ggautomap

ggautomap provides ggplot2 geometries that make use of cartographer, a framework for matching place names with map data. With ggautomap your input dataset doesn’t need to be spatially aware: the geometries will automatically attach the map data (providing it’s been registered with cartographer).

See the ‘Getting started’ article, vignette("ggautomap"), for some recipes for different types of plots.

Installation

You can install ggautomap like so:

# CRAN release
install.packages('ggautomap')

# development version
install.packages('ggautomap', repos = c('https://cidm-ph.r-universe.dev', 'https://cloud.r-project.org'))

Example

Let’s use the example dataset from cartographer:

library(cartographer)

head(nc_type_example_2)
#>      county type
#> 1    MARTIN    A
#> 2  ALAMANCE    B
#> 3    BERTIE    A
#> 4   CHATHAM    B
#> 5   CHATHAM    B
#> 6 HENDERSON    B

A possible workflow is to use cartographer::add_geometry() to convert this into a spatial data frame and then use ggplot2::geom_sf() to draw it.

ggautomap instead provides geoms that do this transparently as needed, so you don’t need to do a lot of boilerplate to wrangle the data into the right form before handing it off to the plotting code.

library(ggplot2)
library(ggautomap)

ggplot(nc_type_example_2, aes(location = county)) +
  geom_boundaries(feature_type = "sf.nc") +
  geom_geoscatter(aes(colour = type), size = 0.5) +
  coord_automap(feature_type = "sf.nc")

ggplot(nc_type_example_2, aes(location = county)) +
  geom_choropleth() +
  geom_boundaries(feature_type = "sf.nc") +
  scale_fill_steps(low = "#e6f9ff", high = "#00394d", na.value = "white") +
  coord_automap(feature_type = "sf.nc")