The names of the variables are in the cells of the main diagonal. Each off-diagonal cell shows the scatter plot for its row variable (on the y-axis) and its column variable (on the x-axis). For example, the scatter plot in the first row, second column shows MPG-city on the y-axis and price on the x-axis. In the second row, first column, the axes are reversed: MPG city is on the x-axis, and price is on the y-axis.
The R function for plotting this matrix is pairs(). To calculate the coordinates for all scatter plots, this function works with numerical columns from a matrix or a data frame.
For convenience, you create a data frame that's a subset of the Cars93 data frame. This new data frame consists of just the three variables to plot. The function subset() handles that nicely:
cars.subset <- subset(Cars93, select = c(MPG. city,Price,Horsepower))
The second argument to subset creates a vector of exactly what to select out of Cars93. Just to make sure the new data frame is the way you want it, use the head() function to take a look at the first six rows:
> head(cars.subset)
MPG.city Price Horsepower
1 25 15.9 140
2 18 33.9 200
3 20 29.1 172
4 19 37.7 172
5 22 30.0 208
6 22 15.7 110
And now,
> pairs(cars.subset)
creates the plot shown.
This capability isn't limited to three variables, nor to continuous ones. To see what happens with a different type of variable, add Cylinders to the vector for select and then use the pairs() function on cars.subset.
Box plots
To draw a box plot, you use a formula to show thatHorsepower is the dependent variable and Cylinders is the independent variable:> boxplot(Cars93$Horsepower ~ Cars93$Cylinders, xlab="Cylinders", ylab="Horsepower")
If you get tired of typing the $-signs, here's another way:
> boxplot(Horsepower ~ Cylinders, data = Cars93, xlab="Cylinders", ylab="Horsepower")
With the arguments laid out as in either of the two preceding code examples, plot() works exactly like boxplot().