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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(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:

# 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.

Overall, the eurostat package includes the following user-facing 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-defunct        Defunct functions in eurostat
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 New Eurostat Time Format
eurotime2num            Conversion of Eurostat Time Format to Numeric
get_bibentry            Create A Data Bibliography
get_eurostat            Get Eurostat Data
get_eurostat_dic        Download Eurostat Dictionary
get_eurostat_folder     Get all datasets in a folder
get_eurostat_geospatial
                        Download Geospatial Data from GISCO
get_eurostat_interactive
                        Get Eurostat data interactive
get_eurostat_json       Get Data from Eurostat API in JSON
get_eurostat_raw        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 for data downloaded from new
                        dissemination API
list_eurostat_cache_items
                        Output cache information as data.frame
search_eurostat         Grep Datasets Titles from Eurostat
set_eurostat_cache_dir
                        Set Eurostat Cache
tgs00026                Auxiliary Data
evaluate <- curl::has_internet()

Finding data

First stop for most researchers would be to browse the Eurostat Data Browser website 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.

# Load the package
library(eurostat)

# 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 hierarchy
Persons living in households with very low work intensity by age and sex (population aged 0 to 64 years) ilc_lvhl11n dataset 08.04.2024 02.02.2024 2014 2023 47100 4
People living in households with very low work intensity by group of country of birth (population aged 18 to 64 years) ilc_lvhl16n dataset 08.04.2024 02.02.2024 2014 2023 189180 4
In-work at-risk-of-poverty rate by age and sex - EU-SILC survey ilc_iw01 dataset 08.04.2024 02.02.2024 2003 2023 68175 4
Severe housing deprivation rate by age, sex and poverty status - EU-SILC survey ilc_mdho06a dataset 22.03.2024 03.01.2024 2003 2020 87354 4
Overcrowding rate by age, sex and poverty status - total population - EU-SILC survey ilc_lvho05a dataset 08.04.2024 02.02.2024 2003 2023 105525 4
Housing cost overburden rate by age, sex and poverty status - EU-SILC survey ilc_lvho07a dataset 08.04.2024 02.02.2024 2003 2023 110358 4

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.

kable(head(get_eurostat_toc(lang = "fr")))
title code type last.update.of.data last.table.structure.change data.start data.end values hierarchy
Base de données par thèmes data folder NA 0
Statistiques générales et régionales general folder NA 1
Indicateurs européens et nationaux pour l’analyse à court terme euroind folder NA 2
Balance des paiements ei_bp folder NA 3
Compte courant - données trimestrielles ei_bpm6ca_q table 09.04.2024 09.04.2024 1992-Q1 2023-Q4 274378 4
Compte financier - données trimestrielles ei_bpm6fa_q table 09.04.2024 09.04.2024 1992-Q1 2023-Q4 49248 4

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 “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”.

# 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 hierarchy
Air passenger transport enps_avia_pa dataset 26.02.2024 26.02.2024 2005 2022 421 6
Modal split of air, sea and inland passenger transport tran_hv_ms_psmod dataset 29.06.2023 03.01.2024 2008 2021 2100 4
Modal split of inland passenger transport tran_hv_psmod dataset 29.06.2023 03.01.2024 1990 2021 4219 4
Volume of passenger transport relative to GDP tran_hv_pstra dataset 11.08.2023 03.01.2024 1990 2021 969 4
Maritime passenger transport performed in the Exclusive Economic Zone (EEZ) of the countries mar_tp_pa dataset 04.03.2024 04.03.2024 2005 2022 1861 4
Air passenger transport by reporting country avia_paoc dataset 14.03.2024 14.03.2024 1993 2024-01 2478521 5

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.

kable(head(search_eurostat("migr", column = "code")))
title code type last.update.of.data last.table.structure.change data.start data.end values hierarchy
Total and active population by sex, age, employment status, residence one year prior to the census and NUTS 3 regions cens_01ramigr dataset 26.03.2009 03.01.2024 2001 2001 1485361 6
Total and active population by sex, age, employment status, residence one year prior to the census and NUTS 3 regions cens_01ramigr dataset 26.03.2009 03.01.2024 2001 2001 1485361 6
Population on 1 January by age, sex and broad group of citizenship migr_pop2ctz dataset 26.03.2024 25.03.2024 1998 2023 603367 4
Population on 1 January by age group, sex and citizenship migr_pop1ctz dataset 26.03.2024 25.03.2024 1998 2023 8132768 4
Population on 1 January by age group, sex and country of birth migr_pop3ctb dataset 26.03.2024 25.03.2024 1998 2023 6302436 4
Population on 1 January by age, sex and group of country of birth migr_pop4ctb dataset 26.03.2024 25.03.2024 1998 2023 584797 4

