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.

There are two major functions implemented in tidycensus: get_decennial, which grants access to the 1990, 2000, and 2010 decennial US Census APIs, and get_acs, which grants access to the 5-year American Community Survey APIs. In this basic example, let’s look at median gross rent by state in 1990:

m90 <- get_decennial(geography = "state", variables = "H043A001", year = 1990)

head(m90)
## # A tibble: 6 x 4
##   GEOID NAME       variable value
##   <chr> <chr>      <chr>    <dbl>
## 1 01    Alabama    H043A001   325
## 2 02    Alaska     H043A001   559
## 3 04    Arizona    H043A001   438
## 4 05    Arkansas   H043A001   328
## 5 06    California H043A001   620
## 6 08    Colorado   H043A001   418

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:

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()
"region" Census region get_acs()
"division" Census division get_acs()
"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" 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()
"american indian area (off-reservation trust land only)/hawaiian home land" Only off-reservation trust lands and Hawaiian home lands state get_acs()
"metropolitan statistical area/micropolitan statistical area" Core-based statistical area state get_acs(), get_decennial()
"combined statistical area" Combined statistical area state get_acs()
"new england city and town area" New England city/town area state get_acs()
"combined new england city and town area" Combined New England area state get_acs()
"urban area" Census-defined urbanized areas get_acs()
"congressional district" Congressional district for the year-appropriate Congress state get_acs(), get_decennial()
"school district (elementary)" Elementary school district state get_acs()
"school district (secondary)" Secondary school district state get_acs()
"school district (unified)" Unified school district state get_acs()
"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()

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 - one of "sf1", "sf3", or "acs5". For ideal functionality, I recommend assigning the result of this function to a variable, setting cache = TRUE to store the result on your computer for future access, and using the View function in RStudio to interactively browse for variables.

v15 <- load_variables(2016, "acs5", cache = TRUE)

View(v15)

By filtering for “median age” I can quickly view the variable IDs that correspond to my query.

Working with ACS data

American Community Survey data differ from decennial Census data in that ACS data 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. 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 2011-2015 ACS for counties in Vermont; the endyear is not necessary here as the function defaults to 2015.

## # A tibble: 14 x 5
##    GEOID NAME                       variable  estimate   moe
##    <chr> <chr>                      <chr>        <dbl> <dbl>
##  1 50001 Addison County, Vermont    medincome    61020  2194
##  2 50003 Bennington County, Vermont medincome    51489  3350
##  3 50005 Caledonia County, Vermont  medincome    46931  1876
##  4 50007 Chittenden County, Vermont medincome    66414  2090
##  5 50009 Essex County, Vermont      medincome    39467  2540
##  6 50011 Franklin County, Vermont   medincome    58884  2002
##  7 50013 Grand Isle County, Vermont medincome    64295  2932
##  8 50015 Lamoille County, Vermont   medincome    53316  4047
##  9 50017 Orange County, Vermont     medincome    54263  1743
## 10 50019 Orleans County, Vermont    medincome    43959  2047
## 11 50021 Rutland County, Vermont    medincome    50029  1717
## 12 50023 Washington County, Vermont medincome    58171  1989
## 13 50025 Windham County, Vermont    medincome    50917  1775
## 14 50027 Windsor County, Vermont    medincome    54763  2123

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: