Title:	Download, Merge, and Process Redistricting Data
Version:	0.2.4
Description:	Utility functions to download and process data produced by the ALARM Project, including 2020 redistricting files Kenny and McCartan (2021) https://alarm-redist.org/posts/2021-08-10-census-2020/ and the 50-State Redistricting Simulations of McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.7910/DVN/SLCD3E>. The package extends the data introduced in McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) <doi:10.1038/s41597-022-01808-2> to also include states with only a single district. The package also includes the Japanese 2022 redistricting files from the 47-Prefecture Redistricting Simulations of Miyazaki, Yamada, Yatsuhashi, and Imai (2022) <doi:10.7910/DVN/Z9UKSH>.
Depends:	R (≥ 4.1.0)
Imports:	rlang, cli, curl, dplyr, readr, stringr, sf, dataverse, censable, geomander (≥ 2.1.0), tidyselect, redist (≥ 4.2.0), redistmetrics, tinytiger, rappdirs
Suggests:	rstudioapi, rmapshaper, testthat (≥ 3.0.0), spelling
License:	MIT + file LICENSE
URL:	https://github.com/alarm-redist/alarmdata/, https://alarm-redist.org/alarmdata/
BugReports:	https://github.com/alarm-redist/alarmdata/issues/
Config/testthat/edition:	3
Encoding:	UTF-8
RoxygenNote:	7.3.3
Language:	en-US
NeedsCompilation:	no
Packaged:	2025-12-12 14:10:38 UTC; chris
Author:	Cory McCartan [aut], Christopher T. Kenny [aut, cre], Tyler Simko [aut], Michael Zhao [aut], Sho Miyazaki [aut], Kosuke Imai [aut]
Maintainer:	Christopher T. Kenny <ctkenny@proton.me>
Repository:	CRAN
Date/Publication:	2025-12-12 15:50:12 UTC

alarmdata: Download, Merge, and Process Redistricting Data

Description

Utility functions to download and process data produced by the ALARM Project, including 2020 redistricting files Kenny and McCartan (2021) https://alarm-redist.org/posts/2021-08-10-census-2020/ and the 50-State Redistricting Simulations of McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) doi:10.7910/DVN/SLCD3E. The package extends the data introduced in McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki, and Imai (2022) doi:10.1038/s41597-022-01808-2 to also include states with only a single district. The package also includes the Japanese 2022 redistricting files from the 47-Prefecture Redistricting Simulations of Miyazaki, Yamada, Yatsuhashi, and Imai (2022) doi:10.7910/DVN/Z9UKSH.

Author(s)

Maintainer: Christopher T. Kenny ctkenny@proton.me (ORCID)

Authors:

• Cory McCartan mccartan@psu.edu (ORCID)

• Tyler Simko tsimko@g.harvard.edu (ORCID)

• Michael Zhao michaelzhao@college.harvard.edu (ORCID)

• Sho Miyazaki shomiya@stanford.edu (ORCID)

• Kosuke Imai imai@harvard.edu (ORCID)

See Also

Useful links:

• https://github.com/alarm-redist/alarmdata/

• https://alarm-redist.org/alarmdata/

• Report bugs at https://github.com/alarm-redist/alarmdata/issues/

Download maps and plans from the 50-State Simulation Project

Description

These functions will download redist_map and redist_plans objects for the 50-State Simulation Project from the ALARM Project's Dataverse. alarm_50state_doc() will download documentation for a particular state and show it in a browser. alarm_50state_stats will download just the summary statistics for a state.

Usage

alarm_50state_map(state, year = 2020, refresh = FALSE)

alarm_50state_plans(
  state,
  stats = TRUE,
  year = 2020,
  refresh = FALSE,
  compress = "xz"
)

alarm_50state_stats(state, year = 2020, refresh = FALSE)

alarm_50state_doc(state, year = 2020)

Arguments

Details

Every decade following the Census, states and municipalities must redraw districts for Congress, state houses, city councils, and more. The goal of the 50-State Simulation Project is to enable researchers, practitioners, and the general public to use cutting-edge redistricting simulation analysis to evaluate enacted congressional districts.

Evaluating a redistricting plan requires analysts to take into account each state’s redistricting rules and particular political geography. Comparing the partisan bias of a plan for Texas with the bias of a plan for New York, for example, is likely misleading. Comparing a state’s current plan to a past plan is also problematic because of demographic and political changes over time. Redistricting simulations generate an ensemble of alternative redistricting plans within a given state which are tailored to its redistricting rules. Unlike traditional evaluation methods, therefore, simulations are able to directly account for the state’s political geography and redistricting criteria.

