The goal of cvap is to work with Census citizen voting-age population (CVAP) data.
You can install the released version of cvap from CRAN with:
Install the development version from GitHub with:
The primary tools from this package are to first download CVAP data that’s already processed.
cvap_get() has options for the available geographies in the Census special tabulation.
With that data, we can combine it with ACS and Decennial Census data to estimate block-level data.
de_block <- censable::build_dec(geography = 'block', state = 'DE',
year = 2010, geometry = FALSE)
de_block_group <- censable::build_acs(geography = 'block group', state = 'DE',
geometry = FALSE, year = 2019)For example purposes, the 2010 Delaware Census block data is provided:
Along with the Delaware 2019 ACS data:
This allows us to distribute the block group data approximately between blocks.
This workflow can also be combined into one function
The resulting data has estimated CVAP data for each block:
dplyr::glimpse(block_est)
#> Rows: 24,115
#> Columns: 29
#> $ GEOID <chr> "100010401001000", "100010401001001", "100010401001002", "1~
#> $ NAME <chr> "Block 1000, Block Group 1, Census Tract 401, Kent County, ~
#> $ pop <dbl> 77, 294, 20, 91, 53, 6, 50, 0, 0, 21, 294, 19, 0, 23, 42, 0~
#> $ pop_white <dbl> 53, 280, 20, 91, 50, 6, 47, 0, 0, 19, 261, 18, 0, 23, 41, 0~
#> $ pop_black <dbl> 9, 5, 0, 0, 0, 0, 3, 0, 0, 0, 19, 1, 0, 0, 1, 0, 1, 0, 0, 1~
#> $ pop_hisp <dbl> 9, 3, 0, 0, 3, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 7,~
#> $ pop_aian <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ pop_asian <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ pop_nhpi <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ pop_other <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ pop_two <dbl> 4, 5, 0, 0, 0, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 2, 0, 0, 0,~
#> $ vap <dbl> 48, 223, 12, 64, 44, 6, 42, 0, 0, 14, 203, 13, 0, 13, 30, 0~
#> $ vap_white <dbl> 36, 214, 12, 64, 42, 6, 40, 0, 0, 13, 179, 12, 0, 13, 29, 0~
#> $ vap_black <dbl> 4, 4, 0, 0, 0, 0, 2, 0, 0, 0, 14, 1, 0, 0, 1, 0, 1, 0, 0, 1~
#> $ vap_hisp <dbl> 5, 1, 0, 0, 2, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 2,~
#> $ vap_aian <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ vap_asian <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ vap_nhpi <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ vap_other <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ vap_two <dbl> 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ cvap <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ cvap_white <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ cvap_black <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ cvap_hisp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ cvap_asian <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ cvap_aian <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ cvap_nhpi <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~
#> $ cvap_two <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ cvap_other <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,~Thus, using other packages like PL94171, we can easily aggregate this from blocks to VTDs, where CVAP is not directly provided. vest_crosswalk allows you to download a variant of VEST’s crosswalk that is computationally more favorable for R.