eiCompare: Compares EI, Goodman, RxC Estimates

ei_rc_congruence <- function(ei_rc_table, cand_race, group_race) {
  
  dat <- ei_rc_table@data #extract data table
  dat[,1] <- as.character(dat[,1]) # convert to character
  # Up to 4 groups
  n_groups <- length( ei_rc_table@groups )
  cat(paste0("Number of Groups is: ", n_groups, "\n"))
  if (n_groups == 2) { # Number of groups = 2
    dat <- dat[, c(1:3, 5:6)]
  } else if (n_groups == 3) { # Number of groups = 3
    dat <- dat[, c(1:3, 5:6, 8:9)]
  } else { # Number of groups = 4
    dat <- dat[, c(1:3, 5:6, 8:9, 11:12)]
  } 
  # Set dataframe to just candidates
  dat <- dat[dat$Candidate !="se" & dat$Candidate != "Total",]
  dat$cand_race <- cand_race
  dat <- dat[order(dat$cand_race),] # Sort so that minority candidate comes first
  cat(paste0("Number of Candidates is: ", nrow(dat), "\n"))
  # Use Switch to label
  dat$Candidate[1] <- switch(dat$cand_race[1], 
                             "1" = paste0("Latino Candidate: ",dat$Candidate[1]),
                             "2" = paste0("Black Candidate: ",dat$Candidate[1]),
                             "3" = paste0("Asian Candidate: ",dat$Candidate[1]),
                             "4" = paste0("White Candidate: ",dat$Candidate[1]))
  dat$Candidate[2] <- switch(dat$cand_race[2], 
                             "1" = paste0("Latino Candidate: ",dat$Candidate[2]),
                             "2" = paste0("Black Candidate: ",dat$Candidate[2]),
                             "3" = paste0("Asian Candidate: ",dat$Candidate[2]),
                             "4" = paste0("White Candidate: ",dat$Candidate[2]))
  cat(paste("MC1: ", dat$Candidate[1],"\n"))
  cat(paste("WC1: ", dat$Candidate[2],"\n"))  
  # Order Columns to 2 minority columns first two, whites/nons last 2
  if (4 %in% group_race) {
    col_pos <- which(group_race == 4)
    if (col_pos == 2) {
      whites <- dat[,c(1,4:5, ncol(dat))]
    } else {
      whites <- dat[,c(1,2:3, ncol(dat))]
    }
  } 
  if (3 %in% group_race) {
    col_pos <- which(group_race == 3)
    if (col_pos == 2) {
      asians <- dat[,c(1,4:5, ncol(dat))]
    } else {
      asians <- dat[,c(1,2:3, ncol(dat))]
    }
  } 
  if (2 %in% group_race) {
    col_pos <- which(group_race == 2)
    if (col_pos == 2) {
      blacks <- dat[,c(1,4:5, ncol(dat))]
    } else {
      blacks <- dat[,c(1,2:3, ncol(dat))]
    }
  }
  if (1 %in% group_race) {
    col_pos <- which(group_race == 1) # Assumes 2 column groups
    if (col_pos == 2) {
      latinos <- dat[,c(1,4:5, ncol(dat))]
    } else {
      latinos <- dat[,c(1,2:3, ncol(dat))]
    }
  }
  # Paste Minority group with White groups; will only be one of black asian latino
  if (exists("asians")) { # Asians
    dat <- cbind(asians[,1:3], whites[,2:3])
  } else if(exists("latinos")){ # Latinos
    dat <- cbind(latinos[,1:3], whites[,2:3])
  } else {
    dat <- cbind(blacks[,1:3], whites[,2:3])
  }
  
  # 2 x 2 #
  min_cand <- dat[1,] # Assuming
  white_cand <- dat[2,]
  
  # Congruence
  
  # Row 1 -- Min  
  ei_mv_minus_wv_for_mc <-  as.numeric ( min_cand[2] - min_cand[4] )
  rc_mv_minus_wv_for_mc <-  as.numeric ( min_cand[3] - min_cand[5] )
  cong_mv_minus_wv_for_mc <- 1 - (abs(ei_mv_minus_wv_for_mc - rc_mv_minus_wv_for_mc) / 
                                    abs( mean(c(ei_mv_minus_wv_for_mc, rc_mv_minus_wv_for_mc))))
  
  row1 <- round (c( ei_mv_minus_wv_for_mc, rc_mv_minus_wv_for_mc, cong_mv_minus_wv_for_mc ), 3)
  
