eiCompare (version 3.0.0)

ei_homog: Homogeneous Precinct Analysis

Description

Creates matrix table of homogeneous precinct analysis results by racial/ethnic group. The idea, for example, is to get a basic sense of voting behavior by racial group, examine candidate preference in districts that are above 80% white, 80% black, etc.

Usage

ei_homog(
  data,
  cand_cols,
  race_cols,
  totals_col,
  cp = 0.8,
  warn_row = 5,
  verbose = FALSE
)

Arguments

data

A data.frame() object containing precinct-level turnout data by race and candidate

cand_cols

A character vector listing the column names for turnout for each candidate

race_cols

A character vector listing the column names for turnout by race

totals_col

The name of the column containing total votes cast in each precinct

cp

numeric; homogeneous precinct cut-point, e.g., 0.80; default = 0.80

warn_row

numeric; threshold number of precincts racial group must be above to conduct analysis; default = 5. For example, with three groups, whites, blacks, Hispanics, each group must have at least 5 precincts with at least 80% share of the population for that group. All racial groups need to have at least n number of precincts at or above warn_row level or error will be thrown.

verbose

A boolean indicating whether to print out status messages.

Value

matrix with homogeneous precinct results, columns = race groups, rows = candidates

Details

ei_homog

Examples

# NOT RUN {
# Toy data example
cand_a <- c( rep(.8, 10), rep(.2, 10))
cand_b <- 1 - cand_a
white <- c(rep(.7, 5), rep(.85, 5), rep(.1, 5), rep(.05, 5))
black <- 1 - white
total <- c ( rep(200, 5), rep(100, 5), rep(80, 5), rep(300, 5) )
toy <- data.frame(cand_a, cand_b, white, black, total)

# Default Example #
ei_homog(data = toy,
        race_cols = c("white", "black"),
        cand_cols = c("cand_a", "cand_b"),
        totals_col = "total")
           
# Verbosity Example #
ei_homog(data = toy,
        race_cols = c("white", "black"),
        cand_cols = c("cand_a", "cand_b"),
        totals_col = "total", 
        verbose = TRUE)
           
# Adjust Cut Point (cp) to 0.70
ei_homog(data = toy,
        race_cols = c("white", "black"),
        cand_cols = c("cand_a", "cand_b"),
        totals_col = "total", 
        cp = 0.70,
        verbose = TRUE)
           
# Set Precincts to anything above 3
ei_homog(data = toy,
        race_cols = c("white", "black"),
        cand_cols = c("cand_a", "cand_b"),
        totals_col = "total", 
        warn_row = 3,
        verbose = TRUE)

# }