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.

kable(head(search_eurostat("flughafen", column = "title", lang = "de")))
title code type last.update.of.data last.table.structure.change data.start data.end values hierarchy
Flughafenverkehr nach Meldeflughafen und Luftverkehrsgesellschaften avia_tf_apal dataset 14.03.2024 14.03.2024 1997 2024-01 1574913 4
Kommerzieller Luftverkehr nach Berichtsflughafen – monatliche Daten avia_tf_airpm dataset 10.04.2024 10.04.2024 2019-01 2024-03 650160 4

As mentioned in the beginning, codes for different dataset can be found also from the Eurostat 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.

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

# 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))
title code type last.update.of.data last.table.structure.change data.start data.end values hierarchy
Modal split of air, sea and inland passenger transport tran_hv_ms_psmod dataset 29.06.2023 03.01.2024 2008 2021 2100 4
# Get the id from the table
id <- search_results$code[1]

# Check the id
print(id)

[1] “tran_hv_ms_psmod”

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).

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:

str(dat)
## tibble [2,100 × 6] (S3: tbl_df/tbl/data.frame)
##  $ freq       : Factor w/ 1 level "A": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vehicle    : Factor w/ 5 levels "AC","BUS_TOT",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ unit       : Factor w/ 1 level "PC": 1 1 1 1 1 1 1 1 1 1 ...
##  $ geo        : Factor w/ 30 levels "AT","BE","BG",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ TIME_PERIOD: num [1:2100] 2008 2009 2010 2011 2012 ...
##  $ values     : num [1:2100] 15.6 15.3 16.1 16.9 18.2 18.5 18.9 19.2 19 20.1 ...
kable(head(dat))
freq vehicle unit geo TIME_PERIOD values
A AC PC AT 2008 15.6
A AC PC AT 2009 15.3
A AC PC AT 2010 16.1
A AC PC AT 2011 16.9
A AC PC AT 2012 18.2
A AC PC AT 2013 18.5

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.

dat2 <- get_eurostat(id, filters = list(geo = c("EU27_2020", "FI"), lastTimePeriod = 1), time_format = "num")
kable(dat2)
freq vehicle unit geo time values
A TRN PC EU27_2020 2021 5.6
A TRN PC FI 2021 3.9
A CAR PC EU27_2020 2021 79.7
A CAR PC FI 2021 85.2
A BUS_TOT PC EU27_2020 2021 7.1
A BUS_TOT PC FI 2021 8.0
A SEAV PC EU27_2020 2021 0.3
A SEAV PC FI 2021 1.0
A AC PC EU27_2020 2021 7.3
A AC PC FI 2021 1.9

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().

dat_labeled2 <- get_eurostat(id,
  filters = list(
    geo = c("EU27_2020", "FI"),
    lastTimePeriod = 1
  ),
  type = "label", time_format = "num"
)
kable(head(dat_labeled2))
freq vehicle unit geo time values
Annual Trains Percentage European Union - 27 countries (from 2020) 2021 5.6
Annual Trains Percentage Finland 2021 3.9
Annual Passenger cars Percentage European Union - 27 countries (from 2020) 2021 79.7
Annual Passenger cars Percentage Finland 2021 85.2
Annual Motor coaches, buses and trolley buses Percentage European Union - 27 countries (from 2020) 2021 7.1
Annual Motor coaches, buses and trolley buses Percentage Finland 2021 8.0

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

dat_labeled <- label_eurostat(dat)
kable(head(dat_labeled))
freq vehicle unit geo TIME_PERIOD values
Annual Aircraft Percentage Austria 2008 15.6
Annual Aircraft Percentage Austria 2009 15.3
Annual Aircraft Percentage Austria 2010 16.1
Annual Aircraft Percentage Austria 2011 16.9
Annual Aircraft Percentage Austria 2012 18.2
Annual Aircraft Percentage Austria 2013 18.5

The label_eurostat_vars() allows conversion of variable names as well.

print(label_eurostat_vars(id = "tran_hv_ms_psmod", names(dat_labeled)))
## [1] "Time frequency"                  "Vehicles"                       
## [3] "Unit of measure"                 "Geopolitical entity (reporting)"
## [5] "Time"

Vehicle information has 5 levels. You can check them now with:

levels(dat_labeled$vehicle)
## [1] "Aircraft"                              
## [2] "Motor coaches, buses and trolley buses"
## [3] "Passenger cars"                        
## [4] "Seagoing vessels"                      
## [5] "Trains"

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.

