A sample R session

# Data description:
#This example comes from a June 96 Consumer Reports article
rating 69 brands of beer.  The article gives several pieces of
information about each beer, including price, number of calories,
alcohol content, bitterness, maltyness, a quality rating, a category,
and an indication of where the beer are available (which we'll ignore
for the time being).

The  variables (with abbreviated names) are as follows:
price    price in dollars
qlty     quality rating (0 = worst, 100=best)
N        available in Northern US? (1=Yes, 0=No)
E  East, see N above
W  West, see N above
S  South, see N above
cal      number of calories per 12 oz serving
alc      percent alcohol
bitter   bitterness (0=less bitter, 100=most bitter)
malty    maltyness (0=less malty, 100=most malty)
class    beer category (1,2,3,4,5,6)
        1 = craft lager
        2 = craft ale   
        3 = imported lager
        4 = regular or ice beer
        5 = light beer
        6 = nonalcoholic 

R code
beer <- read.table(
'http://www.student.math.uwaterloo.ca/~stat441/stat441_09_01/beer.dat'
,head=TRUE,row.names=1)
# head option is for column names, row.names=1 says use first column
# of data for names of the rows.

# type the name of the object to see it displayed on the screen
beer
# you can also see/edit the data in a spreadsheet-like viewer
edit(beer)         # for looking only
beer <- edit(beer) # will actually save changes

# make a new variable with the types in no-numeric format
beer$type <- as.factor(
c('c.lager','c.ale','imp.lager','reg','light','nalc')[beer$class])

# basic summaries of each column
summary(beer)

# some graphical summaries
hist(beer$price)
library(lattice) #for histogram and xyplot commands

histogram(~malty|class,data=beer)

plot(beer$price,beer$quality)
plot(beer$malty,beer$bitter)

pairs(beer[,c('price','qlty','malty','bitter')])
pairs(beer[,c('price','qlty','malty','bitter')],col=beer$class,pch=19)
pairs(beer[,c('price','qlty','malty','bitter')],pch=beer$class)

xyplot(bitter~malty | type,data=beer)
coplot(bitter~malty | type,data=beer)  # coplot is similar here
coplot(bitter~malty | price,data=beer) # coplot shows bitter vs. malty
                                      # for different slices of "price"

# look at dependence of price on type
stripplot(price~type,data=beer,pch=19)

mylm <- lm(price~qlty+bitter+malty+type,data=beer)
summary(mylm)