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 redist
objects.
You can install the stable version of redistmetrics
from CRAN with:
install.packages('redistmetrics')
You can install the development version of redistmetrics
from
GitHub with:
if (!requireNamespace('remotes')) install.packages('remotes')
remotes::install_github('alarm-redist/redistmetrics')
library(redistmetrics)
redistmetrics
offers support for 4 common input types and has examples
of each, all based on New Hampshire:
data(nh)
This example is based on comp_polsby()
for the Polsby Popper
compactness, but comp_polsby()
can be substituted for any implemented
measure!
For a single plan, we can pass the single plan to the input. We also
pass an argument to shp
which takes in an sf
dataframe. r_2020
here is the Republican proposal for New Hampshire’s congressional
districts.
comp_polsby(plans = nh$r_2020, shp = nh)
#> [1] 0.2324375 0.1582763
The output here is a numeric vector, where each entry is the output for a district. The first district here has a compactness of about 0.23 and the second district has a compactness of about 0.16.
Now, if you’re redistricting in R, we recommend using the R package
redist
. In which case, you would have a redist_map
object.
We can load an example here with:
data(nh_map)
For redist maps, the workflow is identical!
comp_polsby(plans = nh_map$r_2020, shp = nh)
#> [1] 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
or 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.
data(nh_m)
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)
#> [1] 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):
data(nh_plans)
The benefit of using a redist_plans
object is that we can cleanly
mutate
into it using the .
shortcut:
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 redist_plans
object:
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.
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