https://github.com/ropengov/giscor
Download geospatial data from GISCO API - Eurostat
https://github.com/ropengov/giscor
api-wrapper cran eurostat eurostat-data ggplot2 gis gisco r r-package ropengov rstats spatial thematic-maps
Last synced: 20 days ago
JSON representation
Download geospatial data from GISCO API - Eurostat
- Host: GitHub
- URL: https://github.com/ropengov/giscor
- Owner: rOpenGov
- License: gpl-3.0
- Created: 2020-09-23T19:40:44.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-10-15T06:36:07.000Z (over 1 year ago)
- Last Synced: 2024-10-29T22:31:51.751Z (over 1 year ago)
- Topics: api-wrapper, cran, eurostat, eurostat-data, ggplot2, gis, gisco, r, r-package, ropengov, rstats, spatial, thematic-maps
- Language: R
- Homepage: https://ropengov.github.io/giscoR/
- Size: 121 MB
- Stars: 72
- Watchers: 4
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Citation: CITATION.cff
- Codemeta: codemeta.json
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
tidy = "styler",
comment = "#>",
fig.path = "man/figures/README-",
warning = FALSE,
message = FALSE,
dev = "ragg_png",
dpi = 150,
out.width = "100%"
)
```
# giscoR 
[](https://ropengov.org/)
[](https://CRAN.R-project.org/package=giscoR)
[](https://cran.r-project.org/web/checks/check_results_giscoR.html)
[](https://CRAN.R-project.org/package=giscoR)
[](https://ropengov.r-universe.dev/giscoR)
[](https://github.com/rOpenGov/giscoR/actions/workflows/check-full.yaml)
[](https://github.com/rOpenGov/giscoR/actions/workflows/rhub.yaml)
[](https://app.codecov.io/gh/ropengov/giscoR)
[](https://www.codefactor.io/repository/github/ropengov/giscor)
[](https://doi.org/10.32614/CRAN.package.giscoR)
[](https://www.repostatus.org/#active)
[**giscoR**](https://ropengov.github.io/giscoR//) is an **R** package that
provides a simple interface to [GISCO](https://ec.europa.eu/eurostat/web/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**](https://CRAN.R-project.org/package=giscoR):
```{r, eval=FALSE}
install.packages("giscoR")
```
You can install the development version of **giscoR** with:
```{r, eval=FALSE}
# install.packages("pak")
pak::pak("rOpenGov/giscoR")
```
Alternatively, you can install **giscoR** via
[r-universe](https://ropengov.r-universe.dev/giscoR):
```{r, eval=FALSE}
install.packages("giscoR", repos = c("https://ropengov.r-universe.dev", "https://cloud.r-project.org"))
```
## Quick Example
This script highlights some features of **giscoR** :
```{r resolution-map, fig.alt="The Netherlands boundaries at different resolutions"}
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)
# 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](https://milospopovic.net/) in [this
post](https://milospopovic.net/how-to-make-choropleth-map-in-r/).
We start by extracting the corresponding geographic data:
```{r euroex, fig.asp=1.1}
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:
```{r}
# 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:
```{r thematic-map, fig.asp=1.1, fig.alt="Population density in 2021"}
# 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:
```{r, eval=FALSE}
gisco_set_cache_dir("./path/to/location")
```
Files will be stored locally for faster access.
## Contribute
Check the GitHub page for [source code](https://github.com/rOpenGov/giscoR/).
Contributions are welcome:
- [Use issue tracker](https://github.com/rOpenGov/giscoR/issues) for feedback
and bug reports.
- [Send pull requests](https://github.com/rOpenGov/giscoR/)
- [Star us on the GitHub page](https://github.com/rOpenGov/giscoR)
## Citation
```{r echo=FALSE, results='asis'}
print(citation("giscoR"), bibtex = FALSE)
```
A BibTeX entry for LaTeX users is
```{r echo=FALSE, comment=""}
toBibtex(citation("giscoR"))
```
## General copyright
> [Eurostat's general copyright notice and licence
> policy](https://ec.europa.eu/eurostat/web/main/help/copyright-notice) 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](https://ec.europa.eu/eurostat/web/gisco/geodata/administrative-units)
> and [statistical
> units](https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units) for
> more details.
>
> Source:
## Disclaimer
This package is neither affiliated with nor endorsed by Eurostat. The authors
are not responsible for any misuse of the data.