Margot Tollefson - Visualizing Data in R 4: Graphics Using the base, graphics, stats, and ggplot2 Packages

Margot Tollefson

Visualizing Data in R 4

Graphics Using the base, graphics, stats, and ggplot2 Packages

1st ed.

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For Clay.

R provides many ways to visualize data. Graphics in the R language are generated by functions. Some functions create useful visualizations almost instantly. Other functions combine together to create highly coded sophisticated images. This book shows you how to generate both types of objects.

In the first part of this book, we look in detail at the plot() function the most basic and versatile of the plotting functions. The functions par(), layout(), and split.screen(), which set global plotting parameters and layout options, are also described, as well as graphics devices.

The second part covers the functions in the ggplot2 package, starting with qplot() and ggplot() . The functions qplot() and ggplot() are simpler to use in many ways than the earlier R functions. Truthfully, the syntax used in the ggplot2 package requires long strings of names and arguments but the autocomplete function in RStudio makes code entry quite easy.

The third part of the book includes six appendixes containing the canned plotting functions in the graphics and stats packages. The appendixes are sorted by the type of object to be displayed.

The function plot() takes an object or objects and creates an image based on the class of the object(s). There are many arguments to plot() that affect the appearance of the image. The arguments can be given values within the call to plot(), or many can be assigned globally using the function par(). After the initial call to plot(), there are several ancillary functions that can be used to add to the image. If having more than one plot on a page is the goal of the user, the functions par(), layout() , or split.screen() can be used to set up the format for multiple plots.

In Chapter , the possible arguments to the function par() are described, and a way to set up multiple plots using par(), layout() or split.screen() is given. Also, graphics devices are covered.

The functions qplot() and ggplot() in the ggplot2 package provide alternatives to plot(). Default plots generated by qplot() and ggplot() tend to look nicer than default plots generated by plot(). For simple plots, qplot() is sufficient to give an elegant-looking graphic. The function ggplot() provides for more plotting options than qplot(). Some of the syntax used in the ggplot2 package is not used in standard R.

In ggplot2, a theme for a graphic can be defined using theme() or by a canned theme function beginning with theme_. The objects to be plotted are mappings and are assigned in the function aes(). The type of image to be displayed is a geometry or statistic and is assigned by functions beginning with geom_ or stat_. The appearance of the graphic can be changed by using aes(), aes_ functions, geom_ functions, stat_ functions, and/or other formatting functions. More than one aesthetic, geometry, and statistic can be used to create an image. The appearance of the image can be changed by running a formatting function from within another function or by running a formatting function separately.

In Chapter goes over various functions in the ggplot2 package that also change the appearance of the image.