels = energy3$geo, - col.axis="gray50", col.grid="gray90", - pch = 19, cex.axis=0.8, cex.ticks=0.7, col="grey50") - - # Larger labels - ind <- which(energy3$geo %in% c("Norway", "Iceland","Denmark","Estonia", "Turkey", "Italy", "Finland")) - df <- data.frame(positions$xypos, geo = energy3$geo) - points(df$x[ind], df$y[ind], cex=2, col="red", pch=19) - text(df$x[ind], df$y[ind], df$geo[ind], adj = c(0.5,-1), cex=1.5) -``` - - - -## Maps - -### Disposable income of private households by NUTS 2 regions at 1:60mln resolution using tmap - -The mapping examples below use [`tmap`](https://github.com/mtennekes/tmap) package. - - -```{r maps1-1, eval=TRUE, fig.width=8, fig.height=8} -library(dplyr) -library(eurostat) -library(sf) -library(tmap) - -# Download attribute data from Eurostat -sp_data <- eurostat::get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subset to have only a single row per geo - dplyr::filter(time == 2010, nchar(geo) == 4) %>% - # categorise - dplyr::mutate(income = cut_to_classes(values, n = 5)) - -# Download geospatial data from GISCO -geodata <- get_eurostat_geospatial(output_class = "sf", resolution = "60", nuts_level = 2, year = 2013) - -# merge with attribute data with geodata -map_data <- inner_join(geodata, sp_data) -``` - - -Construct the map - -```{r map1ex, eval=TRUE, warning=FALSE} -map1 <- tmap::tm_shape(geodata) + - tmap::tm_fill("lightgrey") + - tmap::tm_shape(map_data) + - tmap::tm_grid() + - tmap::tm_polygons("income", title = "Disposable household\nincomes in 2010", - palette = "Oranges") -print(map1) -``` - -Interactive maps can be generated as well - -```{r maps1-2, eval=FALSE, fig.width=8, fig.height=8} -# Interactive -tmap_mode("view") -map1 - -# Set the mode back to normal plotting -tmap_mode("plot") -print(map1) -``` - -### Disposable income of private households by NUTS 2 regions in Poland with labels at 1:1mln resolution using tmap - -```{r maps2, fig.width=8, fig.height=8, warning=FALSE} -library(eurostat) -library(dplyr) -library(sf) -library(RColorBrewer) - -# Downloading and manipulating the tabular data -print("Let us focus on year 2014 and NUTS-3 level") -euro_sf2 <- get_eurostat("tgs00026", time_format = "raw", - stringsAsFactors = FALSE, - filter = list(time = "2014")) %>% - - # Subset to NUTS-3 level - dplyr::filter(grepl("PL",geo)) %>% - # label the single geo column - mutate(label = paste0(label_eurostat(.)[["geo"]], "\n", values, "€"), - income = cut_to_classes(values)) - -print("Download geospatial data from GISCO") -geodata <- get_eurostat_geospatial(output_class = "sf", resolution = "60", nuts_level = 2, year = 2013) - -# Merge with attribute data with geodata -map_data <- inner_join(geodata, euro_sf2) - -# plot map -map2 <- tm_shape(geodata) + - tm_fill("lightgrey") + - tm_shape(map_data, is.master = TRUE) + - tm_polygons("income", title = "Disposable household incomes in 2014", - palette = "Oranges", border.col = "white") + - tm_text("NUTS_NAME", just = "center") + - tm_scale_bar() -map2 -``` - -### Disposable income of private households by NUTS 2 regions at 1:10mln resolution using spplot - -```{r maps3, fig.width=8, fig.height=8, dev='CairoPNG'} -library(sp) -library(eurostat) -library(dplyr) -library(RColorBrewer) -dat <- get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subsetting to year 2014 and NUTS-2 level - dplyr::filter(time == 2014, nchar(geo) == 4) %>% - # classifying the values the variable - dplyr::mutate(cat = cut_to_classes(values)) - -# Download geospatial data from GISCO -geodata <- get_eurostat_geospatial(output_class = "spdf", resolution = "10", nuts_level = 2, year = 2013) - -# merge with attribute data with geodata -geodata@data <- left_join(geodata@data, dat) - -# plot map -sp::spplot(obj = geodata, "cat", main = "Disposable household income", - xlim = c(-22,34), ylim = c(35,70), - col.regions = c("dim grey", brewer.pal(n = 5, name = "Oranges")), - col = "white", usePolypath = FALSE) -``` - -### Disposable income of private households by NUTS 2 regions at 1:60mln resolution using ggplot2 - -Meanwhile the CRAN version of `ggplot2` is lacking support for simple features, you can plot maps with `ggplot2` by downloading geospatial data as `data.frame` with `output_class` argument set as `df`. - -```{r maps4, fig.width=8, fig.height=8, dev='CairoPNG'} -library(eurostat) -library(dplyr) -library(ggplot2) -dat <- get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subsetting to year 2014 and NUTS-2 level - dplyr::filter(time == 2014, nchar(geo) == 4) %>% - # classifying the values the variable - dplyr::mutate(cat = cut_to_classes(values)) - -# Download geospatial data from GISCO -geodata <- get_eurostat_geospatial(resolution = "60", nuts_level = "2", year = 2013) - -# merge with attribute data with geodata -map_data <- inner_join(geodata, dat) - -ggplot(data=map_data) + geom_sf(aes(fill=cat),color="dim grey", size=.1) + - scale_fill_brewer(palette = "Oranges") + - guides(fill = guide_legend(reverse=T, title = "euro")) + - labs(title="Disposable household income in 2014", - caption="(C) EuroGeographics for the administrative boundaries - Map produced in R with a help from Eurostat-package ") + - theme_light() + theme(legend.position=c(.8,.8)) + - coord_sf(xlim=c(-12,44), ylim=c(35,70)) - -``` - - -## SDMX - -Eurostat data is available also in the SDMX format. The eurostat R package does not provide custom tools for this but the generic [rsdmx](https://cran.r-project.org/web/packages/rsdmx/index.html) and [rjsdmx](https://github.com/amattioc/SDMX/wiki) R packages can be used to access data in that format when necessary: - -```{r rsdmx, fig.width=8, fig.height=8, dev='CairoPNG'} -library(rsdmx) - -# Data set URL -url <- "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/data/cdh_e_fos/..PC.FOS1.BE/?startperiod=2005&endPeriod=2011" - -# Read the data from eurostat -d <- readSDMX(url) - -# Convert to data frame and show the first entries -df <- as.data.frame(d) - -kable(head(df)) -``` - - - - -# Further examples - -For further examples, see the [package homepage](http://ropengov.github.io/eurostat/articles/index.html). - - -# Citations and related work - -### Citing the data sources - -Eurostat data: cite [Eurostat](http://ec.europa.eu/eurostat/). - -Administrative boundaries: cite EuroGeographics - - -### Citing the eurostat R package - -For main developers and contributors, see the [package homepage](http://ropengov.github.io/eurostat). - -This work can be freely used, modified and distributed under the -BSD-2-clause (modified FreeBSD) license: - -```{r citation, message=FALSE, eval=TRUE, echo=TRUE} -citation("eurostat") -``` - -### Related work - -This [rOpenGov](http://ropengov.github.io) R package is based on the -earlier CRAN packages -[statfi](https://cran.r-project.org/package=statfi) and -[smarterpoland](https://cran.r-project.org/package=SmarterPoland). - -The independent [reurostat](https://github.com/Tungurahua/reurostat) -package develops related Eurostat tools but seems to be in an -experimental stage at the time of writing this tutorial. - -The more generic [quandl](https://cran.r-project.org/package=quandl), -[datamart](https://cran.r-project.org/package=datamart), -[rsdmx](https://cran.r-project.org/package=rsdmx), and -[pdfetch](https://cran.r-project.org/package=pdfetch) packages may -provide access to some versions of eurostat data but these packages -are more generic and hence, in contrast to the eurostat R package, -lack tools that are specifically customized to facilitate eurostat -analysis. - - -### Contact - -For contact information, see the [package homepage](http://ropengov.github.io/eurostat). - - -# Version info - -This tutorial was created with - -```{r sessioninfo, message=FALSE, warning=FALSE} -sessionInfo() -``` diff --git a/vignettes/eurostat_tutorial.md b/vignettes/eurostat_tutorial.md deleted file mode 100755 index dfa93298..00000000 --- a/vignettes/eurostat_tutorial.md +++ /dev/null @@ -1,1371 +0,0 @@ -R Tools for Eurostat Open Data -============================== - -This [rOpenGov](http://ropengov.github.io) R package provides tools to -access [Eurostat database](http://ec.europa.eu/eurostat/data/database), -which you can also browse on-line for the data sets and documentation. -For contact information and source code, see the [package -website](http://ropengov.github.io/eurostat/). - -Installation -============ - -Release version -[(CRAN)](https://cran.r-project.org/web/packages/eurostat/index.html): - - install.packages("eurostat") - -Development version [(Github)](https://github.com/rOpenGov/eurostat): - - library(devtools) - install_github("ropengov/eurostat") - -Overall, the eurostat package includes the following functions: - -Finding data -============ - -Function `get_eurostat_toc()` downloads a table of contents of eurostat -datasets. The values in column 'code' should be used to download a -selected dataset. - - # Load the package - library(eurostat) - library(rvest) - - # Get Eurostat data listing - toc <- get_eurostat_toc() - - # Check the first items - library(knitr) - kable(head(toc)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    titlecodetypelast update of datalast table structure changedata startdata endvalues
    Database by themesdatafolderNANANANANA
    General and regional statisticsgeneralfolderNANANANANA
    European and national indicators for short-term analysiseuroindfolderNANANANANA
    Business and consumer surveys (source: DG ECFIN)ei_bcsfolderNANANANANA
    Consumer surveys (source: DG ECFIN)ei_bcs_csfolderNANANANANA
    Consumers - monthly dataei_bsco_mdataset27.09.201827.09.20181980M012018M09NA
    - -With `search_eurostat()` you can search the table of contents for -particular patterns, e.g. all datasets related to *passenger transport*. -The kable function to produces nice markdown output. Note that with the -`type` argument of this function you could restrict the search to for -instance datasets or tables. - - # info about passengers - kable(head(search_eurostat("passenger transport"))) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    titlecodetypelast update of datalast table structure changedata startdata endvalues
