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CHAPTER 19 Advanced graphics with ggplot2 |
relationships through conditional one-, two-, or three-dimensional graphs called trellis graphs. The ggplot2 package provides a method of creating innovative graphs based on a comprehensive graphical “grammar.”
In this chapter, we’ll start with a brief overview of the four graphic systems. Then we’ll focus on graphs that can be generated with the ggplot2 package. ggplot2 greatly expands the range and quality of the graphs you can produce in R. It allows you to visualize datasets with many variables using a comprehensive and consistent syntax, and easily generate graphs that would be difficult to create using base R graphics.
19.1 The four graphics systems in R
As stated earlier, there are currently four graphical systems available in R. The base graphics system, written by Ross Ihaka, is included in every R installation. Most of the graphs produced in previous chapters rely on base graphics functions.
The grid graphics system, written by Paul Murrell (2011), is implemented through the grid package. grid graphics offer a lower-level alternative to the standard graphics system. The user can create arbitrary rectangular regions on graphics devices, define coordinate systems for each region, and use a rich set of drawing primitives to control the arrangement and appearance of graphic elements.
This flexibility makes grid graphics a valuable tool for software developers. But the grid package doesn’t provide functions for producing statistical graphics or complete plots. Because of this, the package is rarely used directly by data analysts and won’t be discussed further. If you’re interested in learning more about grid, visit Dr. Murrell’s Grid website (http://mng.bz/C86p) for details.
The lattice package, written by Deepayan Sarkar (2008), implements trellis graphs, as outlined by Cleveland (1985, 1993). Basically, trellis graphs display the distribution of a variable or the relationship between variables, separately for each level of one or more other variables. Built using the grid package, the lattice package has grown beyond Cleveland’s original approach to visualizing multivariate data and now provides a comprehensive alternative system for creating statistical graphics in R. A number of packages described in this book (effects, flexclust, Hmisc, mice, and odfWeave) use functions in the lattice package to produce graphs.
Finally, the ggplot2 package, written by Hadley Wickham (2009a), provides a system for creating graphs based on the grammar of graphics described by Wilkinson (2005) and expanded by Wickham (2009b). The intention of the ggplot2 package is to provide a comprehensive, grammar-based system for generating graphs in a unified and coherent manner, allowing users to create new and innovative data visualizations. The power of this approach has led ggplot2 to become an important tool for visualizing data using R.
Access to the four systems differs, as outlined in table 19.1. Base graphic functions are automatically available. To access grid and lattice functions, you must load the appropriate package explicitly (for example, library(lattice)). To access ggplot2 functions, you have to download and install the package (install.packages ("ggplot2")) before first use and then load it (library(ggplot2)).
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An introduction to the ggplot2 package |
439 |
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Table 19.1 Access to graphic systems |
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System |
Included in base installation? |
Must be explicitly loaded? |
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base |
Yes |
No |
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grid |
Yes |
Yes |
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lattice |
Yes |
Yes |
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ggplot2 |
No |
Yes |
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The lattice and ggplot2 packages overlap in functionality but approach the creation of graphs differently. Analysts tend to rely on one package or the other when plotting multivariate data. Given its power and popularity, the remainder of this chapter will focus on ggplot2. If you would like to learn more about the lattice package, I’ve created a supplementary chapter that you can download (www.statmethods
.net/RiA/lattice.pdf) or from the publisher’s website at www.manning.com/ RinActionSecondEdition.
This chapter uses three datasets to illustrate the use of ggplot2. The first is a data frame called singer that comes from the lattice package; it contains the heights and voice parts of singers in the New York Choral Society. The second is the mtcars data frame that you’ve used throughout this book; it contains automotive details on 32 automobiles. The final data frame is called Salaries and is included with the car package described in chapter 8. Salaries contains salary information for university professors and was collected to explore gender discrepancies in income. Together, these datasets offer a variety of visualization challenges.
Before continuing, be sure the ggplot2 and car packages are installed. You’ll also want to install the gridExtra package. It allows you to place multiple ggplot2 graphs into a single plot (see section 19.7.4).
19.2 An introduction to the ggplot2 package
The ggplot2 package implements a system for creating graphics in R based on a comprehensive and coherent grammar. This provides a consistency to graph creation that’s often lacking in R and allows you to create graph types that are innovative and novel. In this section, we’ll start with an overview of ggplot2 grammar; subsequent sections dive into the details.
In ggplot2, plots are created by chaining together functions using the plus (+) sign. Each function modifies the plot created up to that point. It’s easiest to see with an example (the graph is given in figure 19.1):
library(ggplot2)
ggplot(data=mtcars, aes(x=wt, y=mpg)) + geom_point() +
labs(title="Automobile Data", x="Weight", y="Miles Per Gallon")
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