eiExpand (version 1.0.5)

rpv_toDF: Transform RPV results from eiCompare into a simple dataframe object

Description

Create a dataframe from RPV analysis output to facilitate RPV visualizations. The output dataframe of this function can be used directly in rpv_plot().

Usage

rpv_toDF(
  rpv_results = NULL,
  model = NULL,
  jurisdiction = "",
  preferred_candidate = "",
  party = "",
  election_type = "",
  year = "",
  contest = "",
  candidate = ""
)

Value

rpv results in a data.frame

Arguments

rpv_results

RPV analysis results either from the output of ei_iter() or ei_rxc() from the eiCompare package or from the internal function ci_cvap_full().

model

A string indicating the model used to create rpv_results. Examples include "ei", "rxc", "ei cvap", etc.

jurisdiction

A string of the jurisdiction.

preferred_candidate

A character vector of races indicating racial preference of each candidate. The racial preferences must be listed in the correct order with respect to candidate.

party

A character vector containing the political parties of the candidates. Must be listed in the correct order with respect to candidate.

election_type

A string on the election type (usually "General" or "Primary")

year

The year of the contest

contest

A string of contest name as it would appear in an rpv visualization (e.g. "President" or "Sec. of State")

candidate

A character vector of candidate names written as they would appear on a visualization. The candidate names must be listed in the same order as the candidate estimates appear in rpv_results, i.e the same order as the cands argument in eiCompare::ei_iter() or eiCompare::ei_rxc().

Author

Rachel Carroll <[e-mail]>

Kassra Oskooii <[e-mail]>

Examples

# \donttest{
#library(eiExpand)
#data("south_carolina")
#prec_election_demog <- south_carolina[1:50,]

## run rpv analysis
#eiVote <- eiCompare::ei_iter(
 # data = prec_election_demog,
 # cand_cols = c('pct_mcmaster', 'pct_smith'),
 # race_cols = c('pct_white', 'pct_black'),
 # totals_col = "total_vap"
#) %>%
 # rpv_normalize(
 #   cand_cols = c('pct_mcmaster', 'pct_smith'), 
 #   race_cols = c('pct_white', 'pct_black')
 # )

## use function to create dataframe from rpv results
#plotDF <- rpv_toDF(
#   rpv_results = eiVote,
#   model = "ei vap", #since we used ei_iter model normalized with vap denominator       
#   jurisdiction = "Statewide",
#   candidate = c("McMaster", "Smith"), #must be in correct order relative to rpv_results                             
#   preferred_candidate = c("White", "Black"), #must be in correct order rpv_results  
#   party = c("Republican", "Democratic"),
#   election_type = "General",  
#   year = "2020",
#   contest = "Governor"
# )
# }