The goal of cvap is to work with Census citizen voting-age population (CVAP) data.
# install.packages("devtools")
devtools::install_github("christopherkenny/cvap")
The primary tools from this package are to first download CVAP data that’s already processed.
library(cvap)
de_cvap <- cvap_get('DE')
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)
This allows us to distribute the block group data approximately between blocks.
block_est <- cvap_distribute(de_cvap, de_block_group) #> New names: #> • `cvap` -> `cvap...21` #> • `cvap_white` -> `cvap_white...22` #> • `cvap_black` -> `cvap_black...23` #> • `cvap_hisp` -> `cvap_hisp...24` #> • `cvap_aian` -> `cvap_aian...25` #> • `cvap_asian` -> `cvap_asian...26` #> • `cvap_nhpi` -> `cvap_nhpi...27` #> • `cvap_other` -> `cvap_other...28` #> • `cvap_two` -> `cvap_two...29` #> • `cvap` -> `cvap...31` #> • `cvap_white` -> `cvap_white...32` #> • `cvap_black` -> `cvap_black...33` #> • `cvap_hisp` -> `cvap_hisp...34` #> • `cvap_asian` -> `cvap_asian...35` #> • `cvap_aian` -> `cvap_aian...36` #> • `cvap_nhpi` -> `cvap_nhpi...37` #> • `cvap_two` -> `cvap_two...38` #> • `cvap_other` -> `cvap_other...39`
block_est <- cvap_distribute_censable('DE')
dplyr::glimpse(block_est) #> Rows: 574 #> Columns: 40 #> $ GEOID <chr> "100030101042", "100010412002", "100010411002", "10001… #> $ NAME <chr> "Block Group 2, Census Tract 101.04, New Castle County… #> $ pop <dbl> 1909, 2595, 1068, 2852, 0, 473, 63, 35, 53, 781, 57, 2… #> $ pop_white <dbl> 1006, 1067, 713, 1986, 0, 459, 56, 35, 51, 745, 57, 26… #> $ pop_black <dbl> 629, 932, 94, 297, 0, 0, 0, 0, 0, 20, 0, 0, 0, 278, 73… #> $ pop_hisp <dbl> 244, 103, 242, 451, 0, 7, 7, 0, 0, 5, 0, 6, 0, 240, 95… #> $ pop_aian <dbl> 0, 0, 0, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 0, 0, 0, 0… #> $ pop_asian <dbl> 0, 376, 4, 8, 0, 7, 0, 0, 2, 4, 0, 0, 0, 488, 74, 0, 0… #> $ pop_nhpi <dbl> 0, 19, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… #> $ pop_other <dbl> 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… #> $ pop_two <dbl> 30, 82, 10, 75, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 77, 0, 0… #> $ vap <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ vap_white <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ vap_black <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ vap_hisp <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ vap_aian <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ vap_asian <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ vap_nhpi <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ vap_other <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ vap_two <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ cvap...21 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ cvap_white...22 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ cvap_black...23 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ cvap_hisp...24 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ cvap_aian...25 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ cvap_asian...26 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ cvap_nhpi...27 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ cvap_other...28 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ cvap_two...29 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ bg_GEOID <chr> "100030101042", "100010412002", "100010411002", "10001… #> $ cvap...31 <dbl> 1120, 805, 0, 0, 0, 545, 155, 20, 35, 710, 250, 350, 2… #> $ cvap_white...32 <dbl> 630, 415, 0, 0, 0, 535, 150, 20, 30, 605, 250, 270, 25… #> $ cvap_black...33 <dbl> 350, 150, 0, 0, 0, 0, 0, 0, 0, 15, 0, 0, 0, 140, 0, 0,… #> $ cvap_hisp...34 <dbl> 120, 15, 0, 0, 0, 4, 4, 0, 4, 15, 0, 4, 0, 265, 310, 0… #> $ cvap_asian...35 <dbl> 0, 35, 0, 0, 0, 4, 0, 0, 4, 4, 0, 0, 0, 45, 0, 0, 195,… #> $ cvap_aian...36 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 75, 0, 4, 0, 10, 0, 0, 0, 0… #> $ cvap_nhpi...37 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … #> $ cvap_two...38 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 40, 0,… #> $ cvap_other...39 <dbl> 20, 190, 0, 0, 0, 2, 1, 0, 0, 0, 0, 72, 0, 45, 0, 0, 2… #> $ impl_cvap <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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.
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