This R package enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package supports various constraints in the redistricting process, such as geographic compactness and population parity requirements. Tools for analysis, including computation of various summary statistics and plotting functionality, are also included.
redist is available on CRAN and can be installed using:
You can also install the most recent development version of
redist using the `remotes`` package.
A basic analysis has two steps. First, you define a redistricting plan using
redist_map. Then you simulate plans using one of the algorithm functions:
library(redist) library(dplyr) data(iowa) # set a 0.01% population constraint iowa_map = redist_map(iowa, existing_plan=cd_2010, pop_tol=0.0001, total_pop = pop) # simulate 250 plans using the SMC algorithm iowa_plans = redist_smc(iowa_map, nsims=250, verbose=FALSE) #> SEQUENTIAL MONTE CARLO #> Sampling 250 99-unit maps with 4 districts and population between 761513 and 761665. #> Making split 1 of 3 #> Note: maximum hit; falling back to naive k estimator. #> Resampling effective sample size: 246.051 (98.4206 efficiency). #> Making split 2 of 3 #> Resampling effective sample size: 245.859 (98.3437 efficiency). #> Making split 3 of 3 #> Resampling effective sample size: 246.756 (98.7022 efficiency).
After generating plans, you can use
redist’s plotting functions to study the geographic and partisan characteristics of the simulated ensemble.
dev_comp = iowa_plans %>% mutate(comp = distr_compactness(iowa_map)) %>% group_by(draw) %>% summarize(`Population deviation` = max(abs(total_pop/get_target(iowa_map) - 1)), Compactness = comp) hist(dev_comp, `Population deviation`) + hist(dev_comp, Compactness) + plot_layout(guides="collect") + plot_annotation(title="Simulated plan characteristics")
redist.plot.scatter(dev_comp, `Population deviation`, Compactness) + labs(title="Population deviation and compactness by plan")
iowa_plans %>% mutate(`Democratic vote` = group_frac(iowa_map, dem_08, tot_08)) %>% plot(`Democratic vote`, size=0.5, color_thresh=0.5) + scale_color_manual(values=c("tomato2", "dodgerblue")) + labs(title="Democratic vote share by district")
In some cases, there are two functions with the same purpose that operate on different inputs. For most users, we recommend working with the versions that operate on