add rpv_normalize()

NAMESPACE

@@ -40,6 +40,7 @@ export(race_check_2_3)
 export(resolve_missing_vals)
 export(rpv_coef_plot)
 export(rpv_density)
+export(rpv_normalize)
 export(rpv_toDF)
 export(stdize_votes)
 export(stdize_votes_all)

R/rpv_normalize.R

@@ -0,0 +1,146 @@
+#' @export
+#' 
+#' @author Rachel Carroll <rachelcarroll4@@gmail.com>
+#' @author Loren Collingwood <lcollingwood@@unm.edu>
+#' 
+#' @title Normalize RPV results 
+#' 
+#' @description Create a dataframe of normalized RPV results when using the 
+#' cvap, vap, or bisg denominator method, i.e., take RPV results only among 
+#' people estimated to have voted.
+#' 
+#' @param ei_object Output from \code{ei_iter()} or \code{ei_rxc()}
+#' @param cand_cols A character vector of the candidate column names to be 
+#' normalized from \code{ei_object}. Only use candidate column name columns, 
+#' not the No Vote column.  
+#' @param race_cols A character vector of the racial group column names to
+#' be normalized from \code{ei_object}
+#' 
+#' @return Normalized RPV results in a data.frame
+#' @examples 
+#' \donttest{
+#' #library(eiCompare)
+#' #data("south_carolina")
+#' #prec_election_demog <- south_carolina[1:50,]
+#' 
+#' ## run rpv using eiCompare rxc method
+#' #rxcVote <-  ei_rxc(
+#' #  data = prec_election_demog,
+#' #  cand_cols = c('pct_mcmaster', 'pct_smith', 'pct_other_gov', 'pct_NoVote_gov'),
+#' #  race_cols = c('pct_white', 'pct_black', 'pct_race_other'),
+#' #  totals_col = "total_vap")
+#' 
+#' ## normalize results accounting for no vote using rpv_normalize()
+#' ## only include the candidate and race cols of interest for the rpv analysis
+#' #rpv_results <- rpv_normalize(
+#'  # ei_object = rxcVote,
+#'  # cand_cols = c('pct_mcmaster', 'pct_smith', 'pct_other_gov'), 
+#'  # race_cols = c('pct_white', 'pct_black')
+#' #)
+#' }
+
+rpv_normalize <- function(ei_object, cand_cols, race_cols){
+
+  #----------------------------      QC     -----------------------------------#
+
+  # Make sure ei_object is correct class
+  if( !inherits(ei_object, "eiCompare") ) { 
+    stop("ei_object must be an eiCompare output from ei_iter() or ei_rxc()")
+  }
+  # Make sure all cand_cols are in the ei_object
+  canddiff <- setdiff(
+    cand_cols,
+    unique(ei_object$estimates$cand)
+  )
+
+  if( length(canddiff) > 0 ) {
+    stop("cand_cols values are not found in ei_object")
+  }
+
+  # Make sure all race_cols are in the ei_object
+  racediff <- setdiff(
+    race_cols,
+    unique(ei_object$estimates$race)
+  )
+
+  if( length(racediff) > 0 ) {
+    stop("race_cols values are not found in ei_object")
+  }
+
+  #-----------------------     Helper Function    -----------------------------#
+  # Create calculation helper function (used in lapply below)
+  #     candNm = name of cand col in samplesDF (an element from cand_cols )  
+  #     raceNm = name of race/demographic samples contained in samplesDF (an 
+  #               elements from race_cols) 
+  #     samplesDF = data.frame with vote samples from eiCompare model for each 
+  #                 candidate in cand_cols and sums across all candidates. The samples
+  #                 in samplesDF will be associated with a single race from race_cols
+
+  sample_calcs <- function(candNm, raceNm, samplesDF){
+
+    # get share estimate
+    share.est <- samplesDF %>% 
+      dplyr::pull(candNm)/samplesDF$sum  
+
+    # get average
+    mean <- mean(share.est)  
+    # get lower and upper bounds
+    quantile <- quantile(share.est, c(.025, .975)) 
+
+    # compile results into a data.frame row
+    row <- data.frame("mean" = round(mean * 100,2), 
+                      "ci_95_lower" = round(quantile[1] * 100, 2), 
+                      "ci_95_upper" = round(quantile[2] * 100, 2))
+
+    # set col and row names
+    colnames(row) <- paste(raceNm, colnames(row), sep = ".")
+    rownames(row) <- candNm
+
+    # return results
+    return(row)
+  }
+
+  #------------------   Calculate Normalized RPV Results    --------------------#
+
+  # Initiate lists/vars
+  samples <- list()
+  results <- list()
+  ncands <- length(cand_cols)
+
+  # loop through race
+  for ( j in seq(length(race_cols))) { 
+
+    # store race name
+    race_j_name <- race_cols[j]
+
+    # get eiCompare model samples for each cand
+    for ( i in seq(ncands) ) { 
+      # vote samples
+      samples[[race_j_name]][[cand_cols[i]]] <- 
+        ei_object$district_samples[[paste(cand_cols[i], race_j_name, sep="_")]]
+    }
+
+    # create df of samples of all cands for a given demographic preference
+    race_j_samples <- samples[[j]]
+    samplesDF <- dplyr::bind_cols(race_j_samples)
+    # sum across all cand fields
+    samplesDF$sum <- as.numeric(apply(samplesDF[,1:ncands], 1, sum))
+
+    # use helper function to get normalized point estimates and lower/upper bounds
+    resultsList <- lapply(cand_cols, sample_calcs, 
+                          raceNm = race_j_name, samplesDF = samplesDF)
+
+    # store results
+    results[[j]] <- dplyr::bind_rows(resultsList)
+  }
+
+  # combine results into one dataframe
+  out <- dplyr::bind_cols(results)              
+
+  #------------------------    Print and Return    --------------------------#
+
+  #return as object
+  return(out)
+
+} 
+

R/rpv_toDF.R

@@ -12,7 +12,7 @@
 #'
 #' @param rpv_results RPV analysis results either from the output of 
 #' \code{ei_iter()} or \code{ei_rxc()} from the \code{eiCompare} package or from 
-#' the internal function \code{ci_cvap_full()}.
+#' the internal function \code{rpv_normalize()}.
 #' @param model A string indicating the model used to create \code{rpv_results}. 
 #' Examples include "ei", "rxc", "ei cvap", etc. 
 #' @param candidate A character vector of candidate names written as they would 
@@ -100,7 +100,7 @@ rpv_toDF <- function(
     ) 
     sink()
 
-    # If ci_cvap_full output
+    # If rpv_normalize output
   } else if ( inherits(rpv_results, "data.frame") ){
 
     rpv_data <- data.frame(