giscoR

giscoR is an R package that provides a simple interface to Eurostat’s GISCO database. It lets you 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, administrative units and statistical units.
• 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 objects for spatial analysis.
• Cache downloaded files for faster repeated access.

Installation

install.packages("giscoR")

# install.packages("pak")

pak::pak("rOpenGov/giscoR")

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 with the eurostat package, following 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")
#> 
indexed 0B in  0s, 0B/s
indexed 2.15GB in  0s, 2.15GB/s
                                                                              

Finally, merge and transform the data 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 will be 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 Geospatial Data from Eurostats 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 Eurostats 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, administrative and statistical units, transport networks, basic services and other GISCO datasets. This package is not officially related to or endorsed by Eurostat.},
}

General copyright

Disclaimer

This package is neither affiliated with nor endorsed by Eurostat. The authors are not responsible for any misuse of the data.