R Tools for Eurostat Open Data

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.

Installation

Release version (CRAN):

install.packages("eurostat")

Development version (Github):

library(devtools)
install_github("ropengov/eurostat")

Overall, the eurostat package includes the following functions:

add_nuts_level          Add the statistical aggregation level to data
                        frame
check_access_to_data    Check access to ec.europe.eu
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_geodata_60_2016
                        Geospatial data of Europe from Gisco in 1:60
                        million scale from year 2016
eurostat-package        R Tools for Eurostat open data
eurotime2date           Date Conversion from Eurostat Time Format
eurotime2num            Conversion of Eurostat Time Format to Numeric
get_bibentry            Create A Data Bibliography
get_eurostat            Read Eurostat Data
get_eurostat_dic        Download Eurostat Dictionary
get_eurostat_geospatial
                        Download Geospatial Data from GISCO
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
harmonize_geo_code      Harmonize NUTS region codes that changed with
                        the 'NUTS2016' definition
label_eurostat          Get Eurostat Codes
nuts_correspondence     Correspondence Table NUTS2013-NUTS2016
recode_to_nuts_2013     Recode geo labels and rename regions from
                        NUTS2016 to NUTS2013
recode_to_nuts_2016     Recode geo labels and rename regions from
                        NUTS2013 to NUTS2016
regional_changes_2016   Changes in regional boundaries
                        NUTS2013-NUTS2016
search_eurostat         Grep Datasets Titles from Eurostat
tgs00026                Auxiliary Data

evaluate <- curl::has_internet()

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))

title	code	type	last update of data	last table structure change	data start	data end	values
Database by themes	data	folder	NA	NA	NA	NA	NA
General and regional statistics	general	folder	NA	NA	NA	NA	NA
European and national indicators for short-term analysis	euroind	folder	NA	NA	NA	NA	NA
Business and consumer surveys (source: DG ECFIN)	ei_bcs	folder	NA	NA	NA	NA	NA
Consumer surveys (source: DG ECFIN)	ei_bcs_cs	folder	NA	NA	NA	NA	NA
Consumers - monthly data	ei_bsco_m	dataset	28.01.2021	28.01.2021	1980M01	2021M01	NA

Some of the data sets (e.g. in the ‘comext’ type) are not accessible through the standard interface. See the get_eurostat function documentation for more details.

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")))

title	code	type	last update of data	last table structure change	data start	data end	values
Volume of passenger transport relative to GDP	tran_hv_pstra	dataset	01.09.2020	31.08.2020	1990	2018	NA
Modal split of passenger transport	tran_hv_psmod	dataset	01.09.2020	31.08.2020	1990	2018	NA
Air passenger transport by reporting country	avia_paoc	dataset	26.01.2021	26.01.2021	1993	2020Q4	NA
Air passenger transport by main airports in each reporting country	avia_paoa	dataset	26.01.2021	26.01.2021	1993	2020Q4	NA
Air passenger transport between reporting countries	avia_paocc	dataset	26.01.2021	26.01.2021	1993	2020Q4	NA
Air passenger transport between main airports in each reporting country and partner reporting countries	avia_paoac	dataset	26.01.2021	26.01.2021	1993	2020Q4	NA

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)

## tibble [2,798 × 5] (S3: tbl_df/tbl/data.frame)
##  $ unit   : chr [1:2798] "PC" "PC" "PC" "PC" ...
##  $ vehicle: chr [1:2798] "BUS_TOT" "BUS_TOT" "BUS_TOT" "BUS_TOT" ...
##  $ geo    : chr [1:2798] "AT" "BE" "CH" "DE" ...
##  $ time   : num [1:2798] 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
##  $ values : num [1:2798] 8.2 10.6 3.7 9.1 11.3 32.4 14.9 13.5 6 24.8 ...

kable(head(dat))

unit	vehicle	geo	time	values
PC	BUS_TOT	AT	1990	8.2
PC	BUS_TOT	BE	1990	10.6
PC	BUS_TOT	CH	1990	3.7
PC	BUS_TOT	DE	1990	9.1
PC	BUS_TOT	DK	1990	11.3
PC	BUS_TOT	EL	1990	32.4

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)

unit	vehicle	geo	time	values
PC	BUS_TOT	EU28	2018	8.7
PC	BUS_TOT	FI	2018	10.1
PC	CAR	EU28	2018	83.3
PC	CAR	FI	2018	84.2
PC	TRN	EU28	2018	8.0
PC	TRN	FI	2018	5.7

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))

unit	vehicle	geo	time	values
Percentage	Motor coaches, buses and trolley buses	European Union - 28 countries (2013-2020)	2018	8.7
Percentage	Motor coaches, buses and trolley buses	Finland	2018	10.1
Percentage	Passenger cars	European Union - 28 countries (2013-2020)	2018	83.3
Percentage	Passenger cars	Finland	2018	84.2
Percentage	Trains	European Union - 28 countries (2013-2020)	2018	8.0
Percentage	Trains	Finland	2018	5.7

