Visualizing polarization in congress

plyr_NOMINATE.R

# minor changes from script by dsparks
# Letting plyr do the work for us.
# 

# Install and load needed packages
doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("foreign", "plyr", "Hmisc", "ggplot2")
toInstall <- toInstall[!toInstall%in%library()$results[,1]] #check if present
if(doInstall&length(toInstall)!=0)
     {install.packages(toInstall, repos = "http://cran.r-project.org")}
lapply(toInstall , library, character.only = TRUE)

# See: http://voteview.org/dwnominate.asp
dwNominate <- read.dta("ftp://voteview.com/junkord/HL01111E21_PRES.DTA")

# Make a re-coded party variable
dwNominate$majorParty <- "Other"
dwNominate$majorParty[dwNominate$party == 100] <- "Democrat"
dwNominate$majorParty[dwNominate$party == 200] <- "Republican"
head(dwNominate)

# Letting plyr do the work for us (most of the functions are from Hmisc)
aggregatedIdeology <- ddply(.data = dwNominate,
                            .variables = .(cong, majorParty),
                            .fun = summarise,  # Allows the following:
                            Median = wtd.quantile(dwnom1, 1/bootse1, 1/2),
                            q25 = wtd.quantile(dwnom1, 1/bootse1, 1/4),
                            q75 = wtd.quantile(dwnom1, 1/bootse1, 3/4),
                            q05 = wtd.quantile(dwnom1, 1/bootse1, 1/20),
                            q95 = wtd.quantile(dwnom1, 1/bootse1, 19/20),
                            N = length(dwnom1),
                            .progress = "text")  # Because we can.
aggregatedIdeology$majorParty <- factor(aggregatedIdeology$majorParty,
                                 levels = c("Republican", "Democrat", "Other"))
head(aggregatedIdeology)  # All of our stats, calculated "by" our .variables

# Plot of ideological distributions
zp1 <- ggplot(aggregatedIdeology,
              aes(x = cong, y = Median,
                  ymin = q05, ymax = q95,
                  colour = majorParty, alpha = N))
zp1 <- zp1 + geom_linerange(aes(ymin = q25, ymax = q75),  # Plot the 90% CI
                             size = 1)  # it inherits x, y, colour and alpha
zp1 <- zp1 + geom_pointrange(size = 1/2)  # Plot the IQR
zp1 <- zp1 + scale_colour_brewer(palette = "Set1")
zp1 <- zp1 + theme_bw()
zp1 <- zp1 + ylab("Liberal-Conservative") + xlab("")
zp1 <- zp1 + scale_alpha(guide="none") 
zp1 <- zp1 + opts(panel.grid.major =  theme_line(colour = NA),
             panel.grid.minor =  theme_line(colour = NA),
             axis.text.x = theme_text(size=16),
             axis.text.y = theme_text(colour="white"),
             axis.title.y = theme_text(size=16,angle=90),
             title = expression("Polarization in U.S. Congress"))
print(zp1)

uses older ggplot2