🗺 Create US maps including Alaska and Hawaii in R.
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README.md

🗺 usmap

CRAN Downloads Build Status codecov

View code used to generate these plots

resources/examples.R

library(usmap)
library(ggplot2)

# Blank state map ####
blank_state_map <- plot_usmap()

# Blank county map ####
blank_county_map <- plot_usmap("counties")

# Population by state ####
state_pop_map <-
  plot_usmap(data = statepop, values = "pop_2015") +
  scale_fill_continuous(low = "white", high = "red", guide = FALSE) +
  scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(expand = c(0, 0))

# Population by state with labels ####
state_pop_map_labeled <-
  plot_usmap(data = statepop, values = "pop_2015", labels = TRUE) +
  scale_fill_continuous(low = "white", high = "red", guide = FALSE) +
  scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(expand = c(0, 0))

# Population by county ####
county_pop_map <-
  plot_usmap(data = countypop, values = "pop_2015") +
  scale_fill_continuous(low = "blue", high = "yellow", guide = FALSE) +
  scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(expand = c(0, 0))

# Poverty percentage by county ####
county_pov_map <-
  plot_usmap(data = countypov, values = "pct_pov_2014") +
  scale_fill_continuous(low = "blue", high = "yellow", guide = FALSE) +
  scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(expand = c(0, 0))


# Combine plots ####
cowplot::plot_grid(
  blank_state_map,
  state_pop_map,
  state_pop_map_labeled,
  blank_county_map,
  county_pop_map,
  county_pov_map,
  nrow = 2
)

# Save plots ####
ggsave("resources/example_plots.png", width = 18, height = 10, units = "in")

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 fortified data frame for mapping and visualization. This allows the user to easily add data to color the map.

Shape Files

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).

Installation

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. The developer build may be unstable and not function correctly, use with caution.

To begin using usmap, simply 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("introduction", package = "usmap")
vignette("mapping", package = "usmap")

You can also read the vignettes online at the following links:

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

Features

  • Obtain map with certain region breakdown
state_map <- us_map(regions = "states")
str(state_map)
#> 'data.frame':    12999 obs. of  9 variables:
#> $ long : num  1091779 1091268 1091140 1090940 1090913 ...
#> $ lat  : num  -1380695 -1376372 -1362998 -1343517 -1341006 ...
#> $ 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':    54187 obs. of  10 variables:
#> $ long  : num  1225889 1244873 1244129 1272010 1276797 ...
#> $ lat   : num  -1275020 -1272331 -1267515 -1262889 -1295514 ...
#> $ 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)

Acknowledgements

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