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


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 2010:

age10 <- get_decennial(geography = "state", 
                       variables = "P013001", 
                       year = 2010)

## # A tibble: 6 × 4
##   GEOID NAME       variable value
##   <chr> <chr>      <chr>    <dbl>
## 1 01    Alabama    P013001   37.9
## 2 02    Alaska     P013001   33.8
## 3 04    Arizona    P013001   35.9
## 4 05    Arkansas   P013001   37.4
## 5 06    California P013001   35.2
## 6 22    Louisiana  P013001   35.8

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:

age10 %>%
  ggplot(aes(x = value, y = reorder(NAME, value))) + 

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

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 (currently the only choice for 2020), "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)


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 2014-2018 ACS for counties in Vermont.

vt <- get_acs(geography = "county", 
              variables = c(medincome = "B19013_001"), 
              state = "VT", 
              year = 2018)

## # A tibble: 14 × 5
##    GEOID NAME                       variable  estimate   moe
##    <chr> <chr>                      <chr>        <dbl> <dbl>
##  1 50001 Addison County, Vermont    medincome    65093  2424
##  2 50003 Bennington County, Vermont medincome    53040  2307
##  3 50005 Caledonia County, Vermont  medincome    49348  1842
##  4 50007 Chittenden County, Vermont medincome    69896  2132
##  5 50009 Essex County, Vermont      medincome    41045  2661
##  6 50011 Franklin County, Vermont   medincome    64258  1568
##  7 50013 Grand Isle County, Vermont medincome    69583  5812
##  8 50015 Lamoille County, Vermont   medincome    60365  3915
##  9 50017 Orange County, Vermont     medincome    60159  2361
## 10 50019 Orleans County, Vermont    medincome    47915  2193
## 11 50021 Rutland County, Vermont    medincome    54973  1754
## 12 50023 Washington County, Vermont medincome    62108  2065
## 13 50025 Windham County, Vermont    medincome    52659  1706
## 14 50027 Windsor County, Vermont    medincome    58303  1576

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 = "2014-2018 American Community Survey",
       y = "",
       x = "ACS estimate (bars represent margin of error)")