    Volume of passenger transport relative to GDPtran_hv_pstradataset30.07.201830.07.201820002016NA
    Modal split of passenger transporttran_hv_psmoddataset27.08.201827.08.201819902016NA
    Railway transport - total annual passenger transport (1 000 pass., million pkm)rail_pa_totaldataset20.09.201807.09.201820042016NA
    Railway transport - passenger transport by type of transport (detailed reporting only) (1 000 pass.)rail_pa_typepasdataset20.09.201831.08.201720042016NA
    Railway transport - passenger transport by type of transport (detailed reporting only) (million pkm)rail_pa_typepkmdataset20.09.201831.08.201720042016NA
    International railway passenger transport from the reporting country to the country of disembarkation (1 000 passengers)rail_pa_intgongdataset20.09.201817.07.201820042017NA
    - -Codes for the dataset can be searched also from the [Eurostat -database](http://ec.europa.eu/eurostat/data/database). The Eurostat -database gives codes in the Data Navigation Tree after every dataset in -parenthesis. - -Downloading data -================ - -The package supports two of the Eurostats download methods: the bulk -download facility and the Web Services' JSON API. The bulk download -facility is the fastest method to download whole datasets. It is also -often the only way as the JSON API has limitation of maximum 50 -sub-indicators at a time and whole datasets usually exceeds that. To -download only a small section of the dataset the JSON API is faster, as -it allows to make a data selection before downloading. - -A user does not usually have to bother with methods, as both are used -via main function `get_eurostat()`. If only the table id is given, the -whole table is downloaded from the bulk download facility. If also -filters are defined the JSON API is used. - -Here an example of indicator 'Modal split of passenger transport'. This -is the percentage share of each mode of transport in total inland -transport, expressed in passenger-kilometres (pkm) based on transport by -passenger cars, buses and coaches, and trains. All data should be based -on movements on national territory, regardless of the nationality of the -vehicle. However, the data collection is not harmonized at the EU level. - -Pick and print the id of the data set to download: - - # For the original data, see - # http://ec.europa.eu/eurostat/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tsdtr210 - id <- search_eurostat("Modal split of passenger transport", - type = "table")$code[1] - print(id) - -\[1\] "t2020\_rk310" - -Get the whole corresponding table. As the table is annual data, it is -more convient to use a numeric time variable than use the default date -format: - - dat <- get_eurostat(id, time_format = "num") - -Investigate the structure of the downloaded data set: - - str(dat) - - ## Classes 'tbl_df', 'tbl' and 'data.frame': 2536 obs. of 5 variables: - ## $ unit : Factor w/ 1 level "PC": 1 1 1 1 1 1 1 1 1 1 ... - ## $ vehicle: Factor w/ 3 levels "BUS_TOT","CAR",..: 1 1 1 1 1 1 1 1 1 1 ... - ## $ geo : Factor w/ 35 levels "AT","BE","CH",..: 1 2 3 4 5 6 7 8 9 10 ... - ## $ time : num 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ... - ## $ values : num 11 10.6 3.7 9.1 11.3 32.4 14.9 13.5 6 24.8 ... - - kable(head(dat)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    unitvehiclegeotimevalues
    PCBUS_TOTAT199011.0
    PCBUS_TOTBE199010.6
    PCBUS_TOTCH19903.7
    PCBUS_TOTDE19909.1
    PCBUS_TOTDK199011.3
    PCBUS_TOTEL199032.4
    - -Or you can get only a part of the dataset by defining `filters` -argument. It should be named list, where names corresponds to variable -names (lower case) and values are vectors of codes corresponding -desidered series (upper case). For time variable, in addition to a -`time`, also a `sinceTimePeriod` and a `lastTimePeriod` can be used. - - dat2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), lastTimePeriod=1), time_format = "num") - kable(dat2) - -Replacing codes with labels ---------------------------- - -By default variables are returned as Eurostat codes, but to get -human-readable labels instead, use a `type = "label"` argument. - - datl2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), - lastTimePeriod = 1), - type = "label", time_format = "num") - kable(head(datl2)) - -Eurostat codes in the downloaded data set can be replaced with -human-readable labels from the Eurostat dictionaries with the -`label_eurostat()` function. - - datl <- label_eurostat(dat) - kable(head(datl)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    unitvehiclegeotimevalues
    PercentageMotor coaches, buses and trolley busesAustria199011.0
    PercentageMotor coaches, buses and trolley busesBelgium199010.6
    PercentageMotor coaches, buses and trolley busesSwitzerland19903.7
    PercentageMotor coaches, buses and trolley busesGermany (until 1990 former territory of the FRG)19909.1
    PercentageMotor coaches, buses and trolley busesDenmark199011.3
    PercentageMotor coaches, buses and trolley busesGreece199032.4
    - -The `label_eurostat()` allows conversion of individual variable vectors -or variable names as well. - - label_eurostat_vars(names(datl)) - -Vehicle information has 3 levels. You can check them now with: - - levels(datl$vehicle) - -Selecting and modifying data -============================ - -EFTA, Eurozone, EU and EU candidate countries ---------------------------------------------- - -To facilitate smooth visualization of standard European geographic -areas, the package provides ready-made lists of the country codes used -in the eurostat database for EFTA (efta\_countries), Euro area -(ea\_countries), EU (eu\_countries) and EU candidate countries -(eu\_candidate\_countries). These can be used to select specific groups -of countries for closer investigation. For conversions with other -standard country coding systems, see the [countrycode](...) R package. -To retrieve the country code list for EFTA, for instance, use: - - data(efta_countries) - kable(efta_countries) - - - - - - - - - - - - - - - - - - - - - - - - - - -
    codename
    ISIceland
    LILiechtenstein
    NONorway
    CHSwitzerland
    - -EU data from 2012 in all vehicles: ----------------------------------- - - dat_eu12 <- subset(datl, geo == "European Union (current composition)" & time == 2012) - kable(dat_eu12, row.names = FALSE) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    unitvehiclegeotimevalues
    PercentageMotor coaches, buses and trolley busesEuropean Union (current composition)20129.5
    PercentagePassenger carsEuropean Union (current composition)201282.9
    PercentageTrainsEuropean Union (current composition)20127.7
    - -EU data from 2000 - 2012 with vehicle types as variables: ---------------------------------------------------------- - -Reshaping the data is best done with `spread()` in `tidyr`. - - library("tidyr") - dat_eu_0012 <- subset(dat, geo == "EU28" & time %in% 2000:2012) - dat_eu_0012_wide <- spread(dat_eu_0012, vehicle, values) - kable(subset(dat_eu_0012_wide, select = -geo), row.names = FALSE) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    unittimeBUS_TOTCARTRN
    PC200010.482.47.2
    PC200110.282.77.1
    PC20029.983.36.8
    PC20039.983.56.7
    PC20049.883.46.8
    PC20059.883.36.9
    PC20069.783.27.1
    PC20079.883.17.1
    PC20089.982.87.4
    PC20099.383.67.1
    PC20109.483.47.2
    PC20119.483.27.4
    PC20129.582.97.7
    - -Train passengers for selected EU countries in 2000 - 2012 ---------------------------------------------------------- - - dat_trains <- subset(datl, geo %in% c("Austria", "Belgium", "Finland", "Sweden") - & time %in% 2000:2012 - & vehicle == "Trains") - - dat_trains_wide <- spread(dat_trains, geo, values) - kable(subset(dat_trains_wide, select = -vehicle), row.names = FALSE) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    unittimeAustriaBelgiumFinlandSweden
    Percentage20009.76.35.16.8
    Percentage20019.76.44.87.1
    Percentage20029.76.54.87.1
    Percentage20039.56.54.77.0
    Percentage20049.47.14.76.8
    Percentage20059.86.64.87.1
    Percentage200610.06.94.87.6
    Percentage200710.07.15.07.9
    Percentage200811.17.55.48.6
    Percentage200911.17.55.18.7
    Percentage201011.07.75.28.7
    Percentage201111.37.75.08.7
    Percentage201211.87.85.39.1
    - -Visualization -============= - -Visualizing train passenger data with `ggplot2`: - - library(ggplot2) - p <- ggplot(dat_trains, aes(x = time, y = values, colour = geo)) - p <- p + geom_line() - print(p) - -![](fig/trains_plot-1.png) - -**Triangle plot** - -Triangle plot is handy for visualizing data sets with three variables. - - library(tidyr) - library(plotrix) - library(eurostat) - library(dplyr) - library(tidyr) - - # All sources of renewable energy are to be grouped into three sets - dict <- c("Solid biofuels (excluding charcoal)" = "Biofuels", - "Biogasoline" = "Biofuels", - "Other liquid biofuels" = "Biofuels", - "Biodiesels" = "Biofuels", - "Biogas" = "Biofuels", - "Hydro power" = "Hydro power", - "Tide, Wave and Ocean" = "Hydro power", - "Solar thermal" = "Wind, solar, waste and Other", - "Geothermal Energy" = "Wind, solar, waste and Other", - "Solar photovoltaic" = "Wind, solar, waste and Other", - "Municipal waste (renewable)" = "Wind, solar, waste and Other", - "Wind power" = "Wind, solar, waste and Other", - "Bio jet kerosene" = "Wind, solar, waste and Other") - # Some cleaning of the data is required - energy3 <- get_eurostat("ten00081") %>% - label_eurostat(dat) %>% - filter(time == "2013-01-01", - product != "Renewable energies") %>% - mutate(nproduct = dict[as.character(product)], # just three categories - geo = gsub(geo, pattern=" \\(.