19-apis

Author
Affiliation

Prof Amanda Luby

Carleton College
Stat 220 - Winter 2026

TidyCensus

Install tidycensus if not on maize:

install.packages("tidycensus")

Example call:

acs_mn_2020 <- tidycensus::get_acs(
    year = 2020,
    state = "MN",
    geography = "county",
    variables = c("B01003_001", "B19013_001"),
    output = "wide",
    geometry = TRUE
)

Storing your API key

  1. Create a new text file in the same folder as your .rmd
  2. Copy and paste your census key into the empty file
  3. Save the file as census_api_key.txt 
my_key <- readLines("census_api_key.txt")

and tell tidycensus what your API key is with:

Do not commit and push census_api_key.txt to github

acs_mn_2020 |>
  mutate(
    name = str_remove(NAME, ", Minnesota"),
    name = str_remove(name, " County")
  ) |> 
  ggplot(aes(x = B19013_001E, 
                          xmin = B19013_001E - B19013_001M, 
                          xmax = B19013_001E + B19013_001M, 
                          y = fct_reorder(name, B19013_001E))) + 
  geom_point() + 
  geom_errorbarh() + 
  labs(
    x = "",
    y = ""
  )

Try it

Add “educational attainment” to the topic filters and take a look at the first table. What is it showing you?

Your turn

  • Search for the “Median earnings in the past 12 months by major” table that we looked at on the census website using load_variables
  • Run another call to tidycensus::get_acs to access this variable
acs_vars <- load_variables(2020, "acs5", cache = TRUE) 
acs_vars 
# A tibble: 27,850 × 4
   name        label                                    concept        geography
   <chr>       <chr>                                    <chr>          <chr>    
 1 B01001A_001 Estimate!!Total:                         SEX BY AGE (W… tract    
 2 B01001A_002 Estimate!!Total:!!Male:                  SEX BY AGE (W… tract    
 3 B01001A_003 Estimate!!Total:!!Male:!!Under 5 years   SEX BY AGE (W… tract    
 4 B01001A_004 Estimate!!Total:!!Male:!!5 to 9 years    SEX BY AGE (W… tract    
 5 B01001A_005 Estimate!!Total:!!Male:!!10 to 14 years  SEX BY AGE (W… tract    
 6 B01001A_006 Estimate!!Total:!!Male:!!15 to 17 years  SEX BY AGE (W… tract    
 7 B01001A_007 Estimate!!Total:!!Male:!!18 and 19 years SEX BY AGE (W… tract    
 8 B01001A_008 Estimate!!Total:!!Male:!!20 to 24 years  SEX BY AGE (W… tract    
 9 B01001A_009 Estimate!!Total:!!Male:!!25 to 29 years  SEX BY AGE (W… tract    
10 B01001A_010 Estimate!!Total:!!Male:!!30 to 34 years  SEX BY AGE (W… tract    
# ℹ 27,840 more rows

httr2

Install if not already installed:

Example call

hmong_state_request <- request("https://api.census.gov/data") %>% 
    req_url_path_append("2019") %>% 
    req_url_path_append("acs") %>% 
    req_url_path_append("acs1") %>% 
    req_url_query(get = c("NAME", "B02015_009E", "B02015_009M"), `for` = I("state:*"), key = my_key, .multi = "comma")
hmong_state_response <- req_perform(hmong_state_request)
hmong_state_tbl <- hmong_state_response %>%
  resp_body_json(simplifyVector = TRUE) %>%
  janitor::row_to_names(1) %>%
  as_tibble()

hmong_state_tbl

Your Turn

  • Edit the httr code to access the B15013_002E variable from 2024 (median income for “computers, math, statistics” majors)
    • To get US level estimates, use I("us:1") in the for argument
    • To check your answer, copy the URL into a browser and make sure you get "102338"
  • Make a httr request to access the 1-year ACS data from 2022 and 2023. Make sure to save your results from each call!
  • Combine all years into a single dataset (you may need to create a column for year)
  • Make a time series plot with your chosen variable on the y-axis and year on the x-axis
    • You will need to do some cleaning of the data
  • Brainstorm some ways to make the graph more interesting through gathering more data from the census API, and implement those ideas if you have time!