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giscoR is an API package that helps to retrieve data from Eurostat - GISCO (the Geographic Information System of the COmmission). It also provides some lightweight data sets ready to use without downloading.
GISCO is a geospatial open data repository including several data sets as countries, coastal lines, labels or NUTS levels. The data sets are usually provided at several resolution levels (60M/20M/10M/03M/01M) and in 3 different projections (4326/3035/3857).
Note that the package does not provide metadata on the downloaded files, the information is available on the API webpage.
Full site with examples and vignettes on https://ropengov.github.io/giscoR/
Install giscoR from CRAN:
install.packages("giscoR")
You can install the developing version of giscoR with:
::install_github("rOpenGov/giscoR") remotes
Alternatively, you can install giscoR using the 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)
# Different resolutions
<- gisco_get_countries(resolution = "60", country = "DNK") %>%
DNK_res60 mutate(res = "60M")
<-
DNK_res20 gisco_get_countries(resolution = "20", country = "DNK") %>%
mutate(res = "20M")
<-
DNK_res10 gisco_get_countries(resolution = "10", country = "DNK") %>%
mutate(res = "10M")
<-
DNK_res03 gisco_get_countries(resolution = "03", country = "DNK") %>%
mutate(res = "03M")
<- bind_rows(DNK_res60, DNK_res20, DNK_res10, DNK_res03)
DNK_all
# Plot ggplot2
library(ggplot2)
ggplot(DNK_all) +
geom_sf(fill = "tomato") +
facet_wrap(vars(res)) +
theme_minimal()
# Labels and Lines available
<- gisco_get_countries(
labs spatialtype = "LB",
region = "Africa",
epsg = "3857"
)
<- gisco_get_countries(
coast spatialtype = "COASTL",
epsg = "3857"
)
# For zooming
<- st_bbox(labs)
afr_bbox
ggplot(coast) +
geom_sf(col = "deepskyblue4", linewidth = 3) +
geom_sf(data = labs, fill = "springgreen4", col = "darkgoldenrod1", size = 5, shape = 21) +
coord_sf(
xlim = afr_bbox[c("xmin", "xmax")],
ylim = afr_bbox[c("ymin", "ymax")]
)
An example of a labeled map using ggplot2:
<- gisco_get_nuts(country = "Italy", nuts_level = 1)
ITA
ggplot(ITA) +
geom_sf() +
geom_sf_text(aes(label = NAME_LATN)) +
theme(axis.title = element_blank())
An example of a thematic map plotted with the ggplot2 package. The information is extracted via the eurostat package (Lahti et al. 2017). We would follow the fantastic approach presented by Milos Popovic on this post:
We start by extracting the corresponding geographic data:
# Get shapes
<- gisco_get_nuts(
nuts3 year = "2021",
epsg = "3035",
resolution = "10",
nuts_level = "3"
)
# Group by NUTS by country and convert to lines
<- nuts3 %>%
country_lines group_by(
CNTR_CODE%>%
) summarise(n = n()) %>%
st_cast("MULTILINESTRING")
We now download the data from Eurostat:
# Use eurostat
library(eurostat)
<- get_eurostat("demo_r_d3dens") %>%
popdens filter(TIME_PERIOD == "2021-01-01")
By last, we merge and manipulate the data for creating the final plot:
# Merge data
<- nuts3 %>%
nuts3_sf left_join(popdens, by = "geo")
<- nuts3 %>%
nuts3_sf left_join(popdens, by = c("NUTS_ID" = "geo"))
# Breaks and labels
<- c(0, 25, 50, 100, 200, 500, 1000, 2500, 5000, 10000, 30000)
br <- prettyNum(br[-1], big.mark = ",")
labs
# Label function to be used in the plot, mainly for NAs
<- function(x) {
labeller_plot ifelse(is.na(x), "No Data", x)
}<- nuts3_sf %>%
nuts3_sf # Cut with labels
mutate(values_cut = cut(values, br, labels = labs))
# Palette
<- hcl.colors(length(labs), "Lajolla")
pal
# 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/"
) )
Some data sets (as Local Administrative Units - LAU, or high-resolution files) may have a size larger than 50MB. You can use giscoR to create your own local repository at a given local directory passing the following function:
gisco_set_cache_dir("./path/to/location")
You can also download manually the files (.geojson
format) and store them on your local directory.
Some packages recommended for visualization are:
Check the GitHub page for source code.
Contributions are very welcome:
To cite ‘giscoR’ in publications use:
Hernangómez D (2024). 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 = {2024},
version = {0.6.0},
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.},
}
When data downloaded from this page is used in any printed or electronic publication, in addition to any other provisions applicable to the whole Eurostat website, data source will have to be acknowledged in the legend of the map and in the introductory page of the publication with the following copyright notice:
- EN: © EuroGeographics for the administrative boundaries.
- FR: © EuroGeographics pour les limites administratives.
- DE: © EuroGeographics bezüglich der Verwaltungsgrenzen.
For publications in languages other than English, French or German, the translation of the copyright notice in the language of the publication shall be used.
If you intend to use the data commercially, please contact EuroGeographics for information regarding their licence agreements.
From GISCO Web
This package is in no way officially related to or endorsed by Eurostat.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.