> 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
   <chr> <chr>   <chr> <chr> <date>       <dbl>
 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 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(dat_labeled, geo == "European Union - 27 countries (from 2020)" & TIME_PERIOD == 2012)
kable(dat_eu12, row.names = FALSE)
freq vehicle unit geo TIME_PERIOD values
Annual Aircraft Percentage European Union - 27 countries (from 2020) 2012 11.6
Annual Motor coaches, buses and trolley buses Percentage European Union - 27 countries (from 2020) 2012 9.1
Annual Passenger cars Percentage European Union - 27 countries (from 2020) 2012 72.3
Annual Seagoing vessels Percentage European Union - 27 countries (from 2020) 2012 0.4
Annual Trains Percentage European Union - 27 countries (from 2020) 2012 6.7

EU data from 2008 - 2020 with vehicle types as variables:

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

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)
freq unit TIME_PERIOD AC BUS_TOT CAR SEAV TRN
A PC 2008 10.4 8.9 73.8 0.5 6.5
A PC 2009 9.6 8.5 75.1 0.5 6.3
A PC 2010 10.3 8.8 74.0 0.5 6.5
A PC 2011 10.9 9.0 73.1 0.4 6.6
A PC 2012 11.6 9.1 72.3 0.4 6.7
A PC 2013 11.9 9.1 72.0 0.4 6.6
A PC 2014 12.2 8.8 72.0 0.4 6.6
A PC 2015 12.5 8.7 71.8 0.4 6.6
A PC 2016 12.9 8.6 71.6 0.4 6.6
A PC 2017 13.7 8.2 71.1 0.4 6.6
A PC 2018 14.5 8.1 70.4 0.4 6.6
A PC 2019 15.0 8.1 69.8 0.4 6.8
A PC 2020 5.7 6.9 81.9 0.2 5.2

Train passengers for selected EU countries in 2008 - 2020

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)
freq unit TIME_PERIOD Austria Belgium Finland Sweden
Annual Percentage 2008 9.4 6.6 4.9 8.1
Annual Percentage 2009 9.5 6.8 4.7 8.2
Annual Percentage 2010 9.3 6.9 4.7 8.0
Annual Percentage 2011 9.5 7.1 4.6 7.5
Annual Percentage 2012 9.7 6.3 4.8 7.8
Annual Percentage 2013 10.0 6.4 4.8 7.8
Annual Percentage 2014 9.9 6.7 4.6 7.8
Annual Percentage 2015 9.7 6.8 4.8 8.0
Annual Percentage 2016 9.8 6.8 5.0 9.0
Annual Percentage 2017 9.6 6.9 4.9 9.1
Annual Percentage 2018 10.4 7.0 5.1 9.2
Annual Percentage 2019 10.4 7.2 5.5 9.9
Annual Percentage 2020 8.3 6.6 3.7 7.1

Other packages

The giscoR (package homepage) 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 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 and 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) standards.

Further examples

For further examples, see articles in the package homepage.

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:
## 
##   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},
##   }

Contact

For contact information, see the package homepage.

