This loads ACS counts via tidycensus and gives them additional labels and renames some variables to later merge with CCES-based regression models.

get_acs_cces(
  varlist,
  varlab_df = ccesMRPprep::acscodes_df,
  year = 2018,
  states = NULL,
  dataset = "acs1",
  geography = "congressional district"
)

Arguments

varlist

a vector of variable codes to pull

varlab_df

a dataframe that appends the categories based on the varcode

year

The year of the ACS to get. Because of data availability limitations, this is capped to 2010-2018.

states

A vector of states to subset to. Gets passed onto the new state argument in tidycensus::get_acs(), which defaults to NULL.

dataset

Which type of ACS to get. Defaults to "acs1" for ACS-5 year. Use "acs5" for 5-year.

geography

the type of geography to pull. Currently only supports "congressional district".

Details

To run this, you need to have a API token to run get_acs. See census_api_key for details.

See also

get_acs_cces

Examples

if (FALSE) { fm_brm <- yes | responses(n_cell) ~ age + gender + educ + pct_trump + (1|cd) acs_tab <- get_acs_cces( varlist = acscodes_age_sex_educ, varlab_df = acscodes_df, year = 2018) # year cd gender age educ race count count_moe # <dbl> <chr> <fct> <fct> <fct> <fct> <dbl> <dbl> # 1 2018 AL-01 Male 18 to 24 years HS or Less NA 703 240 # 2 2018 AL-01 Male 18 to 24 years HS or Less NA 5665 581 # 3 2018 AL-01 Male 18 to 24 years HS or Less NA 11764 747 # 4 2018 AL-01 Male 18 to 24 years Some College NA 9528 750 # 5 2018 AL-01 Male 18 to 24 years Some College NA 1389 355 # 6 2018 AL-01 Male 18 to 24 years 4-Year NA 1519 276 poststrat <- get_poststrat(acs_tab, cd_info_2018, fm_brm) }