We can load the
happign package, and some additional
packages we will need (
sf to manipulate spatial data and
tmap to create maps)
happign use two web service from IGN :
To download data from IGN web services at least three elements are needed :
API keys can be directly retrieved on the IGN website from
the expert web services (I recommend you at this point to go and
have a look) or with
As for API key, it is possible to find the names of available layers from the expert web services of the IGN. For example, the first layer name in WFS format for “Administratif” category is “ADMINEXPRESS-COG-CARTO.LATEST:arrondissement”
Again, all layer’s name can be accessed from R with the
get_layers_metadata() function. This one connects directly
to the IGN site which allows to have the last updated resources. It can
be used for WMS and WFS :
administratif_wfs <- get_layers_metadata(apikey = "administratif", data_type = "wfs") administratif_wms <- get_layers_metadata(apikey = "administratif", data_type = "wms") head(administratif_wfs) #> Name #> 1 ADMINEXPRESS-COG-CARTO.LATEST:arrondissement #> 2 ADMINEXPRESS-COG-CARTO.LATEST:arrondissement_municipal #> 3 ADMINEXPRESS-COG-CARTO.LATEST:canton #> 4 ADMINEXPRESS-COG-CARTO.LATEST:chflieu_arrondissement_municipal #> 5 ADMINEXPRESS-COG-CARTO.LATEST:chflieu_commune #> 6 ADMINEXPRESS-COG-CARTO.LATEST:chflieu_commune_associee_ou_deleguee #> Title #> 1 ADMINEXPRESS-COG-CARTO.LATEST:arrondissement.title #> 2 ADMINEXPRESS-COG-CARTO.LATEST:arrondissement_municipal.title #> 3 ADMINEXPRESS-COG-CARTO.LATEST:canton.title #> 4 ADMINEXPRESS-COG-CARTO.LATEST:chflieu_arrondissement_municipal.title #> 5 ADMINEXPRESS-COG-CARTO.LATEST:chflieu_commune.title #> 6 ADMINEXPRESS-COG-CARTO.LATEST:chflieu_commune_associee_ou_deleguee.title #> Abstract #> 1 édition 2023 #> 2 édition 2023 #> 3 édition 2023 #> 4 édition 2023 #> 5 édition 2023 #> 6 édition 2023
Now that we know how to get an API key and layer name, it only takes a few lines to get plethora of resources. For the example we will look at the beautiful town of Penmarch in France. A part of this town is stored as a shape in happign.
get_wfs can be used to download borders :
penmarch_borders <- get_wfs(x = penmarch, apikey = "administratif", layer = "LIMITES_ADMINISTRATIVES_EXPRESS.LATEST:commune") #> Features downloaded : 1 # Checking result tm_shape(penmarch_borders)+ tm_polygons(alpha = 0, lwd = 2)+ tm_shape(penmarch)+ tm_polygons(col = "red")+ tm_add_legend(type = "fill", border.col = "black", border.lwd =2, col = NA, labels = "border from get_wfs")+ tm_add_legend(type = "fill", col = "red", labels = "penmarch shape from happign package")+ tm_layout(main.title = "Penmarch borders from IGN", main.title.position = "center", legend.position = c(0.7, -0.1), outer.margins = c(0.1, 0,0,0), frame = FALSE) #> Legend labels were too wide. The labels have been resized to 0.63. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
It’s as simple as that! Now you have to rely on your curiosity to explore the multiple possibilities that IGN offers. For example, who has never wondered how many road junctions there are in Penmarch?
Spoiler : there are 192 of them !
dikes <- get_wfs(x = penmarch_borders, apikey = get_apikeys(), #cartovecto layer = "BDCARTO_BDD_WLD_WGS84G:noeud_routier", spatial_filter = "intersects") #> Features downloaded : 192 # Checking result tm_shape(penmarch_borders) + # Borders of penmarch tm_borders(lwd = 2) + tm_shape(dikes) + # Point use to retrieve data tm_symbols(col = "red", shape = 13, size = 0.3) + tm_add_legend(type = "symbol", shape = 13, label = "Road junction", col = "red") + tm_layout(main.title = "Road nodes recorded by the IGN in Penmarch", main.title.position = "center", legend.position = c("right", "bottom"), frame = FALSE)
For raster, the process is the same, but with the function
get_wms_raster(), but you need to specify the resolution
(note that it must be in the same coordinate system as the crs
parameter). There’s plenty of elevation resources inside “altimetrie”
category. A basic one is the Digital Elevation Model (DEM or MNT in
French). Borders of Penmarch are used to download the DEM.
layers_metadata <- get_layers_metadata("altimetrie", "wms") dem_layer <- layers_metadata[2, 1] #LEVATION.ELEVATIONGRIDCOVERAGE mnt <- get_wms_raster(x = penmarch_borders, apikey = "altimetrie", layer = dem_layer, res = 25, crs = 2154) #> 0...10...20...30...40...50...60...70...80...90...100 - done. #> Raster is saved at : #> C:\Users\PAUL\AppData\Local\Temp\RtmpK29L2a\file12e45e89658a.tif mnt[mnt < 0] <- NA # remove negative values in case of singularity tm_shape(mnt) + tm_raster(title = "Elevation [m]") + tm_shape(penmarch_borders)+ tm_borders(lwd = 2)+ tm_layout(main.title = "DEM of Penmarch", main.title.position = "center", legend.position = c("right", "bottom"), legend.bg.color = "white", legend.bg.alpha = 0.7)
SpatRasterobject from the
terrapackage. To learn more about conversion between other raster type in R go check this out.