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))

unit	vehicle	geo	time	values
Percentage	Motor coaches, buses and trolley buses	Austria	1990	8.2
Percentage	Motor coaches, buses and trolley buses	Belgium	1990	10.6
Percentage	Motor coaches, buses and trolley buses	Switzerland	1990	3.7
Percentage	Motor coaches, buses and trolley buses	Germany (until 1990 former territory of the FRG)	1990	9.1
Percentage	Motor coaches, buses and trolley buses	Denmark	1990	11.3
Percentage	Motor coaches, buses and trolley buses	Greece	1990	32.4

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

label_eurostat_vars(names(datl))

## [1] "Unit of measure"                 "Vehicles"                       
## [3] "Geopolitical entity (reporting)" "Period of time"

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

levels(datl$vehicle)

## NULL

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)

code	name	label
IS	Iceland	Iceland
LI	Liechtenstein	Liechtenstein
NO	Norway	Norway
CH	Switzerland	Switzerland

EU data from 2012 in all vehicles:

dat_eu12 <- subset(datl, geo == "European Union - 28 countries" & time == 2012)
kable(dat_eu12, row.names = FALSE)

unit	vehicle	geo	time	values

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)

unit	time	BUS_TOT	CAR	TRN
PC	2000	10.4	82.5	7.1
PC	2001	10.2	82.8	7.0
PC	2002	9.8	83.4	6.8
PC	2003	9.8	83.6	6.6
PC	2004	9.7	83.5	6.7
PC	2005	9.8	83.4	6.9
PC	2006	9.6	83.4	7.0
PC	2007	9.8	83.1	7.1
PC	2008	9.8	82.9	7.4
PC	2009	9.2	83.7	7.1
PC	2010	9.3	83.5	7.2
PC	2011	9.4	83.2	7.4
PC	2012	9.4	82.9	7.7

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)

unit	time	Austria	Belgium	Finland	Sweden
Percentage	2000	9.8	6.3	5.1	6.9
Percentage	2001	9.8	6.4	4.8	7.3
Percentage	2002	9.7	6.5	4.8	7.2
Percentage	2003	9.6	6.5	4.7	7.1
Percentage	2004	9.5	7.1	4.7	6.9
Percentage	2005	9.8	6.6	4.8	7.1
Percentage	2006	10.0	6.9	4.8	7.7
Percentage	2007	10.1	7.1	5.0	8.0
Percentage	2008	11.1	7.5	5.4	8.7
Percentage	2009	11.2	7.5	5.1	8.8
Percentage	2010	11.1	7.7	5.2	8.7
Percentage	2011	11.5	7.7	5.0	8.7
Percentage	2012	11.9	7.8	5.3	9.1

SDMX

Eurostat data is available also in the Statistical Data and Metadata eXchange (SDMX) Web Services. Our eurostat R package does not provide custom tools for this but the following generic R packages provide access to eurostat SDMX version:

Further examples

For further examples, see the package homepage.

Citations and related work

Recommended packages

NOTE: we recommend to check also 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.

Citing the data sources

Eurostat data: cite Eurostat.

Administrative boundaries: cite EuroGeographics

Citing the eurostat R package

For main developers and contributors, see the package homepage.

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 3.7.1 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 3.7.1},
##   }

Contact

For contact information, see the package homepage.

Version info

This tutorial was created with

sessionInfo()

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.10
## 
## Matrix products: default
## BLAS:   /home/lemila/bin/R-4.0.3/lib/libRblas.so
## LAPACK: /home/lemila/bin/R-4.0.3/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] tidyr_1.1.2    rvest_0.3.6    xml2_1.3.2     eurostat_3.7.1 knitr_1.31    
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.1.0   xfun_0.20          purrr_0.3.4        sf_0.9-7          
##  [5] lattice_0.20-41    vctrs_0.3.6        generics_0.1.0     htmltools_0.5.1.1 
##  [9] yaml_2.2.1         rlang_0.4.10       e1071_1.7-4        pkgdown_1.6.1     
## [13] pillar_1.4.7       glue_1.4.2         DBI_1.1.1          sp_1.4-5          
## [17] RColorBrewer_1.1-2 lifecycle_0.2.0    plyr_1.8.6         stringr_1.4.0     
## [21] ragg_0.4.1         memoise_2.0.0      evaluate_0.14      fastmap_1.1.0     
## [25] curl_4.3           class_7.3-18       fansi_0.4.2        highr_0.8         
## [29] broom_0.7.4        Rcpp_1.0.6         KernSmooth_2.23-18 readr_1.4.0       
## [33] backports_1.2.1    classInt_0.4-3     cachem_1.0.1       desc_1.2.0        
## [37] jsonlite_1.7.2     countrycode_1.2.0  systemfonts_0.3.2  fs_1.5.0          
## [41] textshaping_0.2.1  hms_1.0.0          digest_0.6.27      stringi_1.5.3     
## [45] dplyr_1.0.3        rprojroot_2.0.2    grid_4.0.3         cli_2.2.0         
## [49] tools_4.0.3        magrittr_2.0.1     tibble_3.0.6       RefManageR_1.3.0  
## [53] crayon_1.3.4       pkgconfig_2.0.3    ellipsis_0.3.1     lubridate_1.7.9.2 
## [57] assertthat_0.2.1   rmarkdown_2.6.4    httr_1.4.2         R6_2.5.0          
## [61] units_0.6-7        compiler_4.0.3

Tutorial for the eurostat R package

2021-02-01