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 ──
#> ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
#> ✔ tibble  3.1.8     ✔ dplyr   1.0.9
#> ✔ tidyr   1.2.0     ✔ stringr 1.4.1
#> ✔ readr   2.1.2     ✔ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
 
# load precincts
data('va18sub')

# create block data
block <- create_block_table(state = 'VA', county = '087')  

# 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://www.christophertkenny.com/geomander/