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 (such as country boundaries, NUTS regions, coastlines, and labels) directly in R.
- Retrieve GISCO files 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.
- Returns sf objects for spatial analysis.
- Caches downloads for faster repeated access.
Install giscoR from CRAN:
install.packages("giscoR")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")
)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()
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 lines 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")
#>
indexed 0B in 0s, 0B/s
indexed 2.15GB in 0s, 2.15GB/s
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 function used in the plot, mainly for missing values.
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 for NA
labels = labeller_plot,
drop = FALSE,
guide = guide_legend(direction = "horizontal", nrow = 1)
) +
# Theming
theme_void() +
# 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")
) +
# Annotate and labs
labs(
title = "Population density in 2021",
subtitle = "NUTS-3 level",
fill = "people per sq. kilometer",
caption = paste0(
"Source: Eurostat, ",
gisco_attributions(),
"\nBased on Milos Popovic's work"
)
)
Large datasets (e.g., LAU or high-resolution files) can exceed 50 MB. Use:
gisco_set_cache_dir("./path/to/location")Files will be stored locally for faster access.
Check the GitHub page for source code.
Contributions are welcome:
- Use the issue tracker for feedback and bug reports.
- Send pull requests.
- Star us on the GitHub page.
To cite ‘giscoR’ in publications use:
Hernangómez D (2026). giscoR: Download Map 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 Map Data from the GISCO API},
doi = {10.32614/CRAN.package.giscoR},
author = {Diego Hernangómez},
year = {2026},
version = {1.1.0},
url = {https://ropengov.github.io/giscoR/},
abstract = {Tools to download global and European map data from the GISCO (Geographic Information System of the Commission) Eurostat 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.},
}
Source:Eurostat’s general copyright notice and license policy applies. Moreover, there are specific rules that apply to some of the following datasets available for downloading. The download and use of these data are subject to these rules being accepted. See our administrative units and statistical units for more details.
This package is neither affiliated with nor endorsed by Eurostat. The authors are not responsible for any misuse of the data.