# Read in data
myData = read.delim(file = "http://www.kktg.net/R/Chapter12Data.txt",
  colClasses = c("character", "numeric", "numeric", "numeric"), header = T)

# Create categorical versions of the dem/polity data
myData$dem = 
  ifelse(myData$P4 > 7, 1, 0)          # Dem if Polity >7    
myData$corrupt = 
  ifelse(myData$CPI < 4, 1, 0)         # Corrupt if CPI<4
myData$cGDPk = myData$cGDP/1000        # Per capita GDP in 1000s

# Remove all observations with missing data
myData = myData[!is.na(myData$CPI) & !is.na(myData$P4),]

# 12.8 Mosaic Plots ======================================================= 12.8
# There are two steps here.  First we have to transform the data into a
# contingency table.  This is an array that shows the frequency of observations
# at each combination of the variables of interest.

# A simple 2x2 table

myTable = table(                       # Create a contingency table
  myData$dem,                          #   with x = dem/not dem
  myData$corrupt)                      #   and y = corrupt/not corrupt

myTable                                # Display contingency table

png(filename = "illustrations/fig-12-16-mosaicplot1.png",
  units = "in",                        # Set measurements in inches
  res = 1200,                          # Set resolution at 1200dpi
  width = 6,                           # Width at 6 inches
  height = 4)                          # Height at 4 inches

par(mai = c(.5, .5, .5, .25))          # Change margins for png graphs


mosaicplot(myTable,                    # Create mosaic plot for myTable    
  main = "Democracy & Corruption",     # Set main title
  xlab = "Dem",                        # Set x axis label
  ylab = "Corrupt")                    # Set y axis label

dev.off()                              # Output png file