# 11.6 Visualizing missing data =========================================== 11.6

myDF = data.frame(cbind(               # Create some data w/missing variables
  c(1, 3, 4, NA, 2, 5, NA, 7, 9, 3, 2, 5),
  c(7, 8, 2, 4, 3, 6, 9, 1, 0, 4, 2, NA),
  c(6, NA, 3, NA, 4, 6, 1, 9, NA, 2, NA, 8),
  c(5, 3, 2, NA, 2, 1, 8, 6, 7, 4, 7, 9)))

rownames(myDF) = paste(                # Paste together observation labels
  rep("Subject", 12),                  #  by concatenating "subject"
  c(seq(1:12)))                        #  with numbers 1-12

# Ceate a vector with the number of missing values for each observation

missobs = apply(myDF, 1,               # Create new variable w/num missing
  function(x) sum(is.na(x)))           #  for each observation

missobs = sort(missobs, decreasing=T)  # Sort from most to least missing

png(filename = "illustrations/fig-11-1-missing-values.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(1, 1, .25, .25))           # Set margins - no title

dotchart(missobs,                      # Create a plot of missingness
  las = 1,                             # Rotate the y labels by 90 degrees
  xlim = c(0, length(myDF[1,])),       # Set range of x axis to num of vars
  xlab = "Number of Missing Observations")

dev.off()                              # Output png