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

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

To get started working with tidycensus, users should load the package along with the tidyverse package, and set their Census API key. A key can be obtained from http://api.census.gov/data/key_signup.html.

library(tidycensus)
library(tidyverse)

library(tidycensus)
library(tidyverse)

census_api_key("YOUR API KEY GOES HERE")

There are two major functions implemented in tidycensus: get_decennial(), which grants access to the 2000, 2010, and 2020 decennial US Census APIs, and get_acs(), which grants access to the 1-year and 5-year American Community Survey APIs.

In this basic example, let's look at median age by state in 2020, with data drawn from the Demographic and Housing Characteristics summary file:

age20 <- get_decennial(geography = "state", 
                       variables = "P13_001N", 
                       year = 2020,
                       sumfile = "dhc")

head(age20)

The function returns a tibble with four columns by default: GEOID, which is an identifier for the geographical unit associated with the row; NAME, which is a descriptive name of the geographical unit; variable, which is the Census variable represented in the row; and value, which is the value of the variable for that unit. By default, tidycensus functions return tidy data frames in which rows represent unit-variable combinations; for a wide data frame with Census variable names in the columns, set output = "wide" in the function call.

As the function has returned a tidy object, we can visualize it quickly with ggplot2:

age20 %>%
  ggplot(aes(x = value, y = reorder(NAME, value))) + 
  geom_point()

Geography in tidycensus

To get decennial Census data or American Community Survey data, tidycensus users supply an argument to the required geography parameter. Arguments are formatted as consumed by the Census API, and specified in the table below. Not all geographies are available for all surveys, all years, and all variables. Most Census geographies are supported in tidycensus at the moment; if you require a geography that is missing from the table below, please file an issue at https://github.com/walkerke/tidycensus/issues.

If state or county is in bold face in "Available by", you are required to supply a state and/or county for the given geography.

|Geography|Definition|Available by|Available in| |---------|----------|------------|------------| |"us"|United States||get_acs(), get_decennial()| |"region"|Census region||get_acs(), get_decennial()| |"division"|Census division||get_acs(), get_decennial()| |"state"|State or equivalent|state|get_acs(), get_decennial()| |"county"|County or equivalent|state, county|get_acs(), get_decennial()| |"county subdivision"|County subdivision|state, county|get_acs(), get_decennial()| |"tract"|Census tract|state, county|get_acs(), get_decennial()|
|"block group" OR "cbg"|Census block group|state, county|get_acs(), get_decennial()|
|"block"|Census block|state, county|get_decennial()| |"place"|Census-designated place|state|get_acs(), get_decennial()|
|"alaska native regional corporation"|Alaska native regional corporation|state|get_acs(), get_decennial()|
|"american indian area/alaska native area/hawaiian home land"|Federal and state-recognized American Indian reservations and Hawaiian home lands|state|get_acs(), get_decennial()|
|"american indian area/alaska native area (reservation or statistical entity only)"|Only reservations and statistical entities|state|get_acs(), get_decennial()| |"american indian area (off-reservation trust land only)/hawaiian home land"|Only off-reservation trust lands and Hawaiian home lands|state|get_acs()| |"metropolitan/micropolitan statistical area" (2021 5-year ACS and later) OR "metropolitan statistical area/micropolitan statistical area" OR "cbsa" |Core-based statistical area||get_acs(), get_decennial()| |"combined statistical area"|Combined statistical area|state|get_acs(), get_decennial()| |"new england city and town area"|New England city/town area|state|get_acs(), get_decennial()| |"combined new england city and town area"|Combined New England area|state|get_acs(), get_decennial()| |"urban area"|Census-defined urbanized areas||get_acs(), get_decennial()| |"congressional district"|Congressional district for the year-appropriate Congress|state|get_acs(), get_decennial()|
|"school district (elementary)"|Elementary school district|state|get_acs(), get_decennial()| |"school district (secondary)"|Secondary school district|state|get_acs(), get_decennial()| |"school district (unified)"|Unified school district|state|get_acs(), get_decennial()| |"public use microdata area"|PUMA (geography associated with Census microdata samples)|state|get_acs()| |"zip code tabulation area" OR "zcta"|Zip code tabulation area||get_acs(), get_decennial()|
|"state legislative district (upper chamber)"|State senate districts|state|get_acs(), get_decennial()|
|"state legislative district (lower chamber)"|State house districts|state|get_acs(), get_decennial()| |"voting district"|Voting districts (2020 only)|state|get_decennial()|

Searching for variables

Getting variables from the Census or ACS requires knowing the variable ID - and there are thousands of these IDs across the different Census files. To rapidly search for variables, use the load_variables() function. The function takes two required arguments: the year of the Census or endyear of the ACS sample, and the dataset name, which varies in availability by year. For the decennial Census, possible dataset choices include "pl" for the redistricting files; "dhc" for the Demographic and Housing Characteristics file and "dp" for the Demographic Profile (2020 only), and "sf1" or "sf2" (2000 and 2010) and "sf3" or "sf4" (2000 only) for the various summary files. Special island area summary files are available with "as", "mp", "gu", or "vi".

For the ACS, use either "acs1" or "acs5" for the ACS detailed tables, and append /profile for the Data Profile and /subject for the Subject Tables. To browse these variables, assign the result of this function to a variable and use the View function in RStudio. An optional argument cache = TRUE will cache the dataset on your computer for future use.

v17 <- load_variables(2017, "acs5", cache = TRUE)

View(v17)

By filtering for "median age" variable IDs corresponding to that query can be browsed interactively. For the 5-year ACS detailed tables (denoted by "acs5"), a geography column will also be returned that tells users the smallest geography at which a given variable is available.

Working with ACS data

American Community Survey (ACS) data are available from the 1-year ACS since 2005 for geographies of population 65,000 and greater, and from the 5-year ACS for all geographies down to the block group level starting with the 2005-2009 dataset. get_acs() defaults to the 5-year ACS with the argument survey = "acs5", but 1-year ACS data are available using survey = "acs1".

ACS data differ from decennial Census data as they are based on an annual sample of approximately 3 million households, rather than a more complete enumeration of the US population. In turn, ACS data points are estimates characterized by a margin of error. tidycensus will always return the estimate and margin of error together for any requested variables when using get_acs(). In turn, when requesting ACS data with tidycensus, it is not necessary to specify the "E" or "M" suffix for a variable name. Let's fetch median household income data from the 2017-2021 ACS for counties in Vermont.

vt <- get_acs(geography = "county", 
              variables = c(medincome = "B19013_001"), 
              state = "VT", 
              year = 2021)

vt

The output is similar to a call to get_decennial(), but instead of a value column, get_acs returns estimate and moe columns for the ACS estimate and margin of error, respectively. moe represents the default 90 percent confidence level around the estimate; this can be changed to 95 or 99 percent with the moe_level parameter in get_acs if desired.

As we have the margin of error, we can visualize the uncertainty around the estimate:

vt %>%
  mutate(NAME = gsub(" County, Vermont", "", NAME)) %>%
  ggplot(aes(x = estimate, y = reorder(NAME, estimate))) +
  geom_errorbarh(aes(xmin = estimate - moe, xmax = estimate + moe)) +
  geom_point(color = "red", size = 3) +
  labs(title = "Household income by county in Vermont",
       subtitle = "2017-2021 American Community Survey",
       y = "",
       x = "ACS estimate (bars represent margin of error)")

walkerke/tidycensus documentation built on March 28, 2024, 7:04 p.m.