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 simple features (sf) object for mapping and visualization. This allows the user to easily add spatial data or features to the US 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 |
|---|---|---|---|
| January 20, 2024 | 0.7.0 | 2022 | 🔗 |
| 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:
⚠️ The developer build may be unstable and not function correctly, use with caution.
To install the package from this repository, run the following code in an R console:
This method will provide the most recent developer build of usmap.
To begin using usmap, import the package using the library command:
To read the package vignettes, which explain helpful uses of the package, use vignette:
vignette(package = "usmap")
vignette("usmap1", package = "usmap") # 1. Introduction
vignette("usmap2", package = "usmap") # 2. Mapping the US
vignette("usmap3", package = "usmap") # 3. Advanced MappingFor further help with this package, open an issue or ask a question on Stack Overflow with the usmap tag.
states <- plot_usmap("states")
counties <- plot_usmap("counties")
cowplot::plot_grid(states, counties, nrow = 1)
library(ggplot2)
mt <- plot_usmap("states", include = .mountain, labels = TRUE)
fl <- plot_usmap("counties", data = countypov, values = "pct_pov_2021", include = "FL") +
scale_fill_continuous(low = "green", high = "red", guide = "none")
ne <- plot_usmap("counties", data = countypop, values = "pop_2022", include = .new_england) +
scale_fill_continuous(low = "blue", high = "yellow", guide = "none")
cowplot::plot_grid(mt, fl, ne, nrow = 1)
library(ggplot2)
# Transform included `usrivers` data set
rivers_transformed <- usmap_transform(usrivers)
river_map <- plot_usmap("counties", color = "gray80") +
geom_sf(data = rivers_transformed, aes(linewidth = Shape_Length), color = "blue") +
scale_linewidth_continuous(range = c(0.3, 1.5), guide = "none")
# Transform included `earthquakes` data set
eq_transformed <- usmap_transform(earthquakes)
earthquake_map <- plot_usmap() +
geom_sf(data = eq_transformed, aes(size = mag), color = "red", alpha = 0.25) +
scale_size_continuous(guide = "none")
cowplot::plot_grid(river_map, earthquake_map, nrow = 1)
us_map(regions = "states")
#> Simple feature collection with 51 features and 3 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -2590847 ymin: -2608151 xmax: 2523583 ymax: 731405.7
#> Projected CRS: NAD27 / US National Atlas Equal Area
#> # A tibble: 51 × 4
#> fips abbr full geom
#> <chr> <chr> <chr> <MULTIPOLYGON [m]>
#> 1 02 AK Alaska (((-2396840 -2547726, -2393291 -2546396, -2…
#> 2 01 AL Alabama (((1093779 -1378539, 1093270 -1374227, 1092…
#> 3 05 AR Arkansas (((483066 -927786.9, 506063 -926262.2, 5315…
#> 4 04 AZ Arizona (((-1388677 -1254586, -1389182 -1251858, -1…
#> 5 06 CA California (((-1719948 -1090032, -1709613 -1090025, -1…
#> 6 08 CO Colorado (((-789537.1 -678772.6, -789536.6 -678768.3…
#> 7 09 CT Connecticut (((2161728 -83727.16, 2177177 -65210.71, 21…
#> 8 11 DC District of Columbia (((1955475 -402047.2, 1960230 -393564, 1964…
#> 9 10 DE Delaware (((2042501 -284358, 2043073 -279990.9, 2044…
#> 10 12 FL Florida (((1855614 -2064805, 1860160 -2054368, 1867…
#> # ℹ 41 more rowsus_map(regions = "counties")
#> Simple feature collection with 3144 features and 4 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -2590847 ymin: -2608151 xmax: 2523583 ymax: 731405.7
#> Projected CRS: NAD27 / US National Atlas Equal Area
#> # A tibble: 3,144 × 5
#> fips abbr full county geom
#> <chr> <chr> <chr> <chr> <MULTIPOLYGON [m]>
#> 1 02013 AK Alaska Aleutians East Borough (((-1762717 -2477334, -17612…
#> 2 02016 AK Alaska Aleutians West Census Area (((-2396840 -2547726, -23932…
#> 3 02020 AK Alaska Anchorage Municipality (((-1517576 -2089907, -15176…
#> 4 02050 AK Alaska Bethel Census Area (((-1905137 -2137044, -19008…
#> 5 02060 AK Alaska Bristol Bay Borough (((-1685824 -2253496, -16840…
#> 6 02063 AK Alaska Chugach Census Area (((-1476668 -2101298, -14698…
#> 7 02066 AK Alaska Copper River Census Area (((-1457012 -2063407, -14434…
#> 8 02068 AK Alaska Denali Borough (((-1585790 -1980739, -15864…
#> 9 02070 AK Alaska Dillingham Census Area (((-1793023 -2236834, -17884…
#> 10 02090 AK Alaska Fairbanks North Star Borough (((-1512362 -1851013, -14878…
#> # ℹ 3,134 more rowsfips("New Jersey")
#> [1] "34"
fips(c("AZ", "CA", "New Hampshire"))
#> [1] "04" "06" "33"
fips("NJ", county = "Mercer")
#> [1] "34021"
fips("NJ", county = c("Bergen", "Hudson", "Mercer"))
#> [1] "34003" "34017" "34021"fips_info(c("34", "35"))
#> abbr fips full
#> 1 NJ 34 New Jersey
#> 2 NM 35 New Mexico
fips_info(c("34021", "35021"))
#> full abbr county fips
#> 1 New Jersey NJ Mercer County 34021
#> 2 New Mexico NM Harding County 35021library(dplyr)
data <- data.frame(
state = c("NJ", "NJ", "NJ", "PA"),
county = c("Bergen", "Hudson", "Mercer", "Allegheny")
)
data %>% rowwise %>% mutate(fips = fips(state, county))
#> # A tibble: 4 × 3
#> # Rowwise:
#> state county fips
#> <chr> <chr> <chr>
#> 1 NJ Bergen 34003
#> 2 NJ Hudson 34017
#> 3 NJ Mercer 34021
#> 4 PA Allegheny 42003The images generated by usmap are not under any copyright restrictions and may be used and distributed freely in any publication or otherwise.
The underlying shapefiles used to generate the map data are derived from the US Census Bureau’s TIGER/Line Shapefiles which are not copyrighted but do suggest citation. See section 1.2 of this document.
If you wish to cite usmap in a publication (appreciated but never required!), you may do so in the following way:
citation("usmap")
#> To cite package 'usmap' in publications use:
#>
#> Di Lorenzo P (2024). _usmap: US Maps Including Alaska and Hawaii_. R
#> package version 0.7.0, <https://usmap.dev>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {usmap: US Maps Including Alaska and Hawaii},
#> author = {Paolo {Di Lorenzo}},
#> year = {2024},
#> note = {R package version 0.7.0},
#> url = {https://usmap.dev},
#> }usmap uses the US National Atlas Equal Area coordinate system:
sf::st_crs(9311)
#> 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]]This coordinate reference system (CRS) can also be obtained with usmap::usmap_crs().
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