giscoR

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.

Key features

• 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, 3035, or 3857.
• Works seamlessly with sf objects for spatial analysis.
• Includes caching for faster repeated access.

Installation

Install giscoR from CRAN:

install.packages("giscoR")

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 The 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: 13
#> $ CNTR_ID   <chr> "NL", "NL", "NL", "NL"
#> $ 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", "UN…
#> $ CAPT      <chr> "Amsterdam", "Amsterdam", "Amsterdam", "Amsterdam"
#> $ 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"
#> $ geometry  <MULTIPOLYGON [°]> MULTIPOLYGON (((7.208935 53..., MULTIPOLYGON (((7.202794 53.…
#> $ res       <chr> "60M", "20M", "10M", "03M"

# Plot with ggplot2

library(ggplot2)

ggplot(nl_all) +
  geom_sf(fill = "#AD1D25") +
  facet_wrap(~res) +
  labs(
    title = "The 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 approach presented by Milos Popovic in this post.

We start by extracting the corresponding geographic data:

library(giscoR)
library(dplyr)
library(eurostat)
library(ggplot2)

# Get sf objects
nuts3 <- gisco_get_nuts(
  year = 2021,
  epsg = 3035,
  resolution = 10,
  nuts_level = 3
)

# Get country lines (NUTS 0 level)

country_lines <- gisco_get_nuts(
  year = 2021,
  epsg = 3035,
  resolution = 10,
  spatialtype = "BN",
  nuts_level = 0
)

We now download the data from Eurostat:

# Use eurostat
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")

# 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 NAs
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))


# Palette
pal <- hcl.colors(length(labs), "Lajolla")


# 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 in Europe: EPSG 3035
  coord_sf(
    xlim = c(2377294, 7453440),
    ylim = c(1313597, 5628510)
  ) +
  # 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 = .5),
    legend.title = element_text(color = "grey20", hjust = .5),
    legend.position = "bottom",
    legend.title.position = "top",
    legend.text.position = "bottom",
    legend.key.height = unit(.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: ",
      "https://milospopovic.net/how-to-make-choropleth-map-in-r/"
    )
  )

Caching

Large datasets (e.g., LAU or high-resolution files) can exceed 50MB. Use:

gisco_set_cache_dir("./path/to/location")

Files will be stored locally for faster access.

Contribute

Check the GitHub page for source code.

Contributions are welcome:

• Use issue tracker for feedback and bug reports.
• Send pull requests
• Star us on the GitHub page

Citation

To cite ‘giscoR’ in publications use:

Hernangómez D (2025). giscoR: Download Map Data from GISCO API - Eurostat. 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 GISCO API - Eurostat},
  doi = {10.32614/CRAN.package.giscoR},
  author = {Diego Hernangómez},
  year = {2025},
  version = {0.9.9.9999},
  url = {https://ropengov.github.io/giscoR/},
  abstract = {Tools to download data from the GISCO (Geographic Information System of the Commission) Eurostat database <https://ec.europa.eu/eurostat/web/gisco>. Global and European map data available. This package is in no way officially related to or endorsed by Eurostat.},
}

General copyright

Eurostat’s general copyright notice and licence 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.

Source: https://ec.europa.eu/eurostat/web/gisco/geodata

Disclaimer

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