maposm

The OpenStreetMap database provides numerous and very detailed geographic layers on a global scale. To obtain synthetic information that can be used for cartographic purposes, layers must be selected, simplified, merged, or modified.
The maposm package provides a set of composite layers that are built from extractions performed with the osmdata package (which itself uses the Overpass API).

The resulting layers are:

• Urban areas
• Buildings
• Green spaces
• Main roads
• Secondary roads
• Railroads
• Water bodies

Installation

You can install maposm from the r-universe.

install.packages("maposm", repos = "https://riatelab.r-universe.dev")

Demo

om_get() is used to download, merge and simplify the geographic layers.
om_map() can be used to map the layers.

library(maposm)
#> Data © OpenStreetMap contributors, ODbL 1.0. https://www.openstreetmap.org/copyright.
#> Maps based on OpenStreetMap data should cite "© OpenStreetMap contributors" as the data source.
castries = om_get(x = c(-60.9903, 14.0096), r = 2000)
#> Getting urban areas: 0.878 sec elapsed
#> Getting buildings: 6.82 sec elapsed
#> Getting green areas: 1.393 sec elapsed
#> Getting roads: 0.557 sec elapsed
#> Getting streets: 0.732 sec elapsed
#> Getting railways: 0.459 sec elapsed
#> Getting water bodies: 11.854 sec elapsed
om_map(x = castries, title = "Castries, Saint Lucia", theme = "light")

Several themes are available to map the layers (“light”, “dark”, “grey” and “pizza”).

om_map(x = castries, title = "Castries, Saint Lucia", theme = "grey")

You can also use an sf object to extract the layers and your prefered library to map the layers.

library(mapsf)
roseau = st_as_sf(data.frame(lat = 15.300, lon = -61.388), 
                   coords = c("lon", "lat"), 
                   crs = "EPSG:4326") |>
  st_transform("EPSG:3857") |>
  st_buffer(dist = 500, nQuadSegs = 2) |> 
  om_get(quiet = TRUE)

mf_map(roseau$zone, col = "#f2efe9", border = NA, add = FALSE)
mf_map(roseau$urban, col = "#e0dfdf", border = "#e0dfdf", lwd = .5, add = TRUE)
mf_map(roseau$green, col = "#c8facc", border = "#c8facc", lwd = .5, add = TRUE)
mf_map(roseau$water, col = "#aad3df", border = "#aad3df", lwd = .5, add = TRUE)
mf_map(roseau$railway, col = "grey50", lty = 2, lwd = .2, add = TRUE)
mf_map(roseau$road, col = "white", border = "white", lwd = .5, add = TRUE)
mf_map(roseau$street, col = "white", border = "white", lwd = .5, add = TRUE)
mf_map(roseau$building, col = "#d9d0c9", border = "#c6bab1", lwd = .5, add = TRUE)
mf_map(roseau$zone, col = NA, border = "#c6bab1", lwd = 4, add = TRUE)
mf_credits(txt = "\ua9 OpenStreetMap contributors")
mf_scale(size = 100, scale_units = "m")
mf_title("Roseau, Dominique")

om_write() can be used to write the layers to a geopackage file.
om_read() can be used to read the geopackages.

# save
om_write(x = roseau, filename = "roseau.gpkg")
# import
om_read(x = "roseau.gpkg")

Alternatives

• osmdata to access to OSM data through the overpass API
• osmplotr from the author of osmdata
• osmextract for large extracts from OSM
• prettymap is a full featured Python library
• mapBliss to plot flight paths and road trips on OSM map
• maptiles to download (raster) OSM map tiles

Community Guidelines

One can contribute to the package through pull requests and report issues or ask questions here. This project uses conventional commits and semantic versioning.