  # Row 2 - Minority Candidate prefered by Minority Voters
  ei_mc_prefer <- as.character ( ifelse (min_cand[2] > white_cand[2], "Yes", "No" ) )
  rc_mc_prefer <- as.character ( ifelse (min_cand[3] > white_cand[3], "Yes", "No" ) )
  cong_pref <- ifelse ( base::all.equal(ei_mc_prefer, rc_mc_prefer), 1, 0 )
  
  row2 <- c( ei_mc_prefer, rc_mc_prefer, cong_pref )
  
  # Row 3 - Minority Candidate Preference Rate
  ei_min_cand_pref_rate <- as.numeric ( (min_cand[2] - white_cand[2]) / 2 )
  rc_min_cand_pref_rate <- as.numeric ( (min_cand[3] - white_cand[3]) / 2 )
  cong_min_cand_pref <- 1 - (abs (ei_min_cand_pref_rate -rc_min_cand_pref_rate ) / 
                               abs(mean (c (ei_min_cand_pref_rate, rc_min_cand_pref_rate))) )
  
  row3 <- round ( c ( ei_min_cand_pref_rate, rc_min_cand_pref_rate, cong_min_cand_pref ),3)
  
  # Row 4/5 -- Minority Candidate Blockd by White Voters
  ei_min_cand_block <- as.numeric ( (min_cand[4] - white_cand[4]) / 2 )
  rc_min_cand_block <- as.numeric ( (min_cand[5] - white_cand[5]) / 2 )
  cong_min_cand_block <- 1 - (abs (ei_min_cand_block -rc_min_cand_block ) / 
                                abs(mean (c (ei_min_cand_block, rc_min_cand_block))) )
  
  row5 <- round( c(ei_min_cand_block, rc_min_cand_block, cong_min_cand_block), 3)
  
  ei_block_yes <- ifelse(ei_min_cand_block < 0, "Yes", "No")
  rc_block_yes <- ifelse(rc_min_cand_block < 0, "Yes", "No")
  cong_block_score <- ifelse ( base::all.equal(ei_block_yes, rc_block_yes), 1, 0 )
  
  row4 <- c(ei_block_yes, rc_block_yes, cong_block_score)
  
  # Combine Minority scors
  all_mc <- rbind(row1, row2, row3, row4, row5)
  
  # Row 6: Model congruence
  row6 <- c("", "", mean(as.numeric(all_mc[,3]))) # For adding total congruence
  all_mc <- rbind(all_mc, row6)
  colnames(all_mc) <- c("EI", "RxC", "Congruence")
  row.names(all_mc) <- c("MV1-WV for MC1", "MC1 preferred by MV1", "MC1 preference rate",
                         "MC1 blocked by WV", "MC1 block rate", "MC Model Congruence")
  
  # Put rows 1-6 and 7-12 together; need to adjust the datatype, first
  all_mc <- data.frame(all_mc)
  all_mc$EI <- as.character(all_mc$EI)
  all_mc$RxC <- as.character(all_mc$RxC)
  all_mc$Congruence <- as.character(all_mc$Congruence)
  all_wc <- all_mc
  row.names(all_wc) <- c("MV1-WV for WC1", "WC1 preferred by WV1", "WC1 preference rate",
                         "WC1 blocked by MV1", "WC1 block rate", "WC Model Congruence")
  
  # Replace coefficient signs for "white candidate"
  for (i in c(1,3,5)){
    for (j in 1:2){
      all_wc[i,j] <- as.numeric(all_wc[i,j])*-1
    }
  }
  # Put it all back together
  mc_wc <- rbind(all_mc, all_wc)
  sw1 <- mc_wc[9,] # Get a little funky
  sw2 <- mc_wc[11,]
  mc_wc[9,] <- sw2
  mc_wc[11,] <- sw1
  final_row <- c("", "", mean(as.numeric(mc_wc[6,3]), as.numeric(mc_wc[12,3]) )) # For adding total congruence
  
  # Output
  out <- rbind(mc_wc, "Total Model Congruence Score"= final_row)
  
  return(out)    
  
}