Tutorial for the eurostat R package
2023-03-07
Source:vignettes/articles/eurostat_tutorial.Rmd
eurostat_tutorial.RmdR 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(remotes)
remotes::install_github("ropengov/eurostat")Overall, the eurostat package includes the following functions:
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-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
eurotime2date2 Date Conversion from New Eurostat Time Format
eurotime2num Conversion of Eurostat Time Format to Numeric
eurotime2num2 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_raw2 Download Data from Eurostat Dissemination API
get_eurostat_toc Download Table of Contents of Eurostat Data
Sets
harmonize_country_code
Harmonize Country Code
label_eurostat Get Eurostat Codes
label_eurostat2 Get Eurostat Codes for data downloaded from new
dissemination API
search_eurostat Grep Datasets Titles from Eurostat
set_eurostat_cache_dir
Set Eurostat Cache
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(tail(toc))| title | code | type | last update of data | last table structure change | data start | data end | values |
|---|---|---|---|---|---|---|---|
| Persons living in households with very low work intensity by age and sex (population aged 0 to 64 years) | ilc_lvhl11n | dataset | 03.03.2023 | 15.02.2023 | 2015 | 2022 | NA |
| People living in households with very low work intensity by group of country of birth (population aged 18 to 64 years) | ilc_lvhl16n | dataset | 03.03.2023 | 15.02.2023 | 2015 | 2022 | NA |
| In-work at-risk-of-poverty rate by age and sex - EU-SILC survey | ilc_iw01 | dataset | 03.03.2023 | 15.02.2023 | 2003 | 2022 | NA |
| Severe housing deprivation rate by age, sex and poverty status - EU-SILC survey | ilc_mdho06a | dataset | 09.09.2022 | 19.05.2021 | 2003 | 2020 | NA |
| Overcrowding rate by age, sex and poverty status - total population - EU-SILC survey | ilc_lvho05a | dataset | 03.03.2023 | 15.02.2023 | 2003 | 2022 | NA |
| Housing cost overburden rate by age, sex and poverty status - EU-SILC survey | ilc_lvho07a | dataset | 03.03.2023 | 15.02.2023 | 2003 | 2022 | 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 |
|---|---|---|---|---|---|---|---|
| Air passenger transport | enps_avia_pa | dataset | 10.02.2022 | 10.02.2022 | 2005 | 2020 | NA |
| Modal split of air, sea and inland passenger transport | tran_hv_ms_psmod | dataset | 31.01.2023 | NA | 2008 | 2020 | NA |
| Modal split of inland passenger transport | tran_hv_psmod | dataset | 13.07.2022 | 13.07.2022 | 1990 | 2020 | NA |
| Volume of passenger transport relative to GDP | tran_hv_pstra | dataset | 13.07.2022 | 13.07.2022 | 1990 | 2020 | NA |
| 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 | NA |
| Air passenger transport by reporting country | avia_paoc | dataset | 13.02.2023 | 13.02.2023 | 1993 | 2022Q4 | 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"
)$code[1]
print(id)[1] NA
Get the whole corresponding table. As the table is annual data, it is more convenient to use a numeric time variable than use the default date format:
dat <- get_eurostat(id, time_format = "num")Investigate the structure of the downloaded data set:
str(dat)Or you can get only a part of the dataset by defining
filters argument. It should be named list, where names
corresponds to variable names (lower case) and values are vectors of
codes corresponding desired series (upper case). For time variable, in
addition to a time, also a sinceTimePeriod and
a lastTimePeriod can be used.
dat2 <- get_eurostat(id, filters = list(geo = c("EU28", "FI"), lastTimePeriod = 1), time_format = "num")
kable(dat2)Replacing codes with labels
By default variables are returned as Eurostat codes, but to get
human-readable labels instead, use a type = "label"
argument.
datl2 <- get_eurostat(id,
filters = list(
geo = c("EU28", "FI"),
lastTimePeriod = 1
),
type = "label", time_format = "num"
)
kable(head(datl2))Eurostat codes in the downloaded data set can be replaced with
human-readable labels from the Eurostat dictionaries with the
label_eurostat() function.
datl <- label_eurostat(dat)
kable(head(datl))The label_eurostat() allows conversion of individual
variable vectors or variable names as well.
label_eurostat_vars(names(datl))Vehicle information has 3 levels. You can check them now with:
levels(datl$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:
EU data from 2000 - 2012 with vehicle types as variables:
Reshaping the data is best done with spread() in
tidyr.
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. doi: 10.32614/RJ-2017-019
## Package URL: http://ropengov.github.io/eurostat Article URL:
## https://journal.r-project.org/archive/2017/RJ-2017-019/index.html
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## 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},
## }Contact
For contact information, see the package homepage.
Version info
This tutorial was created with
## R version 4.2.2 (2022-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
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## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
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## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] eurostat_3.8.3 knitr_1.42
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## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.10 lubridate_1.9.2 here_1.0.1 countrycode_1.4.0
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