Garrett Grolemund

Hadley Wickham

English

Research Papers in Economics

JSS Journal of Statistical Software
September 2010, Volume 36, Book Review 3. http://www.jstatsoft.org/
Reviewer: Hadley Wickham and Garrett Grolemund
Rice University
Graphics for Statistics and Data Analysis with R
Kevin J. Keen
Chapman and Hall/CRC, Boca Raton, FL, 2010.
ISBN 9781584880875. 489 pp. USD 69.95 (H).
http://www.crcpress.com/product/isbn/9781584880875
A proliferation of misused graphics has followed the proliferation of cheap statistical and
graphing software. To quell this epidemic, we must develop our graphicacy skills, something
that is rarely taught in school. Keen oers this book, \Graphics for Statistics and Data
Analysis with R", as a lesson in the subject.
A good graph can communicate information more directly and immediately than numbers or
words, but it is not easy to make a good graph. Keen begins in Chapter 1 with an outline
of the principles behind good graphics. A good graph should be tailored to its audience and
the audience must have adequate time to understand it. A good graph should also have
a thoughtfully chosen scale and thoughtfully chosen design elements (which Keen calls the
\graphical frame"). The guiding principle behind these choices should be to avoid ambiguity
through clear labelling.
Keen subscribes to a mental model where people must access a \graphical icon" in their long
term memory before they can understand a graph. When a viewer encounters a novel graph
type, it takes him longer to interpret it because he must rst build a new graphical icon. By
this reasoning, Keen recommends that analysts stick to commonly used graphs. This becomes
the organizing principle for the remainder of the text:
￿ single discrete: basic (Chapter 2), and advanced (Chapter 3); dot, bar, pie, stacked bar,
pictogram.
￿ single continuous: exploration (Chapter 4), diagnostics (Chapter 5), nonparametric
density estimation (Chapter 6), parametric density estimation (Chapter 7); dotplot,
stemplot, boxplot, ECDF, QQ plot, probability plot, histogram, density plots, normal
density estimation, Person's curves, etc.
￿ two variables: both discrete (Chapter 8), one discrete and one continuous (Chapter 9)
and both continuous (Chapter 10); grouped and side-by-side dot (with and without
whiskers), bar, box, and pie chart, mosaic plot, sunower plot, bagplot, 2D histogram,
etc.2 Graphics for Statistics and Data Analysis with R
￿ models: simple linear regression (Chapter 11) and polynomial regression (Chapter 12);
residual plots, inuence plots, etc.
￿ multivariate data (Chapter 13); glyphs, 3D charts, scatterplot matrices, etc.
The book is methodical and complete, but focuses too much on plotting one or two variables
from a small data set. Too much space is taken up by minor variations of a plot (especially
the rst eight color gures, pp. 71{74 and 88{89), and too little (only 44 pages out of 435) on
multivariate data. This is particularly problematic given that almost all real data have more
than two variables!
This is a result of Keen's model of graphical reading: we must explicitly list every named
graphic, rather than learning how they are generated from underlying principles. There are
two powerful and general techniques that receive short shrift because of this: there is no
discussion of color and only a tiny discussion of conditioned/trellised plots. (The author also
incorrectly describes a casement plot as a scatterplot matrix, not, correctly, as a trellised
plot.)
For a book on graphics, the details of the graphic display could be better. The graphics
all look a little large (especially for the amount of data they display), and the text on the
graphics is substantially larger than the text on the page. Many graphics lack frames, and
have poor gure-ground contrast, making it hard to distinguish guides from the data.
Reading this book will give you the ability to recognize and create the majority of the named
graphics of statistics, but named graphics are only a small portion of interesting and useful
graphics. I would recommend this book if you were interested in a detailed survey of 1D and
2D graphics (a better title would be \1D and 2D graphics in R"), but it will not provide you
with the skills to explore and understand large, modern datasets, nor to create new types of
graphic tailored for a specic purpose.
Reviewer:
Hadley Wickham
Rice University
Department of Statistics
Houston, TX, United States of America
E-mail: hadley@rice.edu
URL: http://had.co.nz/
Journal of Statistical Software http://www.jstatsoft.org/
published by the American Statistical Association http://www.amstat.org/
Volume 36, Book Review 3 Published: 2010-09-01
September 2010

Graphics for Statistics and Data Analysis with R

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Research Papers in Economics