Registering map data with ‘cartographer’


If you are often working with spatial data concerning a specific region, it might make sense to bundle up the map data along with its registration as an R package. This is true even if the data is only for internal use.

For a general introduction to R packaging, see the R packaging book. The chapter on packaging data in particular contains many relevant hints.

General guidelines

Your source dataset needs to be transformed into a {sf} data frame. The sf::read_sf() can ingest many popular spatial data formats Make use of usethis::use_data_raw() and usethis::use_data() to prepare the data.

Your package will need to inform {cartographer} of the data by calling register_map(). This needs to happen after your package is loaded, so the base::.onLoad() hook is the appropriate place:

# in R/zzz.R
.onLoad <- function(libname, pkgname) {
    data = rnaturalearth::ne_states(country = "united kingdom", returnclass = "sf"),
    feature_column = "name_en",
    outline = rnaturalearth::ne_countries(
      country = "united kingdom", returnclass = "sf", scale = "large"

The hook needs to come last when your package is evaluated, so the convention is to include it in a file called R/zzz.R, which is normally alphabetically last. The registration should at least include the map data and the name of the column that labels the features. It can also contain an alias mapping if there are alternative names or abbreviations for places that would plausibly appear in datasets.

Reducing file size

Spatial datasets can often be quite large as they contain high resolution data. This can especially be a problem if attempting to commit the data to git or publish your package to CRAN (where 1 MB is the soft limit for the total compressed size of packages).

Consider removing extra columns from the spatial dataset so that only the geometry column and the name column remain.

A useful pattern to reduce the size is to simplify the geometry to a specified resolution:

# Preserve the original coordinate reference system.
crs_orig <- sf::st_crs(high_res_sf_data_frame)

# Convert to a more suitable CRS for manipulation. Note that the lat_ts argument
# here is the "latitude of true scale", i.e. the latitude at which scale will be
# the least distorted. Adjust this based on your data.
crs_working <- sf::st_crs("+proj=eqc +lat_ts=34 units=m")

# Choose a resolution: features smaller than this scale will be lost.
tolerance_m <- 1000L

low_res <- high_res_sf_data_frame |>
  sf::st_transform(crs_working) |>
  sf::st_simplify(dTolerance = tolerance_m) |>

# Compare the size after reducing the resolution:

See the documentation for the equidistant cylindrical projection for details of its configuration.

You may also wish to remove holes in features:

new_geom <- geom |>
  sf::st_transform(crs_working) |>
  sf::st_union() |>
  nngeo::st_remove_holes() |>