This vignette will explore some of the more advanced mapping features
of usmap
. Before continuing, be sure to check out Mapping the US as that will cover more of the
basics of plotting US maps and styling them with
ggplot2
.
As of usmap 0.4.0
, maps with state labels can be
created:
usmap 0.5.0
adds the ability to add county labels:
Labels can be colored using the label_color
parameter:
ggplot2
aesthetic mapping parametersParameters used by the map’s aesthetic mapping
(ggplot2::aes
) can be passed directly via
plot_usmap
by adding the parameters anywhere at the call
site:
usmap::plot_usmap("counties",
include = c("MA", "CT", "RI"),
labels = TRUE, label_color = "blue",
fill = "yellow", alpha = 0.25, color = "orange", linewidth = 2)
Notice in this case we set the fill
and
alpha
parameters to fill in the counties with a
semi-transparent yellow color.
The following parameters are supported:
fill
: fill color of the state/county polygonsalpha
: transparency of the state/county polygon fill
colorscolor
/colour
: line color of the
state/county polygonslinewidth
: thickness of the state/county polygon
linesusmap
projectionData sets with longitude and latitude coordinates can be transformed
to match the projection used in usmap
(Albers Equal Area
projection). This is convenient for plotting location-specific data and
values using ggplot2
layers such as geom_point
and geom_label
.
The projection used by usmap
can also be accessed by
using usmap_crs()
:
usmap::usmap_crs()
#> Coordinate Reference System:
#> User input: EPSG:9311
#> wkt:
#> PROJCRS["NAD27 / US National Atlas Equal Area",
#> BASEGEOGCRS["NAD27",
#> DATUM["North American Datum 1927",
#> ELLIPSOID["Clarke 1866",6378206.4,294.978698213898,
#> LENGTHUNIT["metre",1]]],
#> PRIMEM["Greenwich",0,
#> ANGLEUNIT["degree",0.0174532925199433]],
#> ID["EPSG",4267]],
#> CONVERSION["US National Atlas Equal Area",
#> METHOD["Lambert Azimuthal Equal Area (Spherical)",
#> ID["EPSG",1027]],
#> PARAMETER["Latitude of natural origin",45,
#> ANGLEUNIT["degree",0.0174532925199433],
#> ID["EPSG",8801]],
#> PARAMETER["Longitude of natural origin",-100,
#> ANGLEUNIT["degree",0.0174532925199433],
#> ID["EPSG",8802]],
#> PARAMETER["False easting",0,
#> LENGTHUNIT["metre",1],
#> ID["EPSG",8806]],
#> PARAMETER["False northing",0,
#> LENGTHUNIT["metre",1],
#> ID["EPSG",8807]]],
#> CS[Cartesian,2],
#> AXIS["easting (X)",east,
#> ORDER[1],
#> LENGTHUNIT["metre",1]],
#> AXIS["northing (Y)",north,
#> ORDER[2],
#> LENGTHUNIT["metre",1]],
#> USAGE[
#> SCOPE["Statistical analysis."],
#> AREA["United States (USA) - onshore and offshore."],
#> BBOX[15.56,167.65,74.71,-65.69]],
#> ID["EPSG",9311]]
A convenience method called usmap_transform
is provided
that transforms a data.frame
containing longitude/latitude
columns to use this projection. (Currently, only
data.frame
s are supported. Other structures may be
supported in the future.)
Here is an example using the provided earthquakes
dataset:
library(usmap)
library(ggplot2)
eq_transformed <- usmap_transform(earthquakes)
plot_usmap() +
geom_sf(data = eq_transformed, aes(size = mag),
color = "red", alpha = 0.25) +
labs(title = "US Earthquakes",
subtitle = "Source: USGS, Jan 1 to Jun 30 2019",
size = "Magnitude") +
theme(legend.position = "right")
And a more comprehensive example using the provided
citypop
dataset:
library(usmap)
library(ggplot2)
cities_t <- usmap_transform(citypop)
plot_usmap(fill = "yellow", alpha = 0.25) +
geom_sf(data = cities_t,
aes(size = city_pop),
color = "purple", alpha = 0.5) +
ggrepel::geom_label_repel(data = cities_t,
aes(label = most_populous_city, geometry = geometry),
size = 3, alpha = 0.8,
label.r = unit(0.5, "lines"), label.size = 0.5,
segment.color = "red", segment.size = 1,
stat = "sf_coordinates", seed = 1002,
max.overlaps = 20) +
scale_size_continuous(range = c(1, 16),
label = scales::comma) +
labs(title = "Most Populous City in Each US State",
subtitle = "Source: US Census 2010",
size = "City Population") +
theme(legend.position = "right")
Here is an example of transforming and plotting another
sf
object on the map, using the provided
rivers
dataset. In this example the width of the river
corresponds to its length, and the color indicates the river system it
belongs to.
library(usmap)
library(ggplot2)
rivers_t <- usmap_transform(usrivers)
plot_usmap("counties", color = "gray80") +
geom_sf(data = rivers_t, aes(linewidth = Shape_Length, color = SYSTEM, fill = SYSTEM)) +
scale_linewidth_continuous(range = c(0.3, 1.8), guide = "none") +
scale_color_discrete(guide = "none") +
labs(title = "Major Rivers in the United States",
subtitle = "Source: ESRI 2010",
fill = "River System") +
theme(legend.position = "right")