The ggplot2 Plotting System - Part 1

• An implementation of The Grammar of Graphics by Leland Wilkinson
• Written by Hadley Wickham (while he was a graduate student at Iowa State)
• A “third” graphics system for R (along with base and lattice)
• Available from CRAN via install.packages()
• Web site: http://ggplot2.org (better documentation)

• Grammar of graphics represents an abstraction of graphics ideas/objects
• Think “verb”, “noun”, “adjective” for graphics
• Allows for a “theory” of graphics on which to build new graphics and graphics objects
• “Shorten the distance from mind to page”

“In brief, the grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system”

• from ggplot2 book

• “Artist’s palette” model
• Start with blank canvas and build up from there
• Start with plot function (or similar)
• Use annotation functions to add/modify (text, lines, points, axis)

• Convenient, mirrors how we think of building plots and analyzing data
• Can’t go back once plot has started (i.e. to adjust margins); need to plan in advance
• Difficult to “translate” to others once a new plot has been created (no graphical “language”)
  • Plot is just a series of R commands

• Plots are created with a single function call (xyplot, bwplot, etc.)
• Most useful for conditioning types of plots: Looking at how \(y\) changes with \(x\) across levels of \(z\)
• Things like margins/spacing set automatically because entire plot is specified at once
• Good for putting many many plots on a screen

• Sometimes awkward to specify an entire plot in a single function call
• Annotation in plot is not intuitive
• Use of panel functions and subscripts difficult to wield and requires intense preparation
• Cannot “add” to the plot once it’s created

• Split the difference between base and lattice
• Automatically deals with spacings, text, titles but also allows you to annotate by “adding”
• Superficial similarity to lattice but generally easier/more intuitive to use
• Default mode makes many choices for you (but you can customize!)

• Works much like the plot function in base graphics system
• Looks for data in a data frame, similar to lattice, or in the parent environment
• Plots are made up of aesthetics (size, shape, color) and geoms (points, lines)

• Factors are important for indicating subsets of the data (if they are to have different properties); they should be labeled
• The qplot() hides what goes on underneath, which is okay for most operations
• ggplot() is the core function and very flexible for doing things qplot() cannot do

library(ggplot2)
str(mpg)

'data.frame':   234 obs. of  11 variables:
 $ manufacturer: Factor w/ 15 levels "audi","chevrolet",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ model       : Factor w/ 38 levels "4runner 4wd",..: 2 2 2 2 2 2 2 3 3 3 ...
 $ displ       : num  1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
 $ year        : int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
 $ cyl         : int  4 4 4 4 6 6 6 4 4 4 ...
 $ trans       : Factor w/ 10 levels "auto(av)","auto(l3)",..: 4 9 10 1 4 9 1 9 4 10 ...
 $ drv         : Factor w/ 3 levels "4","f","r": 2 2 2 2 2 2 2 1 1 1 ...
 $ cty         : int  18 21 20 21 16 18 18 18 16 20 ...
 $ hwy         : int  29 29 31 30 26 26 27 26 25 28 ...
 $ fl          : Factor w/ 5 levels "c","d","e","p",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ class       : Factor w/ 7 levels "2seater","compact",..: 2 2 2 2 2 2 2 2 2 2 ...

qplot(displ, hwy, data = mpg)

qplot(displ, hwy, data = mpg, color = drv)

qplot(displ, hwy, data = mpg, geom = c("point", "smooth"))

qplot(hwy, data = mpg, fill = drv)

qplot(displ, hwy, data = mpg, facets = . ~ drv)
qplot(hwy, data = mpg, facets = drv ~ ., binwidth = 2)

• Mouse Allergen and Asthma Cohort Study
• Baltimore children (aged 5—17)
• Persistent asthma, exacerbation in past year
• Study indoor environment and its relationship with asthma morbidity
• Recent publication: http://goo.gl/WqE9j8

str(maacs)

'data.frame':   750 obs. of  5 variables:
 $ id       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ eno      : num  141 124 126 164 99 68 41 50 12 30 ...
 $ duBedMusM: num  2423 2793 3055 775 1634 ...
 $ pm25     : num  15.6 34.4 39 33.2 27.1 ...
 $ mopos    : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...

qplot(log(eno), data = maacs)

qplot(log(eno), data = maacs, fill = mopos)

qplot(log(eno), data = maacs, geom = "density")
qplot(log(eno), data = maacs, geom = "density", color = mopos)

qplot(log(pm25), log(eno), data = maacs)
qplot(log(pm25), log(eno), data = maacs, shape = mopos)
qplot(log(pm25), log(eno), data = maacs, color = mopos)

qplot(log(pm25), log(eno), data = maacs, color = mopos, 
      geom = c("point", "smooth"), method = "lm")

qplot(log(pm25), log(eno), data = maacs, geom = c("point", "smooth"), 
      method = "lm", facets = . ~ mopos)

• The qplot() function is the analog to plot() but with many built-in features
• Syntax somewhere in between base/lattice
• Produces very nice graphics, essentially publication ready (if you like the design)
• Difficult to go against the grain/customize (don’t bother; use full ggplot2 power in that case)

• The ggplot2 book by Hadley Wickham
• The R Graphics Cookbook by Winston Chang (examples in base plots and in ggplot2)
• ggplot2 web site (http://ggplot2.org)
• ggplot2 mailing list (http://goo.gl/OdW3uB), primarily for developers