redistmetrics is one of the R packages developed and maintained by the ALARM Project.
redistmetrics provides the back-end for the computation of summary statistics for a redistricting plan. It provides a more direct access point to use methods in
redist without requiring
You can install the stable version of
redistmetrics from CRAN with:
You can install the development version of
redistmetrics from GitHub with:
redistmetrics offers support for 4 common input types and has examples of each, all based on New Hampshire:
For a single plan, we can pass the single plan to the input. We also pass an argument to
shp which takes in an
r_2020 here is the Republican proposal for New Hampshire’s congressional districts.
comp_polsby(plans = nh$r_2020, shp = nh) #>  0.2324375 0.1582763
The output here is a numeric vector, where each entry is the output for a district. The first district here has a compactness of about 0.23 and the second district has a compactness of about 0.16.
Now, if you’re redistricting in R, we recommend using the R package
redist. In which case, you would have a
We can load an example here with:
For redist maps, the workflow is identical!
comp_polsby(plans = nh_map$r_2020, shp = nh) #>  0.2324375 0.1582763
For multiple plans, we can pass either a matrix of plans or a
redist_plans object to plans. We will still need
nh_map to provide the shapes.
If we have a matrix, we can compare with
nh_m a matrix of plans, where each column indicates a plan.
From there, the process is nearly identical. Here we compute the Polsby Popper compactness for the first two columns:
comp_polsby(plans = nh_m[, 1:2], shp = nh) #>  0.1844955 0.1796426 0.2324375 0.1582763
Now we got 4 outputs: 1 for each district x 2 for each plan x 2 plans.
If we are using
redist, we likely have a
redist_plans object which hides the matrix as an attribute to give a more familiar tidy workflow. With that, we can do a very similar process:
First, we load the plans object (included as an example):
The benefit of using a
redist_plans object is that we can cleanly
mutate into it using the
library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union nh_plans <- nh_plans %>% mutate(polsby = comp_polsby(plans = ., shp = nh)) #> Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1
Now our values are cleanly held in the
head(nh_plans) #> # A tibble: 6 x 4 #> draw district total_pop polsby #> <fct> <int> <dbl> <dbl> #> 1 d_2020 1 688739 0.184 #> 2 d_2020 2 688790 0.180 #> 3 r_2020 1 688676 0.232 #> 4 r_2020 2 688853 0.158 #> 5 1 1 688961 0.235 #> 6 1 2 688568 0.349
Detailed information on each measure are contained in the vignettes and references are contained in the function documentation.