Using R for Introductory Statistics : John Verzani

Multivariate data 123appropriate. The boxplot allows us to compare centers and spreads much more easily. Asthere are nine levels to the income variable, we wouldn’t want to specify the data withcommands like gestation [inc == 1]. Instead, we can use the model formula gestation ~inc with boxplot(). We read the formula as gestation is modeled by inc, which isinterpreted by boxplot() by splitting the variable gestation into pieces corresponding tothe values of inc and creating boxplots for each.We use this approach three times. The last appears in Figure 4.6, where the argumentvarwidth=TRUE is specified to show boxes with width depending on the relative size ofthe sample.> boxplot(gestation ~ inc, data=babies) # not yet> boxplot(gestation ~ inc, subset=gestation != 999 &inc != 98,+ data=babies) # better> boxplot(gestation ~ inc, subset=gestation != 999 &inc != 98,+ data=babies,varwidth=TRUE, # variable width to seesizes+ xlab="income level”, ylab="gestation (days)")Figure 4.6 Boxplot of gestation timesfor income levels4.3.2 The plot () function with model formulaBoth boxplot() and plot() are generic functions allowing the programmers of R to writemodel-formula interfaces to them. The boxplot() function always draws boxplots, butwe’ve seen already that the plot() function can draw many types of plots (depending onits first argument). In Example 3.6 the plot() command was called with a model formulaof the type numeric ~ numeric, resulting in a scatterplot. If x and y are paired, numericdata vectors, then the model formula y ~ x represents the model y i =β 0 +β 1 x i +ε i . Thetypical plot for viewing this type of model is the scatterplot.