Getting Started with rict

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Overview

rict produces clean, publication-ready tables for redistricting analysis. It is designed to accompany maps from redist and related packages, summarizing district-level data on population, demographics, elections, compactness, contiguity, and administrative splits.

The package builds on two core R table libraries:

gt for formatted HTML output
tibble for data-frame output (set as_gt = FALSE)

Data

rict ships with two example datasets from West Virginia:

wv: a redist_map object with county-level demographic, partisan, and geographic data
wv_plans: a redist_plans object with sampled redistricting plans and the enacted 2020 congressional plan

data(wv)
data(wv_plans)

Quick start with `rict()`

The simplest entry point is rict(), which creates a summary table from a redist_map or redist_plans object:

rict(wv)
#> Warning: There was 1 warning in `dplyr::summarize()`.
#> ℹ In argument: `dplyr::across(where(is.numeric), sum, na.rm = TRUE)`.
#> ℹ In group 1: `district = 1`.
#> Caused by warning:
#> ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
#> Supply arguments directly to `.fns` through an anonymous function instead.
#> 
#>   # Previously
#>   across(a:b, mean, na.rm = TRUE)
#> 
#>   # Now
#>   across(a:b, \(x) mean(x, na.rm = TRUE))
#> ℹ The deprecated feature was likely used in the redist package.
#>   Please report the issue at <https://github.com/alarm-redist/redist/issues>.

district	pop	pop_white	pop_black	pop_hisp	pop_aian	pop_asian	pop_nhpi	pop_other	pop_two	vap	vap_white	vap_black	vap_hisp	vap_aian	vap_asian	vap_nhpi	vap_other	vap_two	cd_2010	cd_2020	pre_20_rep_tru	pre_20_dem_bid	ndv	adv_20	nrv	arv_20	sample_1	sample_2	sample_3	sample_4	sample_5	sample_6	sample_7	sample_8	sample_9	sample_10	sample_11	sample_12	state
1	896067	805369	36345	11331	1632	6132	155	2136	32967	715682	649695	29039	7866	1426	5030	116	1294	21216	73	28	273523	112947	112947	112947	273523	273523	37	31	36	35	38	33	35	36	31	31	37	36	WV
2	897649	793465	28404	23496	1555	8771	274	2516	39168	717250	643345	22963	15617	1335	7203	217	1687	24883	35	54	271859	123037	123037	123037	271859	271859	52	53	50	53	51	51	53	53	53	52	51	53	WV

For a redist_plans object, specify which plan to display:

rict(wv_plans, "cd_2020")

district	total_pop	chain	pop_overlap	total_vap	plan_dev	comp_edge	comp_polsby	pop_white	pop_black	pop_hisp	pop_aian	pop_asian	pop_nhpi	pop_other	pop_two	vap_white	vap_black	vap_hisp	vap_aian	vap_asian	vap_nhpi	vap_other	vap_two	pre_20_rep_tru	pre_20_dem_bid	adv_20	arv_20	county_splits	ndv	nrv	ndshare	e_dvs	pr_dem	e_dem	pbias	egap
1	896067	NA	1	715682	0.000881968	0.94	0.26271	805369	36345	11331	1632	6132	155	2136	32967	649695	29039	7866	1426	5030	116	1294	21216	273523	112947	112947	273523	0	112947	273523	0.2923	0.2923	0	0	0	0.104
2	897649	NA	1	717250	0.000881968	0.94	0.13358	793465	28404	23496	1555	8771	274	2516	39168	643345	22963	15617	1335	7203	217	1687	24883	271859	123037	123037	271859	0	123037	271859	0.3116	0.3116	0	0	0	0.104

Specialized tables

For more targeted analysis, use the rict_* family of functions. Each accepts a map and plan argument, and returns either a gt table (default) or a tibble.

Population parity

rict_population() shows total population per district and deviation from the ideal (equal) population:

rict_population(map = wv, plan = wv$cd_2020)

District	Population	Deviation
District	Population	People	%
1	896,067	−791	−0.1%
2	897,649	791	0.1%

Demographics

rict_demographics() breaks down population and voting-age population (VAP) by race and ethnicity. By default, subcategory columns are normalized to proportions:

rict_demographics(map = wv, plan = wv$cd_2020)

District	Total Population									Voting Age Population
District	Pop.	White	Black	Hispanic	Native	Asian	NH/PI	Other	Two+	VAP	White	Black	Hispanic	Native	Asian	NH/PI	Other	Two+
1	896,067	89.9%	4.1%	1.3%	0.2%	0.7%	0.0%	0.2%	3.7%	715,682	90.8%	4.1%	1.1%	0.2%	0.7%	0.0%	0.2%	3.0%
2	897,649	88.4%	3.2%	2.6%	0.2%	1.0%	0.0%	0.3%	4.4%	717,250	89.7%	3.2%	2.2%	0.2%	1.0%	0.0%	0.2%	3.5%

