Cartographer comes with one example dataset registered (the
map of North Carolina provided by the {sf}
package where
the feature names correspond to counties). If you install the
{maps}
package, some additional maps are available.
library(cartographer)
# list registered datasets
feature_types()
#> [1] "maps.italy" "rnaturalearth.countries_hires"
#> [3] "maps.lakes" "rnaturalearth.countries"
#> [5] "rnaturalearth.australia" "maps.nz"
#> [7] "maps.world" "maps.state"
#> [9] "sf.nc" "maps.france"
# list feature names in one of the datasets
head(feature_names("sf.nc"))
#> [1] "Ashe" "Alleghany" "Surry" "Currituck" "Northampton"
#> [6] "Hertford"
Our example dataset has a column called county
with the
name of the county and a column type
that is one of two
values. We’d like to draw a map that shows us where the entries are by
type.
head(nc_type_example_1)
#> county type
#> 1 BERTIE B
#> 2 BERTIE A
#> 3 BERTIE A
#> 4 PAMLICO B
#> 5 WARREN B
#> 6 PAMLICO B
We want to convert our data frame into a spatial data frame, then
we’ll be able to use standard {ggplot2}
tools to draw our
map. Cartographer just needs to know which of its maps we’re using
("sf.nc"
, although it’s able to guess that if we don’t
specify it), and which column has the feature names
(county
):
add_geometry(nc_type_example_1, county, feature_type = "sf.nc")
#> Error in `resolve_feature_names()`:
#> ! location contains unexpected values
#> ✖ The unknown values are PAMILCO.
#> ℹ Expected sf.nc names like Ashe, Alleghany, and Surry.
#> ℹ See feature_names('sf.nc') for the full list.
What happened? The matching of county names was case-insensitive, so
that wasn’t the issue; it looks like our data contained a typo. One of
the rows contains "PAMILCO"
instead of
"PAMLICO"
in the county
column.
library(dplyr, warn.conflicts = FALSE)
# cleaned dataset
nc_type_example_1_fixed <-
nc_type_example_1 |>
mutate(county = case_match(county, "PAMILCO" ~ "PAMLICO", .default = county))
add_geometry(nc_type_example_1_fixed, county, feature_type = "sf.nc")
#> Simple feature collection with 50 features and 2 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -82.07776 ymin: 34.98222 xmax: -76.50713 ymax: 36.55629
#> Geodetic CRS: NAD27
#> First 10 features:
#> county type geometry
#> 1 BERTIE B MULTIPOLYGON (((-76.78307 3...
#> 2 BERTIE A MULTIPOLYGON (((-76.78307 3...
#> 3 BERTIE A MULTIPOLYGON (((-76.78307 3...
#> 4 PAMLICO B MULTIPOLYGON (((-76.94324 3...
#> 5 WARREN B MULTIPOLYGON (((-78.30876 3...
#> 6 PAMLICO B MULTIPOLYGON (((-76.94324 3...
#> 7 NORTHAMPTON B MULTIPOLYGON (((-77.21767 3...
#> 8 PAMLICO A MULTIPOLYGON (((-76.94324 3...
#> 9 PAMLICO B MULTIPOLYGON (((-76.94324 3...
#> 10 PAMLICO B MULTIPOLYGON (((-76.94324 3...
Now that we have our spatial geometry added, we can go ahead and plot
it, adding a layer that uses the full "sf.nc"
dataset to
give us the base map:
library(ggplot2)
nc_type_example_1_fixed |>
count(county, type) |>
add_geometry(county, feature_type = "sf.nc") |>
ggplot() +
geom_sf(data = map_sf("sf.nc")) +
geom_sf(aes(fill = n)) +
facet_wrap(vars(type))
The package {ggautomap}
uses cartographer to provide
some new {ggplot2}
geoms that transparently attach the map
data.