Graphics Reference
In-Depth Information
▪ Scalescontrol the mapping from the values in the data space to values in the aesthetic space.
A continuous yscale maps larger numerical values to vertically higher positions in space.
▪ Guidesshow the viewer how to map the visual properties back to the data space. The most
commonly used guides are the tick marks and labels on an axis.
Here's an example of how a typical mapping works. You have data, which is a set of numerical
or categorical values. You have geomsto represent each observation. You have an aesthetic, such
as y(vertical) position. And you have a scale, which defines the mapping from the data space
(numeric values) to the aesthetic space (vertical position). A typical linear y-scale might map the
value 0 to the baseline of the graph, 5 to the middle, and 10 to the top. A logarithmic yscale
would place them differently.
These aren't the only kinds of data and aesthetic spaces possible. In the abstract grammar of
graphics, the data and aesthetics could be anything; in the ggplot2 implementation, there are
some predetermined types of data and aesthetics. Commonly used data types include numeric
values, categorical values, and text strings. Some commonly used aesthetics include horizontal
and vertical position, color, size, and shape.
To interpret the graph, viewers refer to the guides. An example of a guide is the y-axis, including
the tick marks and labels. The viewer refers to this guide to interpret what it means when a point
is in the middle of the scale. A legendis another type of scale. A legend might show people what
it means for a point to be a circle or a triangle, or what it means for a line to be blue or red.
Some aesthetics can only work with categorical variables, such as the shape of a point: triangles,
circles, squares, etc. Some aesthetics work with categorical or continuous variables, such as x
(horizontal) position. For a bar graph, the variable must be categorical—it would make no sense
for there to be a continuous variable on the x-axis. For a scatter plot, the variable must be numer-
ic. Both of these types of data (categorical and numeric) can be mapped to the aesthetic space of
xposition, but they require different types of scales.
NOTE
In ggplot2 terminology, categorical variables are called discrete, and numeric variables are called con-
tinuous. These terms may not always correspond to how they're used elsewhere. Sometimes a variable
that is continuous in the ggplot2 sense is discrete in the ordinary sense. For example, the number of
visible sunspots must be an integer, so it's numeric (continuousto ggplot2) and discrete (in ordinary
language).
