giscoR is an R package that provides a simple interface to GISCO data from Eurostat. It allows you to download and work with global and European geospatial datasets directly in R, including country boundaries, NUTS regions, coastal lines and labels.
Key features
- Retrieve GISCO datasets for countries, regions and administrative units.
- Access data at multiple resolutions:
60M,20M,10M,03M,01M. - Choose from three projections: EPSG:4326, EPSG:3035, or EPSG:3857.
- Return
sfobjects for spatial analysis. - Cache downloads for faster repeated access.
Installation
Check the documentation for the development version at https://ropengov.github.io/giscoR/dev/.
You can install the development version of giscoR with:
# install.packages("pak")
pak::pak("rOpenGov/giscoR")Alternatively, you can install giscoR via r-universe:
install.packages(
"giscoR",
repos = c("https://ropengov.r-universe.dev", "https://cloud.r-project.org")
)Quick example
This script highlights some features of giscoR:
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 data are obtained via the eurostat package. This follows the work of Milos Popovic.
We start by extracting the corresponding geographic data:
library(giscoR)
library(dplyr)
library(eurostat)
library(ggplot2)
# Retrieve sf 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 data from Eurostat:
# Retrieve Eurostat data.
popdens <- get_eurostat("demo_r_d3dens") |>
filter(TIME_PERIOD == "2021-01-01")Finally, we merge and manipulate the data to create the final 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 will be stored locally for faster 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 Geospatial Data from the GISCO API. 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 Geospatial Data from the GISCO API},
doi = {10.32614/CRAN.package.giscoR},
author = {Diego Hernangómez},
year = {2026},
version = {1.1.0.9000},
url = {https://ropengov.github.io/giscoR/},
abstract = {Tools to download global and European geospatial data from Eurostats GISCO (Geographic Information System of the Commission) database <https://ec.europa.eu/eurostat/web/gisco>. The package provides helpers for working with country boundaries, NUTS regions, statistical units, transport networks and other geospatial 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.