R Tools for Eurostat Open Data: maps
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.
See the vignette of eurostat
(vignette(package = "eurostat")) for installation and basic
use.
Maps
NOTE: we recommend to check also the
giscoRpackage (https://dieghernan.github.io/giscoR/). 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
package.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(eurostat)
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
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))
#> Table tgs00026 cached at /tmp/RtmpoLH6G0/eurostat/3c53672580607ed36618dd79a9c342ac.rds
# Download geospatial data from GISCO
geodata <- get_eurostat_geospatial(nuts_level = 2, year = 2016)
#> Extracting data from eurostat::eurostat_geodata_60_2016
# merge with attribute data with geodata
map_data <- inner_join(geodata, sp_data, by = "geo")Construct the map
# 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"
)
#>
#> ── tmap v3 code detected ───────────────────────────────────────────────────────
#> [v3->v4] `tm_shape()`: use `crs` instead of `projection`.
#> [v3->v4] `tm_tm_polygons()`: migrate the argument(s) related to the scale of
#> the visual variable `fill` namely 'palette' (rename to 'values') to fill.scale
#> = tm_scale(<HERE>).
#> [v3->v4] `tm_polygons()`: migrate the argument(s) related to the legend of the
#> visual variable `fill` namely 'title' to 'fill.legend = tm_legend(<HERE>)'
print(map1)
#> [cols4all] color palettes: use palettes from the R package cols4all. Run
#> `cols4all::c4a_gui()` to explore them. The old palette name "Oranges" is named
#> "brewer.oranges"
#> Multiple palettes called "oranges" found: "brewer.oranges", "matplotlib.oranges". The first one, "brewer.oranges", is returned.
Interactive maps can be generated as well
# Interactive
tmap_mode("view")
#> ℹ tmap mode set to "view".
map1
#> [cols4all] color palettes: use palettes from the R package cols4all. Run
#> `cols4all::c4a_gui()` to explore them. The old palette name "Oranges" is named
#> "brewer.oranges"
#> Multiple palettes called "oranges" found: "brewer.oranges", "matplotlib.oranges". The first one, "brewer.oranges", is returned.
# Set the mode back to normal plotting
tmap_mode("plot")
#> ℹ tmap mode set to "plot".
print(map1)
#> [cols4all] color palettes: use palettes from the R package cols4all. Run
#> `cols4all::c4a_gui()` to explore them. The old palette name "Oranges" is named
#> "brewer.oranges"
#> Multiple palettes called "oranges" found: "brewer.oranges", "matplotlib.oranges". The first one, "brewer.oranges", is returned.
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)
# Downloading and manipulating the tabular data
print("Let us focus on year 2016 and NUTS-3 level")
#> [1] "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)
)
#> Table tgs00026 cached at /tmp/RtmpoLH6G0/eurostat/2f9293a52d41fc32a4ad9cce47024c12.rds
print("Download geospatial data from GISCO")
#> [1] "Download geospatial data from GISCO"
geodata <- get_eurostat_geospatial(
resolution = "01", nuts_level = 2,
year = 2016, country = "PL"
)
#> Loading required namespace: giscoR
#> Extracting data using giscoR package, please report issues on https://github.com/rOpenGov/giscoR/issues
# 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)
#>
#> ── tmap v3 code detected ───────────────────────────────────────────────────────
#> [v3->v4] `tm_tm_polygons()`: migrate the argument(s) related to the scale of
#> the visual variable `fill` namely 'palette' (rename to 'values') to fill.scale
#> = tm_scale(<HERE>).
#> [v3->v4] `tm_polygons()`: use 'fill' for the fill color of polygons/symbols
#> (instead of 'col'), and 'col' for the outlines (instead of 'border.col').
#> [v3->v4] `tm_polygons()`: migrate the argument(s) related to the legend of the
#> visual variable `fill` namely 'title' to 'fill.legend = tm_legend(<HERE>)'
#> [v3->v4] `tm_text()`: migrate the layer options 'just' to 'options =
#> opt_tm_text(<HERE>)'
#> ! `tm_scale_bar()` is deprecated. Please use `tm_scalebar()` instead.
map2
#> [cols4all] color palettes: use palettes from the R package cols4all. Run
#> `cols4all::c4a_gui()` to explore them. The old palette name "Oranges" is named
#> "brewer.oranges"
#> Multiple palettes called "oranges" found: "brewer.oranges", "matplotlib.oranges". The first one, "brewer.oranges", is returned.
Disposable income of private households by NUTS 2 regions at 1:10mln resolution using ggplot2
# 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))
#> Dataset query already saved in cache_list.json...
#> Reading cache file /tmp/RtmpoLH6G0/eurostat/3c53672580607ed36618dd79a9c342ac.rds
#> Table tgs00026 read from cache file: /tmp/RtmpoLH6G0/eurostat/3c53672580607ed36618dd79a9c342ac.rds
# Download geospatial data from GISCO
data_geo <- get_eurostat_geospatial(
resolution = "01", nuts_level = "2",
year = 2016
)
#> Extracting data using giscoR package, please report issues on https://github.com/rOpenGov/giscoR/issues
# 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.
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
#>
#> 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
#>
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.Contact
For contact information, see the package homepage.
Version info
This tutorial was created with
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