geom_map | R Documentation |
Display polygons as a map. This is meant as annotation, so it does not
affect position scales. Note that this function predates the geom_sf()
framework and does not work with sf geometry columns as input. However,
it can be used in conjunction with geom_sf()
layers and/or
coord_sf()
(see examples).
geom_map(
mapping = NULL,
data = NULL,
stat = "identity",
...,
map,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data for this layer.
When using a
|
... |
Other arguments passed on to
|
map |
Data frame that contains the map coordinates. This will
typically be created using |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
geom_map()
understands the following aesthetics (required aesthetics are in bold):
map_id
alpha
colour
fill
group
linetype
linewidth
subgroup
Learn more about setting these aesthetics in vignette("ggplot2-specs")
.
# First, a made-up example containing a few polygons, to explain
# how `geom_map()` works. It requires two data frames:
# One contains the coordinates of each polygon (`positions`), and is
# provided via the `map` argument. The other contains the
# other the values associated with each polygon (`values`). An id
# variable links the two together.
ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3"))
values <- data.frame(
id = ids,
value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5)
)
positions <- data.frame(
id = rep(ids, each = 4),
x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3,
0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3),
y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5,
2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2)
)
ggplot(values) +
geom_map(aes(map_id = id), map = positions) +
expand_limits(positions)
ggplot(values, aes(fill = value)) +
geom_map(aes(map_id = id), map = positions) +
expand_limits(positions)
ggplot(values, aes(fill = value)) +
geom_map(aes(map_id = id), map = positions) +
expand_limits(positions) + ylim(0, 3)
# Now some examples with real maps
if (require(maps)) {
crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)
# Equivalent to crimes %>% tidyr::pivot_longer(Murder:Rape)
vars <- lapply(names(crimes)[-1], function(j) {
data.frame(state = crimes$state, variable = j, value = crimes[[j]])
})
crimes_long <- do.call("rbind", vars)
states_map <- map_data("state")
# without geospatial coordinate system, the resulting plot
# looks weird
ggplot(crimes, aes(map_id = state)) +
geom_map(aes(fill = Murder), map = states_map) +
expand_limits(x = states_map$long, y = states_map$lat)
# in combination with `coord_sf()` we get an appropriate result
ggplot(crimes, aes(map_id = state)) +
geom_map(aes(fill = Murder), map = states_map) +
# crs = 5070 is a Conus Albers projection for North America,
# see: https://epsg.io/5070
# default_crs = 4326 tells coord_sf() that the input map data
# are in longitude-latitude format
coord_sf(
crs = 5070, default_crs = 4326,
xlim = c(-125, -70), ylim = c(25, 52)
)
ggplot(crimes_long, aes(map_id = state)) +
geom_map(aes(fill = value), map = states_map) +
coord_sf(
crs = 5070, default_crs = 4326,
xlim = c(-125, -70), ylim = c(25, 52)
) +
facet_wrap(~variable)
}
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