A set of redistricting plans

A redist_plans object is essentially a data frame of summary information on each district and each plan, along with the matrix of district assignments and information about the simulation process used to generate the plans.

redist_plans(plans, map, algorithm, wgt = NULL, ...)

Arguments

plans

a matrix with n_precinct columns and n_sims rows, or a single vector of precinct assignments.

map

a redist_map object

algorithm

the algorithm used to generate the plans (usually "smc" or "mcmc")

wgt

the weights to use, if any.

...

Other named attributes to set

Value

a new redist_plans object.

Details

The first two columns of the data frame will be draw, a factor indexing the simulation draw, and district, an integer indexing the districts within a plan. The data frame will therefore have n_sims*ndists rows. As a data frame, the usual dplyr methods will work.

Other useful methods for redist_plans objects:

• summary.redist_plans

• add_reference

• subset_sampled

• subset_ref

• pullback

• number_by

• match_numbers

• is_county_split

• prec_assignment

• plan_distances

• get_plans_matrix

• get_plans_weights

• get_sampling_info

• as.matrix.redist_plans

• plot.redist_plans

Examples

data(iowa)

iowa <- redist_map(iowa, existing_plan = cd_2010, pop_tol = 0.05, total_pop = pop)
rsg_plan <- redist.rsg(iowa$adj, iowa$pop, ndists = 4, pop_tol = 0.05)$plan
#> 
#> ==================== 
#> redist.rsg(): Automated Redistricting Starts
#> 
#> 
#> 	4 districts built using 99 precincts in 0.06 seconds...
#> 
redist_plans(rsg_plan, iowa, "rsg")
#> A <redist_plans> containing 1 sampled plan
#> Plans have 4 districts from a 99-unit map, and were drawn using random
#> seed-and-grow.
#> Plans matrix: int [1:99, 1] 4 4 1 3 4 3 1 2 1 1 ...
#> # A tibble: 4 × 3
#>   draw  district total_pop
#> * <fct>    <int>     <dbl>
#> 1 1            1    781611
#> 2 1            2    799275
#> 3 1            3    724719
#> 4 1            4    740750