*", replacement="")) %>% - select(nproduct, geo, values) %>% - group_by(nproduct, geo) %>% - summarise(svalue = sum(values)) %>% - group_by(geo) %>% - mutate(tvalue = sum(svalue), - svalue = svalue/sum(svalue)) %>% - filter(tvalue > 1000) %>% # only large countries - spread(nproduct, svalue) - - # Triangle plot - par(cex=0.75, mar=c(0,0,0,0)) - positions <- plotrix::triax.plot(as.matrix(energy3[, c(3,5,4)]), - show.grid = TRUE, - label.points= FALSE, point.labels = energy3$geo, - col.axis="gray50", col.grid="gray90", - pch = 19, cex.axis=0.8, cex.ticks=0.7, col="grey50") - - # Larger labels - ind <- which(energy3$geo %in% c("Norway", "Iceland","Denmark","Estonia", "Turkey", "Italy", "Finland")) - df <- data.frame(positions$xypos, geo = energy3$geo) - points(df$x[ind], df$y[ind], cex=2, col="red", pch=19) - text(df$x[ind], df$y[ind], df$geo[ind], adj = c(0.5,-1), cex=1.5) - -![](fig/plotGallery-1.png) - -Maps ----- - -### Disposable income of private households by NUTS 2 regions at 1:60mln resolution using tmap - -The mapping examples below use -[`tmap`](https://github.com/mtennekes/tmap) package. - - library(dplyr) - library(eurostat) - library(sf) - - ## Linking to GEOS 3.6.2, GDAL 2.2.3, proj.4 4.9.3 - - library(tmap) - - # Download attribute data from Eurostat - sp_data <- eurostat::get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subset to have only a single row per geo - dplyr::filter(time == 2010, nchar(geo) == 4) %>% - # categorise - dplyr::mutate(income = cut_to_classes(values, n = 5)) - - ## Table tgs00026 cached at /tmp/Rtmp4PAyLJ/eurostat/tgs00026_raw_code_FF.rds - - # Download geospatial data from GISCO - geodata <- get_eurostat_geospatial(output_class = "sf", resolution = "60", nuts_level = 2, year = 2013) - - ## - ## COPYRIGHT NOTICE - ## - ## When data downloaded from this page - ## - ## is used in any printed or electronic publication, - ## in addition to any other provisions - ## applicable to the whole Eurostat website, - ## data source will have to be acknowledged - ## in the legend of the map and - ## in the introductory page of the publication - ## with the following copyright notice: - ## - ## - EN: (C) EuroGeographics for the administrative boundaries - ## - FR: (C) EuroGeographics pour les limites administratives - ## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen - ## - ## For publications in languages other than - ## English, French or German, - ## the translation of the copyright notice - ## in the language of the publication shall be used. - ## - ## If you intend to use the data commercially, - ## please contact EuroGeographics for - ## information regarding their licence agreements. - ## - - ## No encoding supplied: defaulting to UTF-8. - - ## - ## # -------------------------- - ## HEADS UP!! - ## - ## Function now returns the data in 'sf'-class (simple features) - ## by default which is different - ## from previous behaviour's 'SpatialPolygonDataFrame'. - ## - ## If you prefer either 'SpatialPolygonDataFrame' or - ## fortified 'data_frame' (for ggplot2::geom_polygon), - ## please specify it explicitly to 'output_class'-argument! - ## - ## # -------------------------- - ## - - # merge with attribute data with geodata - map_data <- inner_join(geodata, sp_data) - - ## Joining, by = "geo" - -Construct the map - - map1 <- tmap::tm_shape(geodata) + - tmap::tm_fill("lightgrey") + - tmap::tm_shape(map_data) + - tmap::tm_grid() + - tmap::tm_polygons("income", title = "Disposable household\nincomes in 2010", - palette = "Oranges") - print(map1) - -![](fig/map1ex-1.png) - -Interactive maps can be generated as well - - # Interactive - tmap_mode("view") - map1 - - # Set the mode back to normal plotting - tmap_mode("plot") - print(map1) - -### Disposable income of private households by NUTS 2 regions in Poland with labels at 1:1mln resolution using tmap - - library(eurostat) - library(dplyr) - library(sf) - library(RColorBrewer) - - # Downloading and manipulating the tabular data - print("Let us focus on year 2014 and NUTS-3 level") - - ## [1] "Let us focus on year 2014 and NUTS-3 level" - - euro_sf2 <- get_eurostat("tgs00026", time_format = "raw", - stringsAsFactors = FALSE, - filter = list(time = "2014")) %>% - - # Subset to NUTS-3 level - dplyr::filter(grepl("PL",geo)) %>% - # label the single geo column - mutate(label = paste0(label_eurostat(.)