A common criticism of R, especially from data scientists who
are new to R but proficient in multiple programming languages, is that R is
“quirky” and annoying because there is almost always more than one way to do
simple things. I usually counter that
they are trying to say that R is “flexible” and “rich”, but by the time we get
around to talking about plots I have to concede that my data scientist friends
have a point. There are, after all, three completely different major plotting
systems in R: the base graphics system,
lattice plots and the ggplot2. Each of these systems comprises its own little
universe of functions, form, feel and things you just have to remember (or look
up every time). They all do the basic plots really well. So, when you come to a new plot that might be
a little way out of the ordinary for you what is the best way to go? Now, I
know. In the preface to his recent source book for producing plots in R, the R
Graphics Cookbook Winston Chang writes. “Each recipe in this book lists a
problem and a solution. In most cases, the solutions I offer aren’t the only
way to do things in R, but they are, in my opinion, the best way.” – no mincing
words here. I only got to page one and
I knew I was going to like this book!
All right, so Winston Chang
is an accomplished ggplot2 developer , and who am I to argue whether the
solutions he picked are the “best” solutions, but how good is the book itself?
Let’s begin with the appendix. (When reading a technical book, I almost always
jump from the beginning to somewhere near the end to get an idea of how much
work I am signing up for.) Appendix A is a succinct, lucid and well-written
introduction to ggplot2. In my opinion, it provides sufficient information,
presented with exemplary clarity, to enable someone unfamiliar with ggplot2 to
work through the rest of the book. Since
a good many of Winston’s examples are based on ggplot2 I recommend that anyone
who is new to this package read appendix first.
The R Graphics Cookbook is mostly written in what might be
called “web page style”: short sentences, bullet points, code snippets and
plots integrated in a manner that, I think, facilitates comprehension. The book
is organized into 15 chapters, eleven of which are either concerned with a
particular kind of plot (e.g. Chapter5:
Scatter Plots) or some potentially vexing task that is often required to
create a satisfying plot (e.g. Chapter 7: Annotations). The remaining chapters
are devoted to supporting material such as Chapter 1: “R Basics” which is a
minimalist account on getting information into R and Chapter 15 “Getting Your
Data into Shape” which is a short, but fairly comprehensive account of
transforming data into the “long form” used by ggplot(). Each chapter is organized as a series of
“problems” with sections entitled “Problem”, “Solution” and “Discussion”. The
Solution section contains the recommended code while the Discussion section
provides some elaboration. In simple cases, the discussion may be just a few
bullet points. At other times, the Discussion anticipates difficulties the
reader may have. For example, the Discussion section “Making a Basic Bar Graph”
introduces factors.
The following plot
Download Stacked Plot from the discussion section of making a
stacked graph illustrates is typical of the many simple examples presented in
the book. It is a variation on the basic theme that shows the sophisticated
look and feel that is the hallmark of ggplot2.
This next plot
Download Vector field which shows a vector field superimposed on a
map is one of the more elaborate examples presented in the book. It gives an
idea of the data manipulation that is often required to make a useful plot.
The R Graphics Cookbook contains a few surprises too. The Note
on the last page of the appendix reads:
“Some introductions to ggplot2 make
use of a function called qplot(), which is intended as a convenient interface
for making graphs. It does require a little less typing than using ggplot()
plus a geom, but I’ve found it a bit confusing to use because it has a slightly
different way of specifying certain graphing parameters. I think it’s simpler and
easier to just use ggplot().”
I was pleasantly surprised to read this ( I have always been
confused by qplot() ), but then I was surprised again to see that the second
plot in the book, and several others, are done with qplot()! Clearly, there is
more going on with these plots than meets the eye. Then, bang!, another
surprise : In the Discussion section of “Making A Scatter Plot Matrix” Winston writes:
“We didn’t use ggplot2 here because it doesn’t make scatter plot matrices (at
least, not well). “ Damn! For two years now, I have been trying to make ggplot2
produce scatter plot matrices that color points by group . (I bet I am not the
only one.) The problem is you start out doing a small project and decide to
make everything look really nice with ggplot2 then, the thought pops in your
head to do a plot matrix. Both the base R graphics system plot(iris[,1:4],col=iris$Species) and lattice
splom(iris[,1:4],col=iris$Species)do
this easily, but there is plotmatrix() lurking in in the depths of ggplot2 and
mocking my aesthetic efforts. Personal
problems aside, the point of all this is that the R Graphics Cookbook is not
just a manual for ggplot2(). It is something much more valuable, a guide to
making good plots in R written by a knowledgeable developer with strong
feelings, a sense of style, and a gift for exposition. The book obviously doesn’t
cover everything, and it would have been nice to see some lattice examples. The R Graphics Cookbook
is, however, a very nice example of the kind of systematic, task orientated documentation
that we need more of for R. I highly recommend it,
especially for people who are new to R. I might even buy a couple of copies for
my multi-language, “need the one best way to do things” friends.