Version info

This tutorial was created with

sessioninfo::session_info()
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##  setting  value
##  version  R version 4.3.3 (2024-02-29)
##  os       Ubuntu 22.04.4 LTS
##  system   x86_64, linux-gnu
##  ui       X11
##  language en
##  collate  C.UTF-8
##  ctype    C.UTF-8
##  tz       UTC
##  date     2024-04-11
##  pandoc   3.1.11 @ /opt/hostedtoolcache/pandoc/3.1.11/x64/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package     * version date (UTC) lib source
##  assertthat    0.2.1   2019-03-21 [1] RSPM
##  backports     1.4.1   2021-12-13 [1] RSPM
##  bibtex        0.5.1   2023-01-26 [1] RSPM
##  bit           4.0.5   2022-11-15 [1] RSPM
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##  cachem        1.0.8   2023-05-01 [1] RSPM
##  cellranger    1.1.0   2016-07-27 [1] RSPM
##  class         7.3-22  2023-05-03 [3] CRAN (R 4.3.3)
##  classInt      0.4-10  2023-09-05 [1] RSPM
##  cli           3.6.2   2023-12-11 [1] RSPM
##  countrycode   1.6.0   2024-03-22 [1] RSPM
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##  curl          5.2.1   2024-03-01 [1] RSPM
##  data.table    1.15.4  2024-03-30 [1] RSPM
##  desc          1.4.3   2023-12-10 [1] RSPM
##  digest        0.6.35  2024-03-11 [1] RSPM
##  dplyr         1.1.4   2023-11-17 [1] RSPM
##  e1071         1.7-14  2023-12-06 [1] RSPM
##  eurostat    * 4.0.0   2024-04-11 [1] local
##  evaluate      0.23    2023-11-01 [1] RSPM
##  fansi         1.0.6   2023-12-08 [1] RSPM
##  fastmap       1.1.1   2023-02-24 [1] RSPM
##  fs            1.6.3   2023-07-20 [1] RSPM
##  generics      0.1.3   2022-07-05 [1] RSPM
##  glue          1.7.0   2024-01-09 [1] RSPM
##  here          1.0.1   2020-12-13 [1] RSPM
##  hms           1.1.3   2023-03-21 [1] RSPM
##  htmltools     0.5.8.1 2024-04-04 [1] RSPM
##  httr          1.4.7   2023-08-15 [1] RSPM
##  httr2         1.0.1   2024-04-01 [1] RSPM
##  ISOweek       0.6-2   2011-09-07 [1] RSPM
##  jquerylib     0.1.4   2021-04-26 [1] RSPM
##  jsonlite      1.8.8   2023-12-04 [1] RSPM
##  KernSmooth    2.23-22 2023-07-10 [3] CRAN (R 4.3.3)
##  knitr       * 1.46    2024-04-06 [1] RSPM
##  lifecycle     1.0.4   2023-11-07 [1] RSPM
##  lubridate     1.9.3   2023-09-27 [1] RSPM
##  magrittr      2.0.3   2022-03-30 [1] RSPM
##  memoise       2.0.1   2021-11-26 [1] RSPM
##  pillar        1.9.0   2023-03-22 [1] RSPM
##  pkgconfig     2.0.3   2019-09-22 [1] RSPM
##  pkgdown       2.0.7   2022-12-14 [1] any (@2.0.7)
##  plyr          1.8.9   2023-10-02 [1] RSPM
##  proxy         0.4-27  2022-06-09 [1] RSPM
##  purrr         1.0.2   2023-08-10 [1] RSPM
##  R6            2.5.1   2021-08-19 [1] RSPM
##  ragg          1.3.0   2024-03-13 [1] RSPM
##  rappdirs      0.3.3   2021-01-31 [1] RSPM
##  Rcpp          1.0.12  2024-01-09 [1] RSPM
##  readr         2.1.5   2024-01-10 [1] RSPM
##  readxl        1.4.3   2023-07-06 [1] RSPM
##  RefManageR    1.4.0   2022-09-30 [1] RSPM
##  regions       0.1.8   2021-06-21 [1] RSPM
##  rlang         1.1.3   2024-01-10 [1] RSPM
##  rmarkdown     2.26    2024-03-05 [1] RSPM
##  rprojroot     2.0.4   2023-11-05 [1] RSPM
##  sass          0.4.9   2024-03-15 [1] RSPM
##  sessioninfo   1.2.2   2021-12-06 [1] any (@1.2.2)
##  stringi       1.8.3   2023-12-11 [1] RSPM
##  stringr       1.5.1   2023-11-14 [1] RSPM
##  systemfonts   1.0.6   2024-03-07 [1] RSPM
##  textshaping   0.3.7   2023-10-09 [1] RSPM
##  tibble        3.2.1   2023-03-20 [1] RSPM
##  tidyr       * 1.3.1   2024-01-24 [1] RSPM
##  tidyselect    1.2.1   2024-03-11 [1] RSPM
##  timechange    0.3.0   2024-01-18 [1] RSPM
##  tzdb          0.4.0   2023-05-12 [1] RSPM
##  utf8          1.2.4   2023-10-22 [1] RSPM
##  vctrs         0.6.5   2023-12-01 [1] RSPM
##  vroom         1.6.5   2023-12-05 [1] RSPM
##  withr         3.0.0   2024-01-16 [1] RSPM
##  xfun          0.43    2024-03-25 [1] RSPM
##  xml2          1.3.6   2023-12-04 [1] RSPM
##  yaml          2.3.8   2023-12-11 [1] RSPM
## 
##  [1] /home/runner/work/_temp/Library
##  [2] /opt/R/4.3.3/lib/R/site-library
##  [3] /opt/R/4.3.3/lib/R/library
## 
## ──────────────────────────────────────────────────────────────────────────────