giscoR is an R package that provides a simple interface to the Eurostat GISCO geodata distribution. It lets you download and work with global and European geospatial datasets directly in R, including country boundaries, NUTS regions, administrative units, statistical units, transport networks and basic service locations.
Key features
- Retrieve GISCO datasets for country boundaries, NUTS regions, administrative units, statistical units, transport networks and basic service locations.
- Access data at multiple resolutions:
60M,20M,10M,03M,01M. - Choose from three coordinate reference systems: EPSG:4326, EPSG:3035 or EPSG:3857.
- Return sf package objects for spatial analysis.
- Cache downloaded files for faster repeated access.
Installation
Install giscoR from CRAN:
install.packages("giscoR")Quick example
This script highlights selected giscoR features:
library(giscoR)
library(sf)
library(dplyr)
# Download Netherlands boundaries at different resolutions.
nl_all <- lapply(c("60", "20", "10", "03"), function(r) {
gisco_get_countries(country = "Netherlands", year = 2024, resolution = r) |>
mutate(res = paste0(r, "M"))
}) |>
bind_rows()
glimpse(nl_all)
#> Rows: 4
#> Columns: 15
#> $ CNTR_ID <chr> "NL", "NL", "NL", "NL"
#> $ COUNTRY_URI <chr> "NLD", NA, "NLD", "NLD"
#> $ CNTR_NAME <chr> "Nederland", "Nederland", "Nederland", "Nederland"
#> $ NAME_ENGL <chr> "Netherlands", "Netherlands", "Netherlands", "Netherlands"
#> $ NAME_FREN <chr> "Pays-Bas", "Pays-Bas", "Pays-Bas", "Pays-Bas"
#> $ ISO3_CODE <chr> "NLD", "NLD", "NLD", "NLD"
#> $ SVRG_UN <chr> "UN Member State", "UN Member State", "UN Member State", "…
#> $ CAPT <chr> "Amsterdam", "Amsterdam", "Amsterdam", "Amsterdam"
#> $ STAT_CODE <chr> "OA", NA, "OA", "OA"
#> $ EU_STAT <chr> "T", "T", "T", "T"
#> $ EFTA_STAT <chr> "F", "F", "F", "F"
#> $ CC_STAT <chr> "F", "F", "F", "F"
#> $ NAME_GERM <chr> "Niederlande", "Niederlande", "Niederlande", "Niederlande"
#> $ res <chr> "60M", "20M", "10M", "03M"
#> $ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((7.208935 53..., MULTIPOLYGON (((7.202794 5…
# Plot with ggplot2.
library(ggplot2)
ggplot(nl_all) +
geom_sf(fill = "#AD1D25") +
facet_wrap(~res) +
labs(
title = "Netherlands boundaries at different resolutions",
subtitle = "Year: 2024",
caption = gisco_attributions()
) +
theme_minimal()
Advanced example: thematic maps
This example shows a thematic map created with the ggplot2 package. The statistical data are obtained with the eurostat package, following the work of Milos Popovic.
Start by downloading the corresponding geospatial data:
library(giscoR)
library(dplyr)
library(eurostat)
library(ggplot2)
# Retrieve **sf** package objects.
nuts3 <- gisco_get_nuts(
year = 2021,
epsg = 3035,
resolution = 10,
nuts_level = 3
)
# Get country boundaries at NUTS 0 level.
country_lines <- gisco_get_nuts(
year = 2021,
epsg = 3035,
resolution = 10,
spatialtype = "BN",
nuts_level = 0
)Next, download the statistical data from Eurostat.
# Retrieve Eurostat data.
popdens <- get_eurostat("demo_r_d3dens") |>
filter(TIME_PERIOD == "2021-01-01")
#>
indexed 0B in 0s, 0B/s
indexed 2.15GB in 0s, 2.15GB/s
Finally, merge and transform the datasets to create the plot.
# Merge data.
nuts3_sf <- nuts3 |>
left_join(popdens, by = "geo")
# Create breaks and labels.
br <- c(0, 25, 50, 100, 200, 500, 1000, 2500, 5000, 10000, 30000)
labs <- prettyNum(br[-1], big.mark = ",")
# Label missing values in the plot.
labeller_plot <- function(x) {
ifelse(is.na(x), "No Data", x)
}
nuts3_sf <- nuts3_sf |>
# Cut with labels.
mutate(values_cut = cut(values, br, labels = labs))
# Create palette.
pal <- hcl.colors(length(labs), "Lajolla")
# Create plot.
ggplot(nuts3_sf) +
geom_sf(aes(fill = values_cut), linewidth = 0, color = NA, alpha = 0.9) +
geom_sf(data = country_lines, col = "black", linewidth = 0.1) +
# Center on Europe with EPSG 3035.
coord_sf(
xlim = c(2377294, 7453440),
ylim = c(1313597, 5628510)
) +
# Configure legends.
scale_fill_manual(
values = pal,
# Label missing values.
labels = labeller_plot,
drop = FALSE,
guide = guide_legend(direction = "horizontal", nrow = 1)
) +
theme_void() +
# Configure the theme.
theme(
plot.title = element_text(
color = rev(pal)[2],
size = rel(1.5),
hjust = 0.5,
vjust = -6
),
plot.subtitle = element_text(
color = rev(pal)[2],
size = rel(1.25),
hjust = 0.5,
vjust = -10,
face = "bold"
),
plot.caption = element_text(color = "grey60", hjust = 0.5, vjust = 0),
legend.text = element_text(color = "grey20", hjust = 0.5),
legend.title = element_text(color = "grey20", hjust = 0.5),
legend.position = "bottom",
legend.title.position = "top",
legend.text.position = "bottom",
legend.key.height = unit(0.5, "line"),
legend.key.width = unit(2.5, "line")
) +
# Add labels.
labs(
title = "Population density in 2021",
subtitle = "NUTS 3 level",
fill = "people per square kilometer",
caption = paste0(
"Source: Eurostat, ",
gisco_attributions(),
"\nBased on Milos Popovic's work"
)
)
Caching
Large datasets, such as LAU or high-resolution files, can exceed 50 MB. Set a cache directory with:
gisco_set_cache_dir("./path/to/location")Files are stored in the local cache for faster repeated access.
Contribute
See the GitHub repository for source code.
Contributions are welcome.
- Use the issue tracker for feedback and bug reports.
- Send pull requests.
- Star giscoR on GitHub.
Citation
To cite ‘giscoR’ in publications use:
Hernangómez D (2026). giscoR: Download Eurostat GISCO Spatial Data. doi:10.32614/CRAN.package.giscoR https://doi.org/10.32614/CRAN.package.giscoR. https://ropengov.github.io/giscoR/.
A BibTeX entry for LaTeX users is:
@Manual{R-giscoR,
title = {{giscoR}: Download Eurostat GISCO Spatial Data},
doi = {10.32614/CRAN.package.giscoR},
author = {Diego Hernangómez},
year = {2026},
version = {1.1.1},
url = {https://ropengov.github.io/giscoR/},
abstract = {Tools to download global and European spatial data from the Eurostat GISCO (Geographic Information System of the Commission) data distribution <https://ec.europa.eu/eurostat/web/gisco>. The package provides helpers for country boundaries, NUTS regions, administrative units, statistical units, transport networks, basic service locations and other GISCO datasets. This package is not officially related to or endorsed by Eurostat.},
}General copyright
Eurostat’s general copyright notice and license policy applies. Some datasets have additional download and usage provisions. The download and use of these data are subject to acceptance of those provisions. See the administrative units and statistical units for more details.