You first encountered faceting in Section 2.5. Faceting generates small multiples each showing a different subset of the data. Small multiples are a powerful tool for exploratory data analysis: you can rapidly compare patterns in different parts of the data and see whether they are the same or different. This section will discuss how you can fine-tune facets, particularly the way in which they interact with position scales.
There are three types of faceting:
facet_null(): a single plot, the default.
facet_wrap(): “wraps” a 1d ribbon of panels into 2d.
facet_grid(): produces a 2d grid of panels defined by variables which form the rows and columns.
The differences between
facet_grid() are illustrated in Figure 16.1.
Faceted plots have the capability to fill up a lot of space, so for this chapter we will use a subset of the mpg dataset that has a manageable number of levels: three cylinders (4, 6, 8), two types of drive train (4 and f), and six classes.
subset(mpg, cyl != 5 & drv %in% c("4", "f") & class != "2seater")mpg2 <-