[["geo"]], "\n", values, "€"), - income = cut_to_classes(values)) - - print("Download geospatial data from GISCO") - - ## [1] "Download geospatial data from GISCO" - - geodata <- get_eurostat_geospatial(output_class = "sf", resolution = "60", nuts_level = 2, year = 2013) - - ## - ## COPYRIGHT NOTICE - ## - ## When data downloaded from this page - ## - ## is used in any printed or electronic publication, - ## in addition to any other provisions - ## applicable to the whole Eurostat website, - ## data source will have to be acknowledged - ## in the legend of the map and - ## in the introductory page of the publication - ## with the following copyright notice: - ## - ## - EN: (C) EuroGeographics for the administrative boundaries - ## - FR: (C) EuroGeographics pour les limites administratives - ## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen - ## - ## For publications in languages other than - ## English, French or German, - ## the translation of the copyright notice - ## in the language of the publication shall be used. - ## - ## If you intend to use the data commercially, - ## please contact EuroGeographics for - ## information regarding their licence agreements. - ## - - ## No encoding supplied: defaulting to UTF-8. - - ## - ## # -------------------------- - ## HEADS UP!! - ## - ## Function now returns the data in 'sf'-class (simple features) - ## by default which is different - ## from previous behaviour's 'SpatialPolygonDataFrame'. - ## - ## If you prefer either 'SpatialPolygonDataFrame' or - ## fortified 'data_frame' (for ggplot2::geom_polygon), - ## please specify it explicitly to 'output_class'-argument! - ## - ## # -------------------------- - ## - - # Merge with attribute data with geodata - map_data <- inner_join(geodata, euro_sf2) - - ## Joining, by = "geo" - - # plot map - map2 <- tm_shape(geodata) + - tm_fill("lightgrey") + - tm_shape(map_data, is.master = TRUE) + - tm_polygons("income", title = "Disposable household incomes in 2014", - palette = "Oranges", border.col = "white") + - tm_text("NUTS_NAME", just = "center") + - tm_scale_bar() - map2 - -![](fig/maps2-1.png) - -### Disposable income of private households by NUTS 2 regions at 1:10mln resolution using spplot - - library(sp) - library(eurostat) - library(dplyr) - library(RColorBrewer) - dat <- get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subsetting to year 2014 and NUTS-2 level - dplyr::filter(time == 2014, nchar(geo) == 4) %>% - # classifying the values the variable - dplyr::mutate(cat = cut_to_classes(values)) - - ## Reading cache file /tmp/Rtmp4PAyLJ/eurostat/tgs00026_raw_code_FF.rds - - ## Table tgs00026 read from cache file: /tmp/Rtmp4PAyLJ/eurostat/tgs00026_raw_code_FF.rds - - # Download geospatial data from GISCO - geodata <- get_eurostat_geospatial(output_class = "spdf", resolution = "10", nuts_level = 2, year = 2013) - - ## - ## COPYRIGHT NOTICE - ## - ## When data downloaded from this page - ## - ## is used in any printed or electronic publication, - ## in addition to any other provisions - ## applicable to the whole Eurostat website, - ## data source will have to be acknowledged - ## in the legend of the map and - ## in the introductory page of the publication - ## with the following copyright notice: - ## - ## - EN: (C) EuroGeographics for the administrative boundaries - ## - FR: (C) EuroGeographics pour les limites administratives - ## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen - ## - ## For publications in languages other than - ## English, French or German, - ## the translation of the copyright notice - ## in the language of the publication shall be used. - ## - ## If you intend to use the data commercially, - ## please contact EuroGeographics for - ## information regarding their licence agreements. - ## - - ## No encoding supplied: defaulting to UTF-8. - - ## SpatialPolygonDataFrame at resolution 1: 10 cached at: /tmp/Rtmp4PAyLJ/eurostat/spdf1022013.RData - - ## - ## # -------------------------- - ## HEADS UP!! - ## - ## Function now returns the data in 'sf'-class (simple features) - ## by default which is different - ## from previous behaviour's 'SpatialPolygonDataFrame'. - ## - ## If you prefer either 'SpatialPolygonDataFrame' or - ## fortified 'data_frame' (for ggplot2::geom_polygon), - ## please specify it explicitly to 'output_class'-argument! - ## - ## # -------------------------- - ## - - # merge with attribute data with geodata - geodata@data <- left_join(geodata@data, dat) - - ## Joining, by = "geo" - - # plot map - sp::spplot(obj = geodata, "cat", main = "Disposable household income", - xlim = c(-22,34), ylim = c(35,70), - col.regions = c("dim grey", brewer.pal(n = 5, name = "Oranges")), - col = "white", usePolypath = FALSE) - -![](fig/maps3-1.png) - -### Disposable income of private households by NUTS 2 regions at 1:60mln resolution using ggplot2 - -Meanwhile the CRAN version of `ggplot2` is lacking support for simple -features, you can plot maps with `ggplot2` by downloading geospatial -data as `data.frame` with `output_class` argument set as `df`. - - library(eurostat) - library(dplyr) - library(ggplot2) - dat <- get_eurostat("tgs00026", time_format = "raw", stringsAsFactors = FALSE) %>% - # subsetting to year 2014 and NUTS-2 level - dplyr::filter(time == 2014, nchar(geo) == 4) %>% - # classifying the values the variable - dplyr::mutate(cat = cut_to_classes(values)) - - ## Reading cache file /tmp/Rtmp4PAyLJ/eurostat/tgs00026_raw_code_FF.rds - - ## Table tgs00026 read from cache file: /tmp/Rtmp4PAyLJ/eurostat/tgs00026_raw_code_FF.rds - - # Download geospatial data from GISCO - geodata <- get_eurostat_geospatial(resolution = "60", nuts_level = "2", year = 