Obtain United States map data frames of varying region types (e.g. county, state). The map data frames include Alaska and Hawaii conveniently placed to the bottom left, as they appear in most maps of the US. Convenience functions for plotting choropleths and working with FIPS codes are also provided.
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")
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
. 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)
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:
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" ...
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"
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
plot_usmap("states")plot_usmap("counties")
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)
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
plot_usmap(labels = TRUE)
plot_usmap(include = .northeast_region)
us_map("counties", include = "TX")
or plot_usmap("counties", include = c("AZ", "NM"))
fips
, see Issue #10.
fips("NJ", c("Bergen", "Hudson"))
map_with_data()
or plot_usmap()
to be preserved.fips
and fips_info
:
fips
to receive a vector of corresponding FIPS codes)fips(c("AK", "AL"))
or fips(c("Alaska", "Alabama"))
fips(c("AK", "Alabama"))
plot_usmap
and map_with_data
(instead of just by FIPS code).
plot_usmap
or map_with_data
(via the data =
parameter), can now be a two column data frame with columns "fips" and "values" or "state" and "values".map_with_data
and plot_usmap
to contain FIPS codes with missing leading zeros.
numeric
from a .csv
file.map_with_data
function for adding user-defined data to map data.map_with_data
function).ggplot2
)