Set normalize = FALSE to see raw counts:

rict_demographics(map = wv, plan = wv$cd_2020, normalize = FALSE)

District	Total Population									Voting Age Population
District	Pop.	White	Black	Hispanic	Native	Asian	NH/PI	Other	Two+	VAP	White	Black	Hispanic	Native	Asian	NH/PI	Other	Two+
1	896,067	80,536,900.0%	3,634,500.0%	1,133,100.0%	163,200.0%	613,200.0%	15,500.0%	213,600.0%	3,296,700.0%	715,682	64,969,500.0%	2,903,900.0%	786,600.0%	142,600.0%	503,000.0%	11,600.0%	129,400.0%	2,121,600.0%
2	897,649	79,346,500.0%	2,840,400.0%	2,349,600.0%	155,500.0%	877,100.0%	27,400.0%	251,600.0%	3,916,800.0%	717,250	64,334,500.0%	2,296,300.0%	1,561,700.0%	133,500.0%	720,300.0%	21,700.0%	168,700.0%	2,488,300.0%

Elections

rict_elections() summarizes partisan election data. It automatically detects individual contest columns (e.g., pre_20_dem_bid), cycle-average columns (adv_20, arv_20), and normal vote columns (ndv, nrv), computing Democratic vote share for each:

rict_elections(map = wv, plan = wv$cd_2020)

District	Average Dem. Vote Share	Contests Dem. Vote Share	Cycle Dem. Vote Share
District	Pre-Average	Pres 2020	2020
1	29.2%	29.2%	29.2%
2	31.2%	31.2%	31.2%

Compactness

rict_compactness() computes geometric compactness measures via redistmetrics. The default measures are Polsby-Popper, Schwartzberg, Reock, and Convex Hull:

rict_compactness(map = wv, plan = wv$cd_2020)

District	Polsby-Popper	Schwartzberg	Reock	Convex Hull
1	35.0%	59.2%	46.8%	79.6%
2	17.4%	41.7%	26.7%	50.1%

You can supply custom measures as a named list of functions:

rict_compactness(
  map = wv, plan = wv$cd_2020,
  measures = list(
    "comp_polsby" = redistmetrics::comp_polsby,
    "comp_lw" = redistmetrics::comp_lw
  )
)

District	Polsby-Popper	Length Width
1	35.0%	70.0%
2	17.4%	71.8%

Contiguity

rict_contiguity() checks how many connected pieces each district has:

rict_contiguity(map = wv, plan = wv$cd_2020)

District	Pieces
1	1
2	1

Administrative splits

rict_splits() counts how administrative boundaries (e.g., counties, states) are split across districts:

rict_splits(map = wv, plan = wv$cd_2020, admin = "state")

District	admin_state	total_state	multi_state
1	1	1	0
2	1	1	0

Component populations

rict_component() breaks down population by administrative unit within each district:

rict_component(map = wv, plan = wv$cd_2020, admin = "county")

county	pop
1
005	21,809
007	12,447
011	94,350
013	6,229
015	8,051
019	40,488
021	7,408
025	32,977
035	27,791
039	180,745
043	20,463
045	32,567
047	19,111
053	25,453
055	59,664
059	23,568
063	12,376
067	24,604
071	6,143
075	7,869
079	57,440
081	74,591
087	14,028
089	11,959
099	38,982
101	8,378
105	5,194
109	21,382
2
001	15,465
003	122,076
009	22,559
017	7,808
023	10,976
027	23,093
029	29,095
031	14,299
033	65,921
037	57,701
041	17,033
049	56,205
051	30,591
057	26,938
061	105,822
065	17,063
069	42,425
073	7,653
077	34,216
083	27,932
085	8,444
091	16,705
093	6,762
095	8,313
097	23,816
103	14,442
107	84,296

Getting tibbles instead of gt tables

Every rict_* function accepts as_gt = FALSE to return a tibble instead of a gt table, making it easy to use the results for further computation:

rict_elections(map = wv, plan = wv$cd_2020, as_gt = FALSE)
#> # A tibble: 2 × 4
#>   District ndshare pre_20 avg_20
#>      <int>   <dbl>  <dbl>  <dbl>
#> 1        1   0.292  0.292  0.292
#> 2        2   0.312  0.312  0.312

Extending gt tables

rict provides several helpers for working with gt tables:

