🗺 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.

Shape Files

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.

Update History

Date usmap version Shape File Year Link
(unreleased) 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.package("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

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_2014", include = "FL") +
  scale_fill_continuous(low = "green", high = "red", guide = "none")

ne <- plot_usmap("counties", data = countypop, values = "pop_2015", 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)

Map Data

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 rows
us_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 rows

FIPS Codes

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"
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
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

Coordinate System

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().

Acknowledgments

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