# \donttest{
grid <- gisco_get_grid(resolution = 20)
-#> Warning: GDAL Message 1: GPKG: unrecognized user_version=0x00000000 (0) on 'C:\Users\runneradmin\AppData\Local\Temp\RtmpGG6ZIU\giscoR\pkgdown\grid_20km_surf.gpkg'
+#> Warning: GDAL Message 1: GPKG: unrecognized user_version=0x00000000 (0) on 'C:\Users\runneradmin\AppData\Local\Temp\RtmpId6oBk\giscoR\pkgdown\grid_20km_surf.gpkg'
# If downloaded correctly proceed
diff --git a/reference/gisco_set_cache_dir.html b/reference/gisco_set_cache_dir.html
index 6c37be1c..f3500e8e 100644
--- a/reference/gisco_set_cache_dir.html
+++ b/reference/gisco_set_cache_dir.html
@@ -138,14 +138,14 @@
diff --git a/search.json b/search.json
index 57ee1ec7..fabc707c 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://ropengov.github.io/giscoR/articles/giscoR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Get started with giscoR","text":"Full site examples vignettes https://ropengov.github.io/giscoR/ giscoR package designed provide clean interaction GISCO API. Within Eurostat, GISCO responsible meeting European Commission’s geographical information needs 3 levels: European Union, member countries, regions. GISCO also provides variety shapefiles different formats, focusing specially European Union area, providing also worldwide shapefiles, country polygons, labels borders coastal lines. GISCO provides data different resolutions suitable representing small areas (01M, 03M) well lightweight datasets specially useful representing wider areas (10M, 20M, 60M). Shapefiles provided 3 different projections: EPSG 4326, 3035 3857. giscoR returns sf class objects, see https://r-spatial.github.io/sf/.","code":""},{"path":"https://ropengov.github.io/giscoR/articles/giscoR.html","id":"caching","dir":"Articles","previous_headings":"","what":"Caching","title":"Get started with giscoR","text":"giscoR provides dataset caching capability, set : file available locally, downloaded directory next time need corresponding data loaded local directory. experience problems downloading, can also manually download file GISCO API website store local directory.","code":"gisco_set_cache_dir(\"./path/to/location\")"},{"path":"https://ropengov.github.io/giscoR/articles/giscoR.html","id":"downloading-data","dir":"Articles","previous_headings":"","what":"Downloading data","title":"Get started with giscoR","text":"Please aware downloading provisions apply using GISCO data: data downloaded page used printed electronic publication, addition provisions applicable whole Eurostat website, data source acknowledged legend map introductory page publication following copyright notice: EN: © EuroGeographics administrative boundaries FR: © EuroGeographics pour les limites administratives DE: © EuroGeographics bezüglich der Verwaltungsgrenzen publications languages English, French German, translation copyright notice language publication shall used. intend use data commercially, please contact EuroGeographics information regarding licence agreements. function, gisco_attributions() guide topic. also provides attributions several languages.","code":"library(giscoR) gisco_attributions(lang = \"en\") #> [1] \"© EuroGeographics for the administrative boundaries\" gisco_attributions(lang = \"fr\") #> [1] \"© EuroGeographics pour les limites administratives\" gisco_attributions(lang = \"de\") #> [1] \"© EuroGeographics bezuglich der Verwaltungsgrenzen\""},{"path":"https://ropengov.github.io/giscoR/articles/giscoR.html","id":"basic-example","dir":"Articles","previous_headings":"","what":"Basic example","title":"Get started with giscoR","text":"examples data downloads Political map Asia can select specific countries name (language), ISO 3 codes Eurostat codes. restriction can’t mix country names, ISO3 Eurostat codes one single call. possible also combine different shapefiles, just set resolution epsg (optionally year) value: Political map North Africa","code":"library(sf) library(ggplot2) # Use ggplot for plotting asia <- gisco_get_countries(region = \"Asia\") ggplot(asia) + geom_sf(fill = \"cornsilk\", color = \"#887e6a\") + theme( panel.background = element_rect(fill = \"#fffff3\"), panel.border = element_rect(colour = \"#887e6a\", fill = NA, linewidth = 1.5), axis.text = element_text( family = \"serif\", colour = \"#887e6a\", face = \"bold\" ) ) africa_north <- gisco_get_countries( country = c( \"Morocco\", \"Argelia\", \"Libia\", \"Tunisia\", \"Egypt\" ), resolution = \"20\", epsg = \"4326\", year = \"2016\" ) # Coastal lines coast <- gisco_get_coastallines(resolution = \"20\", epsg = \"4326\", year = \"2016\") # Plot ggplot(coast) + geom_sf(color = \"grey80\") + geom_sf(data = africa_north, fill = \"grey30\", color = \"white\") + coord_sf(xlim = c(-13, 37), ylim = c(18.5, 40)) + facet_wrap(vars(NAME_ENGL), ncol = 2)"},{"path":"https://ropengov.github.io/giscoR/articles/giscoR.html","id":"thematic-maps-with-giscor","dir":"Articles","previous_headings":"","what":"Thematic maps with giscoR","title":"Get started with giscoR","text":"example giscoR can play nicely Eurostat data. plotting purposes use ggplot2 package however package handles sf objects (e.g. tmap, mapsf, leaflet, etc. used). Also colorspace rcartocolor packages recommended, provide great color palettes. Disposable income private households NUTS 2 regions (2021)","code":"# EU members library(dplyr) library(eurostat) nuts2 <- gisco_get_nuts( year = \"2021\", epsg = \"3035\", resolution = \"10\", nuts_level = \"2\" ) # Borders from countries borders <- gisco_get_countries(epsg = \"3035\", year = \"2020\", resolution = \"3\") eu_bord <- borders %>% filter(CNTR_ID %in% nuts2$CNTR_CODE) # Eurostat data - Disposable income pps <- get_eurostat(\"tgs00026\") %>% filter(TIME_PERIOD == \"2021-01-01\") #> indexed 0B in 0s, 0B/s indexed 2.15GB in 0s, 2.15GB/s nuts2_sf <- nuts2 %>% left_join(pps, by = \"geo\") %>% mutate( values_th = values / 1000, categ = cut(values_th, c(0, 15, 30, 60, 90, 120, Inf)) ) # Adjust the labels labs <- levels(nuts2_sf$categ) labs[1] <- \"< 15\" labs[6] <- \"> 120\" levels(nuts2_sf$categ) <- labs # Finally the plot ggplot(nuts2_sf) + # Background geom_sf(data = borders, fill = \"#e1e1e1\", color = NA) + geom_sf(aes(fill = categ), color = \"grey20\", linewidth = .1) + geom_sf(data = eu_bord, fill = NA, color = \"black\", linewidth = .15) + # Center in Europe: EPSG 3035 coord_sf(xlim = c(2377294, 6500000), ylim = c(1413597, 5228510)) + # Legends and color scale_fill_manual( values = hcl.colors(length(labs), \"Geyser\", rev = TRUE), # Label NA labels = function(x) { ifelse(is.na(x), \"No Data\", x) }, na.value = \"#e1e1e1\" ) + guides(fill = guide_legend(nrow = 1)) + theme_void() + theme( text = element_text(colour = \"grey0\"), panel.background = element_rect(fill = \"#97dbf2\"), panel.border = element_rect(fill = NA, color = \"grey10\"), plot.title = element_text(hjust = 0.5, vjust = -1, size = 12), plot.subtitle = element_text( hjust = 0.5, vjust = -2, face = \"bold\", margin = margin(b = 10, t = 5), size = 12 ), plot.caption = element_text( size = 8, hjust = 0.5, margin = margin(b = 2, t = 13) ), legend.text = element_text(size = 7, ), legend.title = element_text(size = 7), legend.position = \"bottom\", legend.direction = \"horizontal\", legend.text.position = \"bottom\", legend.title.position = \"top\", legend.key.height = rel(0.5), legend.key.width = unit(.1, \"npc\") ) + # Annotate and labels labs( title = \"Disposable income of private households (2021)\", subtitle = \"NUTS-2 level\", fill = \"euros (thousands)\", caption = paste0( \"Source: Eurostat\\n \", gisco_attributions() ) )"},{"path":"https://ropengov.github.io/giscoR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Diego Hernangómez. Author, maintainer, copyright holder. rOpenGov EuroGeographics. Copyright holder. administrative boundaries. Vincent Arel-Bundock. Copyright holder. gisco_countrycode dataset.","code":""},{"path":"https://ropengov.github.io/giscoR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hernangómez D (2024). giscoR: Download Map Data GISCO API - Eurostat. doi:10.32614/CRAN.package.giscoR, https://ropengov.github.io/giscoR/.","code":"@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