Tutorial for the eurostat R package
2024-04-11
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")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 ...| 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 rowsSelecting 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:
| 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
Strongly recommended
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.
Citations and related work
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
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