View code used to generate these plots: resources/examples.R
Typically in R it is difficult to create nice US choropleths that include Alaska and Hawaii. The functions presented here attempt to elegantly solve this problem by manually moving these states to a new location and providing a fortified data frame for mapping and visualization. This allows the user to easily add data to color the map.
The shape files that we use to plot the maps in R are located in the usmapdata package. These are generated from the US Census Bureau cartographic boundary files. Maps at both the state and county levels are included for convenience.
| Date | usmap version |
Shape File Year | Link |
|---|---|---|---|
| February 27, 2022 | 0.6.0 | 2020 | 🔗 |
| June 3, 2018 | 0.3.0 | 2017 | 🔗 |
| January 29, 2017 | 0.1.0 | 2015 | 🔗 |
To install from CRAN (recommended), run the following code in an R console:
install.packages("usmap")To install the package from this repository, run the following code in an R console:
# install.package("devtools")
devtools::install_github("pdil/usmap")Installing using devtools::install_github will provide the most recent developer build of usmap.
To begin using usmap, import the package using the library command:
library(usmap)To read the package vignettes, which explain helpful uses of the package, use vignette:
vignette(package = "usmap")
vignette("introduction", package = "usmap")
vignette("mapping", package = "usmap")
vignette("advanced-mapping", package = "usmap")For further help with this package, open an issue or ask a question on Stackoverflow with the usmap tag.
- Obtain map with certain region breakdown
state_map <- us_map(regions = "states")str(state_map)
#> 'data.frame': 13696 obs. of 9 variables:
#> $ x : num 1093752 1093244 1093125 1092939 1092914 ...
#> $ y : num -1378545 -1374233 -1360891 -1341458 -1338952 ...
#> $ order: int 1 2 3 4 5 6 7 8 9 10 ...
#> $ hole : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
#> $ piece: int 1 1 1 1 1 1 1 1 1 1 ...
#> $ group: chr "01.1" "01.1" "01.1" "01.1" ...
#> $ fips : chr "01" "01" "01" "01" ...
#> $ abbr : chr "AL" "AL" "AL" "AL" ...
#> $ full : chr "Alabama" "Alabama" "Alabama" "Alabama" ...county_map <- us_map(regions = "counties")str(county_map)
#> 'data.frame': 55097 obs. of 10 variables:
#> $ x : num 811200 829408 828835 855600 859265 ...
#> $ y : num -821207 -819722 -814641 -811770 -846158 ...
#> $ order : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ hole : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
#> $ piece : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ group : chr "01001.1" "01001.1" "01001.1" "01001.1" ...
#> $ fips : chr "01001" "01001" "01001" "01001" ...
#> $ abbr : chr "AL" "AL" "AL" "AL" ...
#> $ full : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
#> $ county: chr "Autauga County" "Autauga County" "Autauga County" "Autauga County" ...- Look up FIPS codes for states and counties
fips("New Jersey")
#> "34"
fips(c("AZ", "CA", "New Hampshire"))
#> "04" "06" "33"
fips("NJ", county = "Mercer")
#> "34021"
fips("NJ", county = c("Bergen", "Hudson", "Mercer"))
#> "34003" "34017" "34021"- Retrieve states or counties with FIPS codes
fips_info(c("34", "35"))
#> full abbr fips
#> 1 New Jersey NJ 34
#> 2 New Mexico NM 35
fips_info(c("34021", "35021"))
#> full abbr county fips
#> 1 New Jersey NJ Mercer County 34021
#> 2 New Mexico NM Harding County 35021- Add FIPS codes to data frame
data <- data.frame(
state = c("NJ", "NJ", "NJ", "PA"),
county = c("Bergen", "Hudson", "Mercer", "Allegheny")
)
library(dplyr)
data %>% rowwise %>% mutate(fips = fips(state, county))
#> state county fips
#> 1 NJ Bergen 34003
#> 2 NJ Hudson 34017
#> 3 NJ Mercer 34021
#> 4 PA Allegheny 42003- Plot US maps
plot_usmap("states")
plot_usmap("counties")- Display only certain states, counties, or regions
plot_usmap("states", include = .mountain, labels = TRUE)
plot_usmap("counties", data = countypov, values = "pct_pov_2014", include = "FL") +
ggplot2::scale_fill_continuous(low = "green", high = "red", guide = FALSE)
plot_usmap("counties", data = countypop, values = "pop_2015", include = .new_england) +
ggplot2::scale_fill_continuous(low = "blue", high = "yellow", guide = FALSE)
usmap uses an Albers equal-area conic projection, with arguments as follows:
usmap::usmap_crs()
#> Coordinate Reference System:
#> Deprecated Proj.4 representation:
#> +proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +ellps=sphere
#> +units=m +no_defs
#> WKT2 2019 representation:
#> PROJCRS["unknown",
#> BASEGEOGCRS["unknown",
#> DATUM["unknown",
#> ELLIPSOID["Normal Sphere (r=6370997)",6370997,0,
#> LENGTHUNIT["metre",1,
#> ID["EPSG",9001]]]],
#> PRIMEM["Greenwich",0,
#> ANGLEUNIT["degree",0.0174532925199433],
#> ID["EPSG",8901]]],
#> CONVERSION["unknown",
#> 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["(E)",east,
#> ORDER[1],
#> LENGTHUNIT["metre",1,
#> ID["EPSG",9001]]],
#> AXIS["(N)",north,
#> ORDER[2],
#> LENGTHUNIT["metre",1,
#> ID["EPSG",9001]]]]
This is the same projection used by the US National Atlas.
To obtain the projection used by usmap, use usmap_crs().
Alternatively, the CRS (coordinate reference system) can be created manually with the following command:
sp::CRS(paste("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0",
"+a=6370997 +b=6370997 +units=m +no_defs"))The code used to generate the map files was based on this blog post by Bob Rudis:
Moving The Earth (well, Alaska & Hawaii) With R