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🗺 usmap

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Purpose

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.

Available Map Data

The map data 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.

In usmap v0.8.0 the data_year parameter was added to most package functions, allowing the user to select from multiple available years. Since data is now stored in usmapdata, updates are no longer tied to usmap versions.

Update History

| Date | Available Years | |----|:--:| | May 28, 2025 | 2021, 2022, 2023, 2024 |

Prior to usmap v0.8.0:

| Date | usmap version | Available Year | |----|:--:|:--:| | May 10, 2024 | 0.7.1 | 2023 | | 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 |

Installation

📦 To install from CRAN (recommended), run the following code in an R console:

install.packages("usmap")

Developer Build

⚠️ 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:

install.packages("devtools")
devtools::install_github("pdil/usmap")

This method will provide the most recent developer build of usmap.

To begin using usmap, import the package using the library command:

library(usmap)

Documentation

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 Mapping

For further help with this package, open an issue or ask a question on Stack Overflow with the usmap tag.

Features

Map Plots

• Plot US maps

states <- plot_usmap("states")
counties <- plot_usmap("counties")

cowplot::plot_grid(states, counties, nrow = 1)

<img src="man/figures/README-plots-1.png" style="display: block; margin: auto;" />

• Display only certain states, counties, or regions

library(ggplot2)

mt <- plot_usmap("states", include = .mountain, labels = TRUE)

fl <- plot_usmap("counties", data = countypov, values = "pct_pov_2021", include = "FL", data_year = 2021) +
  scale_fill_continuous(low = "green", high = "red", guide = "none")

ne <- plot_usmap("counties", data = countypop, values = "pop_2022", include = .new_england, data_year = 2022) +
  scale_fill_continuous(low = "blue", high = "yellow", guide = "none")

cowplot::plot_grid(mt, fl, ne, nrow = 1)

<img src="man/figures/README-more_plots-1.png" style="display: block; margin: auto;" />

• Transform and add spatial data to map

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
earthquakes_above_mag_3 <- earthquakes[earthquakes$mag > 3, ]
eq_transformed <- usmap_transform(earthquakes_above_mag_3)

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)

<img src="man/figures/README-sf_plot-1.png" style="display: block; margin: auto;" />

Map Data

• Obtain map data with certain region breakdown

us_map(regions = "states")
#> Simple feature collection with 52 features and 3 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -2584074 ymin: -2602555 xmax: 2516258 ymax: 731628.1
#> Projected CRS: NAD27 / US National Atlas Equal Area
#> First 10 features:
#>    fips abbr                 full                           geom
#> 1    02   AK               Alaska MULTIPOLYGON (((-2390688 -2...
#> 2    01   AL              Alabama MULTIPOLYGON (((1091785 -13...
#> 3    05   AR             Arkansas MULTIPOLYGON (((482022.2 -9...
#> 4    04   AZ              Arizona MULTIPOLYGON (((-1386064 -1...
#> 5    06   CA           California MULTIPOLYGON (((-1716581 -1...
#> 6    08   CO             Colorado MULTIPOLYGON (((-787705.6 -...
#> 7    09   CT          Connecticut MULTIPOLYGON (((2156162 -83...
#> 8    11   DC District of Columbia MULTIPOLYGON (((1950799 -40...
#> 9    10   DE             Delaware MULTIPOLYGON (((2037480 -28...
#> 10   12   FL              Florida MULTIPOLYGON (((1853163 -20...

us_map(regions = "counties")
#> Simple feature collection with 3222 features and 4 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -2584074 ymin: -2602555 xmax: 2516258 ymax: 731628.1
#> Projected CRS: NAD27 / US National Atlas Equal Area
#> First 10 features:
#>     fips abbr   full                       county
#> 1  02013   AK Alaska       Aleutians East Borough
#> 2  02016   AK Alaska   Aleutians West Census Area
#> 3  02020   AK Alaska       Anchorage Municipality
#> 4  02050   AK Alaska           Bethel Census Area
#> 5  02060   AK Alaska          Bristol Bay Borough
#> 6  02063   AK Alaska          Chugach Census Area
#> 7  02066   AK Alaska     Copper River Census Area
#> 8  02068   AK Alaska               Denali Borough
#> 9  02070   AK Alaska       Dillingham Census Area
#> 10 02090   AK Alaska Fairbanks North Star Borough
#>                              geom
#> 1  MULTIPOLYGON (((-1757988 -2...
#> 2  MULTIPOLYGON (((-2390688 -2...
#> 3  MULTIPOLYGON (((-1513326 -2...
#> 4  MULTIPOLYGON (((-1899724 -2...
#> 5  MULTIPOLYGON (((-1681144 -2...
#> 6  MULTIPOLYGON (((-1472549 -2...
#> 7  MULTIPOLYGON (((-1452955 -2...
#> 8  MULTIPOLYGON (((-1581283 -1...
#> 9  MULTIPOLYGON (((-1788018 -2...
#> 10 MULTIPOLYGON (((-1508063 -1...

FIPS Codes

• Look up FIPS codes for states and counties

fips("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"

• Retrieve states or counties with FIPS codes

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 35021

• Add FIPS codes to data frame

library(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 42003

Additional Information

Citation Information

The 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!), yo