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 (FAQ) 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://dieghernan.github.io/giscoR/

Installation

Install giscoR from CRAN:

You can install the developing version of giscoR with:

library(remotes)
install_github("dieghernan/giscoR")

Alternatively, you can install giscoR using the r-universe:

# Enable this universe
options(repos = c(
  dieghernan = "https://dieghernan.r-universe.dev",
  CRAN = "https://cloud.r-project.org"
))


install.packages("giscoR")

Usage

This script highlights some features of giscoR:


library(giscoR)
library(sf)
library(dplyr)

# Different resolutions
DNK_res60 <- gisco_get_countries(resolution = "60", country = "DNK") %>%
  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")


DNK_all <- bind_rows(DNK_res60, DNK_res20, DNK_res10, DNK_res03)

# Plot ggplot2

library(ggplot2)

ggplot(DNK_all) +
  geom_sf(fill = "tomato") +
  facet_wrap(vars(res)) +
  theme_minimal()



# Labels and Lines available

labs <- gisco_get_countries(
  spatialtype = "LB",
  region = "Africa",
  epsg = "3857"
)

coast <- gisco_get_countries(
  spatialtype = "COASTL",
  epsg = "3857"
)

# For zooming
afr_bbox <- st_bbox(labs)

ggplot(coast) +
  geom_sf(col = "deepskyblue4", size = 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")]
  )

Labels

An example of a labeled map using ggplot2:


ITA <- gisco_get_nuts(country = "Italy", nuts_level = 1)

ggplot(ITA) +
  geom_sf() +
  geom_sf_text(aes(label = NAME_LATN)) +
  theme(axis.title = element_blank())

Thematic maps

An example of a thematic map plotted with the ggplot2 package. The information is extracted via the eurostat package. We would follow the fantastic approach presented by Milos Popovic on this post:


# Get shapes
nuts3 <- gisco_get_nuts(
  year = "2016",
  epsg = "3035",
  resolution = "3",
  nuts_level = "3"
)

# Group by NUTS by country and convert to lines
country_lines <- nuts3 %>%
  group_by(
    CNTR_CODE
  ) %>%
  summarise(n = n()) %>%
  st_cast("MULTILINESTRING")


# Use eurostat
library(eurostat)

popdens <- get_eurostat("demo_r_d3dens")
popdens <- popdens[popdens$time == "2018-01-01", ]


# Merge data
nuts3.sf <- merge(nuts3,
  popdens,
  by.x = "NUTS_ID",
  by.y = "geo",
  all.x = TRUE
)

# Breaks and labels

br <- c(0, 25, 50, 100, 200, 500, 1000, 2500, 5000, 10000, 30000)

nuts3.sf$values_cut <- cut(nuts3.sf$values,
  breaks = br,
  dig.lab = 5
)

labs_plot <- prettyNum(br[-1], big.mark = ",")


# Palette
pal <- hcl.colors(length(br) - 1, "Lajolla")


# Plot

ggplot(nuts3.sf) +
  geom_sf(aes(fill = values_cut), size = 0, color = NA, alpha = 0.9) +
  geom_sf(data = country_lines, col = "black", size = 0.1) +
  # Center in Europe: EPSG 3035
  coord_sf(
    xlim = c(2377294, 7453440),
    ylim = c(1313597, 5628510)
  ) +
  labs(
    title = "Population density in 2018",
    subtitle = "NUTS-3 level",
    caption = paste0(
      "Source: Eurostat, ", gisco_attributions(),
      "\nBased on Milos Popovic: https://milospopovic.net/how-to-make-choropleth-map-in-r/"
    )
  ) +
  scale_fill_manual(
    name = "people per sq. kilometer",
    values = pal,
    labels = labs_plot,
    drop = FALSE,
    guide = guide_legend(
      direction = "horizontal",
      keyheight = 0.5,
      keywidth = 2.5,
      title.position = "top",
      title.hjust = 0.5,
      label.hjust = .5,
      nrow = 1,
      byrow = TRUE,
      reverse = FALSE,
      label.position = "bottom"
    )
  ) +
  theme_void() +
  # Theme
  theme(
    plot.title = element_text(
      size = 20, color = pal[length(pal) - 1],
      hjust = 0.5, vjust = -10
    ),
    plot.subtitle = element_text(
      size = 14,
      color = pal[length(pal) - 1],
      hjust = 0.5, vjust = -15, face = "bold"
    ),
    plot.caption = element_text(
      size = 9, color = "grey60",
      hjust = 0.5, vjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    legend.text = element_text(
      size = 10,
      color = "grey20"
    ),
    legend.title = element_text(
      size = 11,
      color = "grey20"
    ),
    legend.position = "bottom"
  )

A note on caching

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 option:

options(gisco_cache_dir = "./path/to/location")

When this option is set, giscoR would look for the cached file and it will load it, speeding up the process.

You can also download manually the files (.geojson format) and store them on your local directory.

Contribute

Check the GitHub page for source code.

Contributions are very welcome:

Disclaimer

This package is in no way officially related to or endorsed by Eurostat.