Maps can be tricky in R! There are many packages to choose from.
usmapis compatible with ggplotmapsis “Base R”- Some require API keys (e.g.,
ggmap,tidycensus) - Some are interactive (e.g.,
leaflet)
Making maps in R
What does a map in R look like?
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Data formats - boundary data
library(tidyverse)
head(map_data("county"))
long lat group order region subregion 1 -86.50517 32.34920 1 1 alabama autauga 2 -86.53382 32.35493 1 2 alabama autauga 3 -86.54527 32.36639 1 3 alabama autauga 4 -86.55673 32.37785 1 4 alabama autauga 5 -86.57966 32.38357 1 5 alabama autauga 6 -86.59111 32.37785 1 6 alabama autauga
Data formats - boundary data
long: Longitude (x-coordinate)lat: Latitude (y-coordinate)group: Identifies unique polygons (each county may have multiple polygons if it contains islands or complex borders).order: Sequence in which points should be connected to form the boundary (polygons).region: State name (e.g., “alabama”).subregion: County name within the state (e.g., “autauga”).
Data formats - sf data
library(usmapdata)
head(us_map("county"))
Simple feature collection with 6 features and 4 fields Geometry type: MULTIPOLYGON Dimension: XY Bounding box: xmin: -2590847 ymin: -2608148 xmax: -1298969 ymax: -2034041 Projected CRS: NAD27 / US National Atlas Equal Area # A tibble: 6 × 5 fips abbr full county geom <chr> <chr> <chr> <chr> <MULTIPOLYGON [m]> 1 02013 AK Alaska Aleutians East Borough (((-1762715 -2477334, -1761280 … 2 02016 AK Alaska Aleutians West Census Area (((-2396847 -2547721, -2393297 … 3 02020 AK Alaska Anchorage Municipality (((-1517576 -2089908, -1517636 … 4 02050 AK Alaska Bethel Census Area (((-1905141 -2137046, -1900900 … 5 02060 AK Alaska Bristol Bay Borough (((-1685825 -2253496, -1684030 … 6 02063 AK Alaska Chugach Census Area (((-1476669 -2101298, -1469831 …
Data formats - sf data
This data is “Simple Feature” (sf) data used for spatial analysis.
These objects store geometric shapes (like points, lines, or polygons) along with associated attributes (metadata).
the geom column is a MULTIPOLYGON — a geometry type representing complex shapes, which may consist of multiple polygons (e.g., islands or non-contiguous regions).
Federal Information Processing System (FIPS) Codes for States and Counties are numbers which uniquely identify geographic areas. See this codebook.
ggplot has spatial functions
geom_polygon() works with boundary data
Let’s plot county outlines and major cities.
library(tidyverse) # `map_data()` from ggplot2
library(maps) # `us.cities` data
wa_county <- map_data("county") %>% filter(region == "washington")
wa_cities <- us.cities %>% filter(country.etc == "WA")
plot_1 <-
ggplot() +
geom_polygon(data = wa_county, aes(x = long, y = lat, group = group),
color = "black", fill = NA) +
geom_point(data = wa_cities, aes(x = long, y = lat)) +
labs(title = "Washington State Cities", x = "longitude", y = "latitude") +
coord_fixed(1.3)
ggplot has spatial functions
maps package is more similar to Base R
library(maps) # `map` and `map.cities` functions and `us.cities` data
map('county', region = 'washington', col = "#5E610B")
map.cities(us.cities, country="WA", col = "#642EFE", cex = 2)
title(main = "Washington State Cities")
maps package is more similar to Base R
usmap is compatible with ggplot
Let’s fill each county based on its population.
library(tidyverse)
library(usmap) # `countypop` data and the `plot_usmap()` function
wa_dat <- countypop %>% filter(abbr == "WA")
plot_3 <-
plot_usmap(data = wa_dat, values = "pop_2022", include = c("WA")) +
scale_fill_continuous() +
theme(legend.position = "right")
usmap is compatible with ggplot
It can get complicated! ggplot fill by county
Sometimes a lot of cleanup is needed to join boundary data with attributes of interest!
library(tidyverse)
library(usmap) # `countypop` data
library(maps) # `us.cities` data
# Get county boundaries
wa_county <- map_data("county") %>% filter(region == "washington")
# Get county-level ("subregion") population
wa_dat <- countypop %>% filter(abbr == "WA") %>%
mutate(subregion = tolower(str_remove(county, " County"))) %>%
group_by(subregion) %>% summarize(pop_2022 = sum(pop_2022))
# Combine the data
wa_complete <- wa_county %>% inner_join(wa_dat)
# Get WA cities and their coordinates
wa_cities <- us.cities %>% filter(country.etc == "WA")
It can get complicated! ggplot fill by county
Sometimes a lot of cleanup is needed to join boundary data with attributes of interest!
# Step 2: create the plot
plot_4 <-
ggplot() +
geom_polygon(data = wa_complete,
aes(x = long, y = lat, group = group, fill = pop_2022)) +
geom_point(data = wa_cities, aes(x = long, y = lat), color = "red") +
labs(
title = "Washington State Population and Cities, 2022",
x = "longitude", y = "latitude") +
coord_fixed(1.3)
It can get complicated! ggplot fill by county
tidycensus is helpful for tract level
Use geom_sf() function with SF data.
Let’s fill each census tract by median household income.
library(tidyverse) # `geom_sf()` from ggplot2 library(tidycensus) # `get_acs()` function for American Community Survey data wa_income <- get_acs( geography = "tract", variables = "B19013_001", # Median income code state = "WA", year = 2022, geometry = TRUE )
tidycensus is helpful for tract level
ggplot(data = wa_income, aes(fill = estimate)) + geom_sf()
Tips for Mapping in R
Know the functions: make sure your data going into plotting functions is similar to
Data Structure: Ensure column names match between datasets for join() operations
- e.g.,
subregionneeds to align in bothwa_countyandwa_datto makewa_complete. - Make sure all datasets (like counties and cities) use the same geographic system, such as longitude-latitude pairs.
- e.g.,
Clean Data to make life easier
- Use functions like
tolower()andstr_remove()to standardize text (e.g., removing “County”). - Group and summarize data when plotting aggregates, like population by county.
- Use functions like
More resources
https://rfortherestofus.com/2024/06/us-maps
https://walker-data.com/tidycensus/articles/spatial-data.html
https://walker-data.com/census-r/mapping-census-data-with-r.html
https://jtr13.github.io/cc19/different-ways-of-plotting-u-s-map-in-r.html