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 data-raw folder. For more information refer to the US Census Bureau. Maps at both the state and county levels are included for convenience (zip code maps may be included in the future).
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