Citizen Voting Age Population.
cvap
The goal of cvap is to work with Census citizen voting-age population (CVAP) data.
Installation
You can install the released version of cvap from CRAN with:
install.packages("cvap")
Install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("christopherkenny/cvap")
Example
The primary tools from this package are to first download CVAP data that’s already processed.
library(cvap)
de_cvap <- cvap_get('DE')
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:
data('de_block')
Along with the Delaware 2019 ACS data:
data('de_block_group')
This allows us to distribute the block group data approximately between blocks.
block_est <- cvap_distribute(de_cvap, de_block, de_block_group)
This workflow can also be combined into one function
block_est <- cvap_distribute_censable('DE')
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.