2013) - - ## - ## COPYRIGHT NOTICE - ## - ## When data downloaded from this page - ## - ## is used in any printed or electronic publication, - ## in addition to any other provisions - ## applicable to the whole Eurostat website, - ## data source will have to be acknowledged - ## in the legend of the map and - ## in the introductory page of the publication - ## with the following copyright notice: - ## - ## - EN: (C) EuroGeographics for the administrative boundaries - ## - FR: (C) EuroGeographics pour les limites administratives - ## - DE: (C) EuroGeographics bezuglich der Verwaltungsgrenzen - ## - ## For publications in languages other than - ## English, French or German, - ## the translation of the copyright notice - ## in the language of the publication shall be used. - ## - ## If you intend to use the data commercially, - ## please contact EuroGeographics for - ## information regarding their licence agreements. - ## - - ## No encoding supplied: defaulting to UTF-8. - - ## - ## # -------------------------- - ## HEADS UP!! - ## - ## Function now returns the data in 'sf'-class (simple features) - ## by default which is different - ## from previous behaviour's 'SpatialPolygonDataFrame'. - ## - ## If you prefer either 'SpatialPolygonDataFrame' or - ## fortified 'data_frame' (for ggplot2::geom_polygon), - ## please specify it explicitly to 'output_class'-argument! - ## - ## # -------------------------- - ## - - # merge with attribute data with geodata - map_data <- inner_join(geodata, dat) - - ## Joining, by = "geo" - - ggplot(data=map_data) + geom_sf(aes(fill=cat),color="dim grey", size=.1) + - scale_fill_brewer(palette = "Oranges") + - guides(fill = guide_legend(reverse=T, title = "euro")) + - labs(title="Disposable household income in 2014", - caption="(C) EuroGeographics for the administrative boundaries - Map produced in R with a help from Eurostat-package ") + - theme_light() + theme(legend.position=c(.8,.8)) + - coord_sf(xlim=c(-12,44), ylim=c(35,70)) - -![](fig/maps4-1.png) - -SDMX ----- - -Eurostat data is available also in the SDMX format. The eurostat R -package does not provide custom tools for this but the generic -[rsdmx](https://cran.r-project.org/web/packages/rsdmx/index.html) and -[rjsdmx](https://github.com/amattioc/SDMX/wiki) R packages can be used -to access data in that format when necessary: - - library(rsdmx) - - # Data set URL - url <- "http://ec.europa.eu/eurostat/SDMX/diss-web/rest/data/cdh_e_fos/..PC.FOS1.BE/?startperiod=2005&endPeriod=2011" - - # Read the data from eurostat - d <- readSDMX(url) - - # Convert to data frame and show the first entries - df <- as.data.frame(d) - - kable(head(df)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    UNITY_GRADFOS07GEOFREQobsTimeobsValueOBS_STATUS
    PCTOTALFOS1BEA2009NAna
    PCTOTALFOS1BEA2006NAna
    PCY_GE1990FOS1BEA200943.75NA
    PCY_GE1990FOS1BEA2006NAna
    - -Further examples -================ - -For further examples, see the [package -homepage](http://ropengov.github.io/eurostat/articles/index.html). - -Citations and related work -========================== - -### Citing the data sources - -Eurostat data: cite [Eurostat](http://ec.europa.eu/eurostat/). - -Administrative boundaries: cite EuroGeographics - -### Citing the eurostat R package - -For main developers and contributors, see the [package -homepage](http://ropengov.github.io/eurostat). - -This work can be freely used, modified and distributed under the -BSD-2-clause (modified FreeBSD) license: - - citation("eurostat") - - ## - ## Kindly cite the eurostat R package as follows: - ## - ## (C) Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek. - ## Retrieval and analysis of Eurostat open data with the eurostat - ## package. R Journal 9(1):385-392, 2017. Version 3.2.93 Package - ## URL: http://ropengov.github.io/eurostat Manuscript URL: - ## https://journal.r-project.org/archive/2017/RJ-2017-019/index.html - ## - ## A BibTeX entry for LaTeX users is - ## - ## @Misc{, - ## title = {eurostat R package}, - ## author = {Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek}, - ## journal = {R Journal}, - ## volume = {9}, - ## number = {1}, - ## pages = {385-392}, - ## year = {2017}, - ## url = {https://journal.r-project.org/archive/2017/RJ-2017-019/index.html}, - ## note = {Version 3.2.93}, - ## } - -### Related work - -This [rOpenGov](http://ropengov.github.io) R package is based on the -earlier CRAN packages -[statfi](https://cran.r-project.org/package=statfi) and -[smarterpoland](https://cran.r-project.org/package=SmarterPoland). - -The independent [reurostat](https://github.com/Tungurahua/reurostat) -package develops related Eurostat tools but seems to be in an -experimental stage at the time of writing this tutorial. - -The more generic [quandl](https://cran.r-project.org/package=quandl), -[datamart](https://cran.r-project.org/package=datamart), -[rsdmx](https://cran.r-project.org/package=rsdmx), and -[pdfetch](https://cran.r-project.org/package=pdfetch) packages