Focuses on creating data sets and other tools that help make understanding gerrymandering faster and easier. Designed for easy preparation to run simulation analysis with the R package redist, but is aimed at the geographic aspects of redistricting, not partitioning methods. Most of these tools are gathered from seminar papers and do not correspond to a single publication.
Installation
You can install the released version of geomander from CRAN with:
install.packages("geomander")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("christopherkenny/geomander")Examples
A very common task is aggregating block data to precincts.
library(geomander)
library(tidyverse)
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v ggplot2 3.3.4 v purrr 0.3.4
#> v tibble 3.1.2 v dplyr 1.0.6
#> v tidyr 1.1.3 v stringr 1.4.0
#> v readr 1.4.0 v forcats 0.5.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
# load precincts
data("va18sub")
# subset to target area
va18sub <- va18sub
# create block data
block <- create_block_table(state = 'VA', county = '087')
#> Getting data from the 2010 decennial Census
#> Using Census Summary File 1
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# match the geographies
matches <- geo_match(from = block, to = va18sub)
# Aggregate
prec <- block2prec(block_table = block, matches = matches)Other important tasks include breaking data into pieces by blocks underlying them.
library(geomander)
library(tidyverse)
# load precincts
data("va18sub")
# subset to target area
va18sub <- va18sub %>% filter(COUNTYFP == '087')Then we can get common block data:
block <- create_block_table(state = 'VA', county = '087') And estimate down to blocks
disagg <- geo_estimate_down(from = va18sub, to = block, wts = block$vap, value = va18sub$G18USSRSTE)For more information, see the documentation and vignettes, available at https://christopherkenny.github.io/geomander/
