I have become quite a big fan of graphics that combine the features of traditional figures (e.g. bar charts, histograms, etc.) with tables. That is, the combination of numerical results with a visual representation has been quite useful for exploring descriptive statistics. I have wrapped two of my favorites (build around ggplot2) and included them as part of my irutils R package (currently under development). Here is the code and results utilizing two item from the 2009 Programme of International Student Assessment (PISA).

library(devtools)
install_github('irutils','jbryer')
library(irutils)
library(ggplot2)
data(pisa)
items29 = pisa[,substr(names(pisa), 1,5) == 'ST25Q']
names(items29) = c("Magazines", "Comic books", "Fiction", "Non-fiction books", "Newspapers")
for(i in 1:ncol(items29)) {
     items29[,i] = factor(items29[,i], levels=1:5,
     labels=c('Never or almost never', 'A few times a year', 'About once a month',
          'Several times a month', 'Several times a week'), ordered=TRUE)
}
plotHeatmapTable(items29) + opts(title="How often do you read these materials because you want to?")

items28 = pisa[,substr(names(pisa), 1,5) == 'ST24Q']
head(items28); ncol(items28)
names(items28) = c("I read only if I have to.",
				   "Reading is one of my favorite hobbies.",
				   "I like talking about books with other people.",
				   "I find it hard to finish books.",
				   "I feel happy if I receive a book as a present.",
				   "For me, reading is a waste of time.",
				   "I enjoy going to a bookstore or a library.",
				   "I read only to get information that I need.",
				   "I cannot sit still and read for more than a few minutes.",
				   "I like to express my opinions about books I have read.",
				   "I like to exchange books with my friends")
for(i in 1:ncol(items28)) {
	items28[,i] = factor(items28[,i], levels=1:4,
		labels=c('Strongly disagree', 'Disagree', 'Agree', 'Strongly Agree'), ordered=TRUE)
}
plotBarchartTable(items28, low.color='maroon', high.color='burlywood4')

plotBarchartTable(items28, grouping=pisa$CNT, low.color='maroon', high.color='burlywood4')