View code used to generate these plots
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:
To install the package from this repository, run the following code in an R console:
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, 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("introduction", package = "usmap")
vignette("mapping", package = "usmap")
vignette("advanced-mapping", package = "usmap")You can also read the vignettes online at the following links: * Introduction * Mapping the US * Advanced Mapping
For further help with this package, open an issue or ask a question on Stackoverflow with the usmap tag.
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" ...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 35021data <- 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 42003plot_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