In walkerke/tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames

library(tidycensus)
knitr::opts_chunk$set(message = FALSE, warning = FALSE, fig.width = 8)

If requested, tidycensus can return simple feature geometry for geographic units along with variables from the decennial US Census or American Community survey. By setting geometry = TRUE in a tidycensus function call, tidycensus will use the tigris package to retrieve the corresponding geographic dataset from the US Census Bureau and pre-merge it with the tabular data obtained from the Census API.

The following example shows median household income from the 2016-2020 ACS for Census tracts in Orange County, California:

library(tidycensus)
library(tidyverse)
options(tigris_use_cache = TRUE)

orange <- get_acs(
  state = "CA",
  county = "Orange",
  geography = "tract",
  variables = "B19013_001",
  geometry = TRUE,
  year = 2020
)

head(orange)

Our object orange looks much like the basic tidycensus output, but with a geometry list-column describing the geometry of each feature, using the geographic coordinate system NAD 1983 (EPSG: 4269) which is the default for Census shapefiles. tidycensus uses the Census cartographic boundary shapefiles for faster processing; if you prefer the TIGER/Line shapefiles, set cb = FALSE in the function call.

As the dataset is in a tidy format, it can be quickly visualized with the geom_sf functionality currently in the development version of ggplot2:

orange %>%
  ggplot(aes(fill = estimate)) + 
  geom_sf(color = NA) + 
  scale_fill_viridis_c(option = "magma") 

Please note that the UTM Zone 11N coordinate system (26911) is appropriate for Southern California but may not be for your area of interest. For help identifying an appropriate projected coordinate system for your data, take a look at the {crsuggest} R package.

Faceted mapping

One of the most powerful features of ggplot2 is its support for small multiples, which works very well with the tidy data format returned by tidycensus. Many Census and ACS variables return counts, however, which are generally inappropriate for choropleth mapping. In turn, get_decennial and get_acs have an optional argument, summary_var, that can work as a multi-group denominator when appropriate. Let's use the following example of the racial geography of Harris County, Texas. First, we'll request data for non-Hispanic whites, non-Hispanic blacks, non-Hispanic Asians, and Hispanics by Census tract for the 2020 Census, using the PL-94171 summary file.

racevars <- c(White = "P2_005N", 
              Black = "P2_006N", 
              Asian = "P2_008N", 
              Hispanic = "P2_002N")

harris <- get_decennial(
  geography = "tract",
  variables = racevars,
  state = "TX",
  county = "Harris County",
  geometry = TRUE,
  summary_var = "P2_001N",
  year = 2020,
  sumfile = "pl"
) 

head(harris)

We notice that there are four entries for each Census tract, with each entry representing one of our requested variables. The summary_value column represents the value of the summary variable, which is total population in this instance. When a summary variable is specified in get_acs, both summary_est and summary_moe columns will be returned.

With this information, we can set up an analysis pipeline in which we calculate a new percent-of-total column and visualize the result for each group in a faceted plot.

harris %>%
  mutate(percent = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = percent)) +
  facet_wrap(~variable) +
  geom_sf(color = NA) +
  theme_void() + 
  scale_fill_viridis_c() + 
  labs(fill = "% of population\n(2020 Census)")

Detailed shoreline mapping with tidycensus and tigris

Geometries in tidycensus default to the Census Bureau's cartographic boundary shapefiles. Cartographic boundary shapefiles are preferred to the core TIGER/Line shapefiles in tidycensus as their smaller size speeds up processing and because they are pre-clipped to the US coastline.

However, there may be circumstances in which your mapping requires more detail. A good example of this would be maps of New York City, in which even the cartographic boundary shapefiles include water area. For example, take this example of median household income by Census tract in Manhattan (New York County), NY:

library(tidycensus)
library(tidyverse)
options(tigris_use_cache = TRUE)

ny <- get_acs(geography = "tract", 
              variables = "B19013_001", 
              state = "NY", 
              county = "New York", 
              year = 2020, 
              geometry = TRUE)

ggplot(ny, aes(fill = estimate)) + 
  geom_sf() + 
  theme_void() + 
  scale_fill_viridis_c(labels = scales::dollar)

As illustrated in the graphic, the boundaries of Manhattan include water boundaries - stretching into the Hudson and East Rivers. In turn, a more accurate representation of Manhattan's land area might be desired. To accomplish this, a tidycensus user can use the core TIGER/Line shapefiles instead with the argument cb = FALSE, then erase water area from Manhattan's geometry. The erase_water() function in the tigris R package will automatically remove proximate water areas from Census polygons, improving cartographic display. The area_threshold argument determines the percentile ranking of the water areas by size in the data's proximity to retain; the default, 0.75, will keep the largest 25 percent of areas. Data should be first transformed to a projected coordinate reference system to improve performance.

library(tigris)
library(sf)

ny_erase <- get_acs(
  geography = "tract",
  variables = "B19013_001",
  state = "NY",
  county = "New York",
  year = 2020,
  geometry = TRUE,
  cb = FALSE
) %>%
  st_transform(26918) %>%
  erase_water(year = 2020)

ggplot(ny_erase, aes(fill = estimate)) + 
  geom_sf() + 
  theme_void() + 
  scale_fill_viridis_c(labels = scales::dollar)

The map appears as before, but instead the polygons now hug the shoreline of Manhattan. Setting the same year in erase_water() as your input data is recommended to avoid sliver polygons, which are small polygons that can appear as a result of misaligned overlay operations.

Writing to shapefiles

Beyond this, you might be interested in writing your dataset to a shapefile or GeoJSON for use in external GIS or visualization applications. You can accomplish this with the st_write function in the sf package:

library(sf)
st_write(orange, "orange.shp")

Your tidycensus-obtained dataset can now be used in ArcGIS, QGIS, Tableau, or any other application that reads shapefiles.

There is a lot more you can do with the spatial functionality in tidycensus, including more sophisticated visualization and spatial analysis; look for updates on my blog and in this space.

walkerke/tidycensus documentation built on July 3, 2025, 7:29 p.m.