--- title: "Get started" author: - name: Diego Hernangómez orcid: 0000-0001-8457-4658 description: First steps with mapSpain. vignette: > %\VignetteIndexEntry{Get started} %\VignetteEngine{quarto::html} %\VignetteEncoding{UTF-8} --- ## Introduction ::: callout-tip For more examples and vignettes, see the full site at . ::: [**mapSpain**](https://ropenspain.github.io/mapSpain/) provides administrative boundaries and static map tiles for Spain. **mapSpain** provides **sf** objects for Autonomous Communities and Cities, provinces, municipalities and NUTS levels in Spain. It also provides grid maps and other complementary geometries, such as the demarcation lines around the Canary Islands. **mapSpain** provides access to static map tiles from Spanish public administrations. Tiles can be represented on static maps with `mapSpain::esp_get_tiles()` or on an R [**leaflet**](https://rstudio.github.io/leaflet/) map using `mapSpain::addProviderEspTiles()`. **mapSpain** also includes a dictionary that translates Spanish subdivision names to English, Spanish, Catalan, Basque and Galician, and converts them to coding standards such as NUTS, ISO2 and INE codes. ## Caching **mapSpain** provides data and tile caching. Set a cache directory with: ``` r esp_set_cache_dir("./path/to/location") ``` **mapSpain** relies on [**giscoR**](https://ropengov.github.io/giscoR/) for downloading some files, and both packages can share cache settings. Using the same cache directory for both packages speeds up data loading in your session. ## Basic example These examples show key **mapSpain** workflows: ``` r library(mapSpain) library(ggplot2) country <- esp_get_spain() lines <- esp_get_can_box() ggplot(country) + geom_sf(fill = "cornsilk", color = "#887e6a") + labs(title = "Map of Spain") + theme( panel.background = element_rect(fill = "#fffff3"), panel.border = element_rect( colour = "#887e6a", fill = NA, ), text = element_text( family = "serif", face = "bold" ) ) ```
Example: map of Spain

Example: map of Spain

``` r # Plot provinces. andalucia <- esp_get_prov_siane("Andalucia") ggplot(andalucia) + geom_sf(fill = "darkgreen", color = "white") + theme_bw() ```
Example: provinces of Andalusia

Example: provinces of Andalusia

``` r # Plot municipalities. euskadi_ccaa <- esp_get_ccaa_siane("Euskadi") euskadi <- esp_get_munic_siane(region = "Euskadi") # Use the dictionary. euskadi$name_eu <- esp_dict_translate(euskadi$ine.prov.name, lang = "eu") ggplot(euskadi_ccaa) + geom_sf(fill = "grey50") + geom_sf(data = euskadi, aes(fill = name_eu)) + scale_fill_manual(values = c("red2", "darkgreen", "ivory2")) + labs( fill = "", title = "Euskal Autonomia Erkidegoko", subtitle = "Probintziak" ) + theme_void() + theme( plot.title = element_text(face = "bold"), plot.subtitle = element_text(face = "italic") ) ```
Example: municipalities of the Basque Country

Example: municipalities of the Basque Country

## Choropleth and label maps Analyze the distribution of women in each Autonomous Community or City with **ggplot2**: ``` r library(dplyr) census <- mapSpain::pobmun25 |> select(-name) # Extract Autonomous Community or City codes from the base dataset. codelist <- mapSpain::esp_codelist |> select(cpro, codauto) |> distinct() census_ccaa <- census |> left_join(codelist) |> # Summarize by Autonomous Community or City. group_by(codauto) |> summarise(pob25 = sum(pob25), men = sum(men), women = sum(women)) |> mutate( porc_women = women / pob25, porc_women_lab = paste0(round(100 * porc_women, 2), "%") ) # Merge into spatial data. ccaa_sf <- esp_get_ccaa() |> left_join(census_ccaa) can <- esp_get_can_box() # Plot with ggplot. library(ggplot2) ggplot(ccaa_sf) + geom_sf(aes(fill = porc_women), color = "grey70", linewidth = 0.3) + geom_sf(data = can, color = "grey70") + geom_sf_label( aes(label = porc_women_lab), fill = "white", alpha = 0.5, size = 3, linewidth = 0 ) + scale_fill_gradientn( colors = hcl.colors(10, "Blues", rev = TRUE), n.breaks = 10, labels = scales::label_percent(), guide = guide_legend(title = "% women", position = "inside") ) + theme_void() + theme(legend.position.inside = c(0.1, 0.6)) + labs(caption = "Source: CartoBase ANE 2006-2024 CC-BY 4.0 ign.es, INE") ```
Percentage of women by Autonomous Community or City (2025)

