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")