Regression and Other Stories: Congress

Predictive uncertainty for congressional elections. See Chapter 2 in Regression and Other Stories.

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Load packages

library("rprojroot")
root<-has_file(".ROS-Examples-root")$make_fix_file()

Load and pre-process data

congress <- vector("list", 49)
for (i in 1:49){
  year <- 1896 + 2*(i-1)
  file <- root("Congress/data",paste(year, ".asc", sep=""))
  data_year <- matrix(scan(file), byrow=TRUE, ncol=5)
  data_year <- cbind(rep(year, nrow(data_year)), data_year)
  congress[[i]] <- data_year
}
region_name <- c("Northeast", "Midwest", "South", "West")

par(mfrow=c(3,5), mar=c(0.1,3,0,0), mgp=c(1.7, .3, 0), tck=-.02, oma=c(1,0,2,0))
for (i in c(27, 37, 47)) {
  year <- 1896 + 2*(i-1)
  cong1 <- congress[[i]]
  cong2 <- congress[[i+1]]
  state_code <- cong1[,2]
  region<- floor(state_code/20) + 1
  inc <- cong1[,4]
  dvote1 <- cong1[,5]/(cong1[,5] + cong1[,6])
  dvote2 <- cong2[,5]/(cong2[,5] + cong2[,6])
  contested <- (abs(dvote1 - 0.5)) < 0.3 & (abs(dvote2 - 0.5) < 0.3)
  plot(c(0, 1), c(0, 1), type="n", xlab="", ylab="", xaxt="n", yaxt="n", bty="n")
  text(0.8, 0.5, paste(year,"\nto\n", year+2, sep=""), cex=1.1)
  for (j in 1:4){
    plot(c(.2, .8), c(-.4, .3), type="n", xlab= "" , ylab=if (j==1) "Vote swing" else "", xaxt="n", yaxt="n", bty="n", cex.lab=.9)
    if (i==47) {
      text(c(.25, .5, .75), rep(-.4, 3), c("25%", "50%", "75%"), cex=.8)
      abline(-.35, 0, lwd=.5, col="gray60")
      segments(c(.25, .5, .75), rep(-.35, 35), c(.25, .5, .75), rep(-.37, 3), lwd=.5)
      mtext("Dem. vote in election 1", side=1, line=.2, cex=.5)
    }
    axis(2, c(-0.25, 0, 0.25), c("-25%", "0", "25%"),  cex.axis=.8)
    abline(0, 0)
    if (i==27) mtext(region_name[j], side=3, line=1, cex=.75)
    ok <- contested & abs(inc)==1 & region==j
    points(dvote1[ok], dvote2[ok] - dvote1[ok], pch=20, cex=.3, col="gray60")
    ok <- contested & abs(inc)==0 & region==j
    points(dvote1[ok], dvote2[ok] - dvote1[ok], pch=20, cex=.5, col="black")
  }
}