Partisan color scales

data_color_party() applies a red-to-blue color scale for Democratic vote share columns:

rict(wv_plans, "cd_2020") |>
  data_color_party(columns = "e_dvs")

district	total_pop	chain	pop_overlap	total_vap	plan_dev	comp_edge	comp_polsby	pop_white	pop_black	pop_hisp	pop_aian	pop_asian	pop_nhpi	pop_other	pop_two	vap_white	vap_black	vap_hisp	vap_aian	vap_asian	vap_nhpi	vap_other	vap_two	pre_20_rep_tru	pre_20_dem_bid	adv_20	arv_20	county_splits	ndv	nrv	ndshare	e_dvs	pr_dem	e_dem	pbias	egap
1	896067	NA	1	715682	0.000881968	0.94	0.26271	805369	36345	11331	1632	6132	155	2136	32967	649695	29039	7866	1426	5030	116	1294	21216	273523	112947	112947	273523	0	112947	273523	0.2923	0.2923	0	0	0	0.104
2	897649	NA	1	717250	0.000881968	0.94	0.13358	793465	28404	23496	1555	8771	274	2516	39168	643345	22963	15617	1335	7203	217	1687	24883	271859	123037	123037	271859	0	123037	271859	0.3116	0.3116	0	0	0	0.104

Geometry plots

gt_plot_sf() embeds small maps of sf geometries into a gt table:

wv_dist <- wv |> dplyr::group_by(cd_2020) |> dplyr::summarize()
gt::gt(wv_dist) |> gt_plot_sf()

cd_2020	geom
1
2

Compactness plots

gt_plot_compactness() adds visual compactness overlays (e.g., equal-area circles, convex hulls) to a table of compactness metrics:

rict(wv_plans, "cd_2020") |>
  gt_plot_compactness(wv, wv$cd_2020)

district	total_pop	chain	pop_overlap	total_vap	plan_dev	comp_edge	comp_polsby	pop_white	pop_black	pop_hisp	pop_aian	pop_asian	pop_nhpi	pop_other	pop_two	vap_white	vap_black	vap_hisp	vap_aian	vap_asian	vap_nhpi	vap_other	vap_two	pre_20_rep_tru	pre_20_dem_bid	adv_20	arv_20	county_splits	ndv	nrv	ndshare	e_dvs	pr_dem	e_dem	pbias	egap	gg_polsby
1	896067	NA	1	715682	0.000881968	0.94	0.26271	805369	36345	11331	1632	6132	155	2136	32967	649695	29039	7866	1426	5030	116	1294	21216	273523	112947	112947	273523	0	112947	273523	0.2923	0.2923	0	0	0	0.104
2	897649	NA	1	717250	0.000881968	0.94	0.13358	793465	28404	23496	1555	8771	274	2516	39168	643345	22963	15617	1335	7203	217	1687	24883	271859	123037	123037	271859	0	123037	271859	0.3116	0.3116	0	0	0	0.104

Other gt utilities

gt_get_data(): extract the underlying tibble from a gt table
gt_hide_lists(): hide list-columns (e.g., adjacency lists) from display

tab <- rict(wv)
gt_get_data(tab)
#> # A tibble: 2 × 40
#>   district    pop pop_white pop_black pop_hisp pop_aian pop_asian pop_nhpi
#>      <int>  <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>    <dbl>
#> 1        1 896067    805369     36345    11331     1632      6132      155
#> 2        2 897649    793465     28404    23496     1555      8771      274
#> # ℹ 32 more variables: pop_other <dbl>, pop_two <dbl>, vap <dbl>,
#> #   vap_white <dbl>, vap_black <dbl>, vap_hisp <dbl>, vap_aian <dbl>,
#> #   vap_asian <dbl>, vap_nhpi <dbl>, vap_other <dbl>, vap_two <dbl>,
#> #   cd_2010 <int>, cd_2020 <int>, pre_20_rep_tru <dbl>, pre_20_dem_bid <dbl>,
#> #   ndv <dbl>, adv_20 <dbl>, nrv <dbl>, arv_20 <dbl>, sample_1 <int>,
#> #   sample_2 <int>, sample_3 <int>, sample_4 <int>, sample_5 <int>,
#> #   sample_6 <int>, sample_7 <int>, sample_8 <int>, sample_9 <int>, …

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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Getting Started with rict

Overview

Data

Quick start with rict()

Specialized tables

Population parity

Demographics

Elections

Compactness

Contiguity

Administrative splits

Component populations

Getting tibbles instead of gt tables

Extending gt tables

Partisan color scales

Geometry plots

Compactness plots

Other gt utilities

Quick start with `rict()`