In this guest blog entry, Randall Pruim offers an alternative way based on a different formula interface. Here's Randall:
For a number of years I and several of my colleagues have been teaching R to beginners using an approach that includes a combination of
latticepackage for graphics,
- several functions from the
statspackage for modeling (e.g.,
lm(), t.test()), and
mosaicpackage for numerical summaries and for smoothing over edge cases and inconsistencies in the other two components.
goal ( y ~ x , data = mydata, ... )
Many data analysis operations can be executed by filling in four pieces of information (goal, y, x, and mydata) with the appropriate information for the desired task. This allows students to become fluent quickly with a powerful, coherent toolkit for data analysis.
Trouble in paradise
As the earlier post noted, the use of
latticehas some drawbacks. While basic graphs like histograms, boxplots, scatterplots, and quantile-quantile plots are simple to make with
lattice, it is challenging to combine these simple plots into more complex plots or to plot data from multiple data sources. Splitting data into subgroups and either overlaying with multiple colors or separating into sub-plots (facets) is easy, but the labeling of such plots is not as convenient (and takes more space) than the equivalent plots made with
ggplot2. And in our experience, students generally find the look of
ggplot2graphics more appealing.
On the other hand, introducing
ggplot2into a first course is challenging. The syntax tends to be more verbose, so it takes up more of the limited space on projected images and course handouts. More importantly, the syntax is entirely unrelated to the syntax used for other aspects of the course. For those adopting a “Less Volume, More Creativity” approach,
ggplot2is tough to justify.
ggformula: The third-and-a half way
Danny Kaplan and I recently introduced
ggformula, an R package that provides a formula interface to
ggplot2graphics. Our hope is that this provides the best aspects of
lattice(the formula interface and lighter syntax) and
ggplot2(modularity, layering, and better visual aesthetics).
For simple plots, the only thing that changes is the name of the plotting function. Each of these functions begins with
gf. Here are two examples, either of which could replace the side-by-side boxplots made with
latticein the previous post.
We can even overlay these two types of plots to see how they compare. To do so, we simply place what I call the "then" operator (
%>%, also commonly called a pipe) between the two layers and adjust the transparency so we can see both where they overlap.
Groups can be compared either by overlaying multiple groups distinguishable by some attribute (e.g., color)
or by creating multiple plots arranged in a grid rather than overlaying subgroups in the same space. The
ggformulapackage provides two ways to create these facets. The first uses
|very much like
latticedoes. Notice that the
gf_lm()layer inherits information from the the
gf_points()layer in these plots, saving some typing when the information is the same in multiple layers.
The second way adds facets with
gf_facet_grid()and can be more convenient for complex plots or when customization of facets is desired.
Fitting into the tidyverse work flow
ggformalaalso fits into a tidyverse-style workflow (arguably better than
ggplot2itself does). Data can be piped into the initial call to a
ggformulafunction and there is no need to switch between
+when moving from data transformations to plot operations.
The “Less Volume, More Creativity” approach is based on a common formula template that has served well for several years, but the arrival of
ggformulastrengthens this approach by bringing a richer graphical system into reach for beginners without introducing new syntactical structures. The full range of
ggplot2features and customizations remains available, and the
ggformulapackage vignettes and tutorials describe these in more detail.