Percentage of women by Autonomous Community or City (2025)

## Thematic maps This example shows how **mapSpain** can be used to create thematic maps. For plotting, we use the [**ggplot2**](https://ggplot2.tidyverse.org/) package, though any package that works with `sf` objects, such as **tmap**, **mapsf** or **leaflet**, could also be used. ``` r # Calculate population density in Spain. library(sf) pop <- mapSpain::pobmun25 |> select(-name) munic <- esp_get_munic_siane(rawcols = TRUE) |> # Use the area field available in the SIANE data. mutate(area_km2 = st_area_sh * 10000) munic_pop <- munic |> left_join(pop) |> mutate(dens = pob25 / area_km2) br <- c(-Inf, 10, 25, 100, 200, 500, 1000, 5000, 10000, Inf) munic_pop$cuts <- cut(munic_pop$dens, br) ggplot(munic_pop) + geom_sf(aes(fill = cuts), color = NA, linewidth = 0) + scale_fill_manual( values = c("grey5", hcl.colors(length(br) - 2, "Spectral")), labels = prettyNum(c(0, br[-1]), big.mark = ","), guide = guide_legend( title = "Pop. per km2", direction = "horizontal", nrow = 1 ) ) + labs(title = "Population density in Spain (2025)") + theme_void() + theme( plot.title = element_text(hjust = 0.5), plot.background = element_rect(fill = "black"), text = element_text(colour = "white"), legend.position = "bottom", legend.title.position = "top", legend.text.position = "bottom", legend.key.width = unit(30, "pt") ) + labs(caption = "Source: CartoBase ANE 2006-2024 CC-BY 4.0 ign.es, INE") ```
Population density in Spain (2025)

Population density in Spain (2025)

## mapSpain and giscoR If you need to plot Spain alongside other countries, use the [**giscoR**](https://ropengov.github.io/giscoR/) package, which is installed as a dependency of **mapSpain**. Here is a basic example: ``` r library(giscoR) # Set the same resolution for a perfect fit. res <- 3 all_countries <- gisco_get_countries(resolution = res) |> st_transform(3035) eu_countries <- gisco_get_countries( resolution = res, region = "EU" ) |> st_transform(3035) ccaa <- esp_get_ccaa( moveCAN = FALSE, resolution = res ) |> st_transform(3035) # Plot. ggplot(all_countries) + geom_sf(fill = "#DFDFDF", color = "#656565") + geom_sf(data = eu_countries, fill = "#FDFBEA", color = "#656565") + geom_sf(data = ccaa, fill = "#C12838", color = "grey80", linewidth = 0.1) + # Center on Europe: EPSG 3035. coord_sf( xlim = c(2377294, 7453440), ylim = c(1313597, 5628510) ) + theme( panel.background = element_blank(), panel.grid = element_line( colour = "#DFDFDF", linetype = "dotted" ) ) + labs(caption = giscoR::gisco_attributions("es")) ```
mapSpain and giscoR example

mapSpain and giscoR example

## Working with static map tiles **mapSpain** provides an interface for working with static map tiles. It can download tiles as `.png` or `.jpeg`, depending on the tile service, and use them alongside your **sf** objects. **mapSpain** also includes a plugin for [**leaflet**](https://rstudio.github.io/leaflet/) maps, which allows you to add several basemaps to interactive maps. The services are implemented with the [leaflet-providersESP](https://dieghernan.github.io/leaflet-providersESP/) Leaflet plugin. All available providers are listed there. ::: callout-note When working with static map tiles, set `moveCAN = FALSE` in `esp_get_*()` functions. See **Displacing the Canary Islands** in `esp_move_can()`. :::