Value

For alarm_50state_map(), a redist_map. For alarm_50state_plans(), a redist_plans. For alarm_50state_doc(), invisibly returns the path to the HTML documentation, and also loads an HTML file into the viewer or web browser. For alarm_50state_stats(), a tibble.

Examples

# requires Harvard Dataverse API key
alarm_50state_map("WA")
alarm_50state_plans("WA", stats = FALSE)
alarm_50state_stats("WA")
alarm_50state_doc("WA")

map <- alarm_50state_map("WY")
pl <- alarm_50state_plans("WY")

Add a reference plan to a set of plans

Description

Facilitates comparing an existing (i.e., non-simulated) redistricting plan to a set of simulated plans.

Usage

alarm_add_plan(
  plans,
  ref_plan,
  map = NULL,
  name = NULL,
  calc_polsby = FALSE,
  GEOID = "GEOID",
  year = 2020
)

Arguments

Value

A modified redist_plans object containing the reference plan. Includes summary statistics if the original redist_plans object had them as well.

Examples

# requires Harvard Dataverse API key
map <- alarm_50state_map("WY")
pl <- alarm_50state_plans("WY")
pl_new <- alarm_add_plan(pl, ref_plan = c(1), map, name = "example")

# download and load a comparison plan
url <- paste0("https://github.com/PlanScore/Redistrict2020/raw/main/files/",
  "NM-2021-10/Congressional_Concept_A.zip")
tf <- tempfile(fileext = ".zip")
utils::download.file(url, tf)
utils::unzip(tf, exdir = dirname(tf))
baf <- readr::read_csv(file = paste0(dirname(tf), "/Congressional Concept A.csv"),
                       col_types = "ci")
names(baf) <- c("GEOID", "concept_a")
# Add it to the plans object
map_nm <- alarm_50state_map("NM")
plans_nm <- alarm_50state_plans("NM", stats = FALSE)
alarm_add_plan(plans_nm, baf, map = map_nm, name = "concept_a")

Work with the the alarmdata cache

Description

Functions to inspect and clear the cache. If the cache is not enabled, uses a temporary directory.

Usage

alarm_cache_size()

alarm_cache_clear(force = FALSE)

alarm_cache_path()

Arguments

Value

For alarm_cache_size(), the size in bytes, invisibly

For alarm_cache_clear(), the path to the cache, invisibly.

For alarm_cache_path(), the path to the cache

Examples

alarm_cache_size()

alarm_cache_clear()

alarm_cache_path()

Download Joined VEST and Census Data

Description

Downloads Census data joined with VEST's election data. All are re-tabulated from precincts collected by VEST to 2020 Census geographies.

Usage

alarm_census_vest(state, geometry = FALSE, epsg = alarm_epsg(state))

Arguments

Value

tibble with Census and election data

Examples

alarm_census_vest("DE", geometry = FALSE)

Suggested EPSG Codes

Description

Provides suggested EPSG codes for each of the 50 states. One of the NAD83 (HARN) coordinate systems for each state.

Usage

alarm_epsg(state)

Arguments

Value

A numeric EPSG code

Examples

alarm_epsg("NY")

Download maps and plans from the Japan 47-Prefecture Simulation Project

Description

These functions will download redist_map and redist_plans objects for the Japan 47-Prefecture Simulation Project from the ALARM Project's Dataverse. alarm_japan_doc() will download documentation for a particular prefecture and show it in a browser. alarm_japan_stats will download just the summary statistics for a prefecture

Usage

alarm_japan_map(pref, year = 2022, refresh = FALSE)

alarm_japan_plans(
  pref,
  stats = TRUE,
  year = 2022,
  refresh = FALSE,
  compress = "xz"
)

alarm_japan_stats(pref, year = 2022, refresh = FALSE)

alarm_japan_doc(pref, year = 2022)

Arguments

Details

The goal of the 47-Prefecture Simulation Project is to generate and analyze redistricting plans for the single-member districts of the House of Representatives of Japan using a redistricting simulation algorithm. In this project, we analyzed the partisan bias of the 2022 redistricting for 25 prefectures subject to redistricting. Our simulations are designed to comply with the that the Council abides by.

Value

For alarm_japan_map(), a redist_map. For alarm_japan_plans(), a redist_plans. For alarm_japan_doc(), invisibly returns the path to the HTML documentation, and also loads an HTML file into the viewer or web browser. For alarm_japan_stats(), a tibble.

Examples

# requires Harvard Dataverse API key
alarm_japan_map("miyagi")
alarm_japan_plans("miyagi", stats = FALSE)
alarm_japan_stats("miyagi")
alarm_japan_doc("miyagi")

map <- alarm_japan_map("miyagi")
pl <- alarm_japan_plans("miyagi")