may -provide access to some versions of eurostat data but these packages are -more generic and hence, in contrast to the eurostat R package, lack -tools that are specifically customized to facilitate eurostat analysis. - -### Contact - -For contact information, see the [package -homepage](http://ropengov.github.io/eurostat). - -Version info -============ - -This tutorial was created with - - sessionInfo() - - ## R version 3.5.1 (2018-07-02) - ## Platform: x86_64-pc-linux-gnu (64-bit) - ## Running under: Ubuntu 18.04.1 LTS - ## - ## Matrix products: default - ## BLAS: /home/lei/bin/R-3.5.1/lib/libRblas.so - ## LAPACK: /home/lei/bin/R-3.5.1/lib/libRlapack.so - ## - ## locale: - ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C - ## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 - ## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 - ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C - ## [9] LC_ADDRESS=C LC_TELEPHONE=C - ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C - ## - ## attached base packages: - ## [1] stats graphics grDevices utils datasets methods base - ## - ## other attached packages: - ## [1] rsdmx_0.5-13 sp_1.3-1 RColorBrewer_1.1-2 - ## [4] tmap_2.1-1 sf_0.6-3 dplyr_0.7.6 - ## [7] plotrix_3.7-4 ggplot2_3.0.0 tidyr_0.8.1 - ## [10] bindrcpp_0.2.2 rvest_0.3.2 xml2_1.2.0 - ## [13] rmarkdown_1.10 pkgdown_1.1.0.9000 knitr_1.20 - ## [16] eurostat_3.2.93 devtools_1.13.6 - ## - ## loaded via a namespace (and not attached): - ## [1] nlme_3.1-137 bitops_1.0-6 fs_1.2.6 - ## [4] satellite_1.0.1 lubridate_1.7.4 webshot_0.5.1 - ## [7] httr_1.3.1 rprojroot_1.3-2 mapview_2.6.0 - ## [10] tools_3.5.1 backports_1.1.2 rgdal_1.3-4 - ## [13] R6_2.3.0 KernSmooth_2.23-15 spData_0.2.9.4 - ## [16] rgeos_0.3-28 DBI_1.0.0 lazyeval_0.2.1 - ## [19] colorspace_1.3-2 raster_2.6-7 withr_2.1.2 - ## [22] tidyselect_0.2.5 processx_3.2.0 leaflet_2.0.2 - ## [25] curl_3.2 compiler_3.5.1 cli_1.0.1 - ## [28] Cairo_1.5-9 desc_1.2.0 labeling_0.3 - ## [31] scales_1.0.0 classInt_0.2-3 readr_1.1.1 - ## [34] callr_3.0.0 commonmark_1.6 stringr_1.3.1 - ## [37] digest_0.6.18 base64enc_0.1-3 dichromat_2.0-0 - ## [40] pkgconfig_2.0.2 htmltools_0.3.6 highr_0.7 - ## [43] htmlwidgets_1.3 rlang_0.2.2 shiny_1.1.0 - ## [46] bindr_0.1.1 jsonlite_1.5 crosstalk_1.0.0 - 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-#knit("eurostat_tutorial.Rmd") -#knit("manuscript.Rmd") -#render("manuscript.Rmd", output_format = "html_document") -#render("manuscript.Rmd", output_format = "pdf_document") -#knit2pdf("eurostat.Rnw") -#library(rmarkdown) -#render("eurostat_tutorial.Rmd", output_format = "pdf_document") diff --git a/vignettes/publications.Rmd b/vignettes/publications.Rmd deleted file mode 100755 index 41476ca1..00000000 --- a/vignettes/publications.Rmd +++ /dev/null @@ -1,27 +0,0 @@ ---- -title: "Publications using the eurostat R package" -date: "`r Sys.Date()`" -output: - rmarkdown::html_vignette: - toc: true -vignette: > - %\VignetteIndexEntry{eurostat blog posts} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteDepends{Cairo} - %\VignetteEncoding{UTF-8} - \usepackage[utf8]{inputenc} ---- - - -You can send us a note via the [issue tracker](https://github.com/rOpenGov/eurostat/issues) if you like to add a publication using the eurostat R package on this list: - - * Retrieval and analysis of Eurostat open data with the eurostat -package. R Journal 2017. -([manuscript](https://journal.r-project.org/archive/2017/RJ-2017-019/index.html); -[source code](https://github.com/rOpenGov/eurostat/tree/master/inst/extras/2017_RJournal_manuscript)). - - -External publications using the eurostat package: - - * Kenett RDS, Shmueli G. Information Quality: The Potential of Data and Analytics to Generate Knowledge. John Wiley & Sons, 2016. - diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd new file mode 100755 index 00000000..8f30dbc6 --- /dev/null +++ b/vignettes/vignette.Rmd @@ -0,0 +1,49 @@ +--- +title: "Vignette for the eurostat R package" +date: "`r Sys.Date()`" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Vignette for the eurostat R package} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true") +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + purl = NOT_CRAN, + eval = NOT_CRAN +) +``` + +```{r setup, include=FALSE} +# Global options +library(knitr) +# opts_chunk$set(fig.path="fig/") +``` + +# Installation + +Release version [(CRAN)](https://CRAN.R-project.org/package=eurostat): + + +```{r install, eval=FALSE} +install.packages("eurostat") +``` + +Development version [(Github)](https://github.com/rOpenGov/eurostat): + +```{r install2, eval=FALSE} +library(remotes) +remotes::install_github("ropengov/eurostat") +``` + +Load the package: + +```{r, echo=FALSE} +library(eurostat) +``` + +For more detailed examples on how to use the package, see the [online tutorial](https://ropengov.github.io/eurostat/articles/articles/eurostat_tutorial.html).

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