We tend to make inferences about relationships between the objects that we see in ways that bear on our interpretation of graphical data, for example. Arrangements of points and lines on a page can encourage us—sometimes quite unconsciously—to make inferences about similarities, clustering, distinctions, and causal relationships that might or might not be there in the numbers. Sometimes these perceptual tendencies can be honestly harnessed to make our graphics more effective. At other times, they will tend to lead us astray, and must take care not to lean on them too much.

Illustrations like this demonstrate why it is worth looking at data. But that does not mean that looking at data is all one needs to do. Real datasets are messy, and while displaying them graphically is very useful, doing so presents problems of its own. As we will see below, there is considerable debate about what sort of visual work is most effective, when it can be superfluous, and how it can at times be misleading to researchers and audiences alike.

Wilkinson 2006 created the grammar of graphics to describe the fundamental features that underlie all statistical graphics.

The grammar of graphics is an answer to the question of what is a statistical graphic?

ggplot2 builds on Wilkinson’s grammar by focusing on the primacy of layers and adapting it for use in R.

In brief, the grammar tells us that a graphic maps the data to the aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars).

The plot may also include statistical transformations of the data and information about the plot’s coordinate system.

Facetting can be used to plot for different subsets of the data.

The combination of these independent components are what make up a graphic.