Package 'alarmdata'

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

state	A state name, abbreviation, FIPS code, or ANSI code.
year	The redistricting cycle to download. Currently only 2020 and 2010 are available.
refresh	If TRUE, ignore the cache and download again.
stats	If TRUE (the default), download summary statistics for each plan.
compress	The compression level used for caching redist_plans objects.

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

plans	A redist_plans object.
ref_plan	An integer vector containing the reference plan, a block assignment file as a tibble or data.frame, or an sf object where each row corresponds to a district.
map	A redist_map object. Only required if the redist_plans object includes summary statistics.
name	A human-readable name for the reference plan. Defaults to the name of ref_plan. If ref_plan is a tibble or data.frame, it should be the name of the column of ref_plan that identifies districts.
calc_polsby	A logical value indicating whether a Polsby-Popper compactness score should be calculated for the reference plan. Defaults to FALSE.
GEOID	character. If ref_plan is a tibble or data.frame, then it should correspond to the column of ref_plan that identifies block GEOIDs. If ref_plan is an sf object, then it should correspond to the column of ref_plan that identifies district numbers. Ignored when ref_plan is numeric. Default is 'GEOID'.
year	the decade to request if passing a tibble to ref_plan, either 2010 or 2020. Default is 2020.

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

force

FALSE by default. Asks the user to confirm if interactive. Does not clear cache if force is FALSE and not interactive.

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

state	A state name, abbreviation, FIPS code, or ANSI code.
geometry	If TRUE (default is FALSE), include sf geometry from Census Bureau TIGER Lines with the data.
epsg	A numeric EPSG code to use as the coordinate system. Default is alarm_epsg(state).

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

state

A state name, abbreviation, FIPS code, or ANSI code.

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

pref	A prefecture name
year	The redistricting cycle to download. Currently only 2022 is available.
refresh	If TRUE, ignore the cache and download again.
stats	If TRUE (the default), download summary statistics for each plan.
compress	The compression level used for caching redist_plans objects.

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")

---