Gabor Grothendieck

English

Research Papers in Economics

JSS Journal of Statistical Software
May 2008, Volume 25, Book Review 2. http://www.jstatsoft.org/
Reviewer: Gabor Grothendieck
GKX Associates Inc.
lattice: Multivariate Data Visualization with R
Deepayan Sarkar
Springer-Verlag, New York, NY, 2008.
ISBN 978-0-387-75968-5. 268 pp. USD 54.95 (P).
http://dsarkar.fhcrc.org/lattice/
The R lattice package is a powerful and complex system implementing William Cleveland's
trellis graphics (Cleveland 1985) { a system focusing on the display of multivariate data in
panels. Trellis was one of the many ideas that came from a particularly notable generation
in the history of statistical developments at Bell Labs { a period that also included the S
programming language (Becker, Chambers, and Wilks 1988). The attraction to lattice is
derived from its inclusion in the R download, i.e., it need not be separately downloaded from
the Comprehensive R Archive Network (CRAN), its Bell Labs heritage, its wide variety of
displayable graphics (xyplot, bwplot, densityplot, levelplot, etc.), its portability as it
is written entirely in R, its maturity through years of improvement and its customizability.
The last item is owing to a variety of graphical parameters, exible use of panel functions,
extensibility through the object model and its leverage of the underlying R grid graphics
system (R Development Core Team 2008) upon which it is built. The package's author and
author of the book under review is a member of the R development core team.
The nature of lattice may be gleaned from the following R code which creates a multipanel
graphic using lattice. Here, yield and nitro are numeric while Variety and Block are
factors. The formula species that for each combination of those factors a scatterplot of
yield vs. nitro is to be displayed in a separate panel. Note that type is similar to the
argument of the same name in R's classic graphics and the\key", i.e., legend, can be specied
within the command itself rather than via a separate legend command, as would be the
case in most classic graphics. Also, we see that the result of the xyplot function is not a
graphic but an object, here assigned to p, that can be further manipulated. The most common
operation is simply to plot the object which is done via the print command below.
R> library("lattice")
R> data(Oats, package = "MEMSS")
R> p <- xyplot(yield ~ nitro | Variety + Block, Oats,
+ type = "o", auto.key = TRUE)
R> print(p)2 lattice: Multivariate Data Visualization with R
Because of its substantial power and history the package has drawn many users yet the
relatively terse documentation has meant that getting up to speed usually involved scavenging
sample code from the internet. One always had the feeling that the author was the sole expert
in its use. This volume attempts to address that with a book length treatment of the package.
The book attempts to address both new users through Part I which is roughly the rst 100
pages and more experienced users through Part II which is approximately the next 100 pages.
The latter covers individual topics with chapters on graphical parameters, axes, etc. Finally,
Part III, discusses extending lattice via panel functions and object orientation.
With 150 sample graphics throughout the book, including both black and white output as well
as color plates, it has sucient sample code to satisfy most readers yet it is not a cookbook
or reference and seems to aim at conveying lattice concepts making use of whatever examples
are needed in the process.
Part I consists of an introductory Chapter 1 which dives right into examples, followed by
Chapter 2 which discusses the main concepts behind lattice including conditioning, shingles,
aspect, layout, scales, annotation, panel functions and other concepts. The remaining chapters
of Part I focus on univariate, e.g., densityplot, bivariate and multivariate scatterplots, e.g.,
xyplot, trivariate, e.g., cloud and graphical representations of tables, e.g., dotplot. Part I
will be particularly useful for new users. For existing users it does have a number of tips
that may be new and also some useful comparative examples of the eectiveness of dierent
displays such as the various representations of the quake data in Chapter 5. Examples of using
related packages such as such as latticeExtra (Sarkar 2007) and hexbin (Carr, Lewin-Koh,
and Maechler 2008) will also be of interest.
Part II covers a number of specic topics such as graphical parameters (Chapter 7), axes
(Chapter 8), legends (Chapter 9) and others followed by chapters focusing on data (Chap-
ter 10), objects (Chapter 11) and interaction (Chapter 12). We note the slick single page
visual in Chapter 7 organizing the multitude of otherwise bewildering graphical parameters
and some unexpected applications of shingles in Chapter 10 as two sample items from this
part.
Part III on extending lattice discusses panel functions (Chapter 13) with eye catching corrgram
(Friendly 2002) and mapping examples while Chapter 14 gives examples of creating new lattice
functions. Note that there is a package corrgram (Wright 2006) on CRAN but it uses classic
graphics dierentiating the presentation here.
The exposition is clear and while a completely linear treatment of such a complex package is
probably not realistic the author was careful to make the forward references explicit when such
were needed. The text has few errors although we did manage to catch a typo on page 109
where Figure 6.15 is erroneously referred to as Figure 6.4.
Not all the packages mentioned in the book are cited in the bibliography which seems an
omission in light of the R citation function. Also the index can be dicult to use until one
discovers that individual \Arguments", \Datasets" and \Packages" are listed as second level
entries under those three topics, respectively. However, once understood that organization
does become convenient.
As many of the chapters are independent of each other, readers may wish to skim the book
and then focus on particular sections of interest while creating their own graphics. Some
additional reference to the online help may still be necessary but having read the book the
user will have a better understanding of the concepts and a wealth of examples to rely on.Journal of Statistical Software { Book Reviews 3
The book is destined to be the standard reference on lattice and is highly recommended to
those with an interest in using lattice graphics in R.
References
Becker RA, Chambers JM, Wilks AR (1988). The New S Language. Wadsworth.
Carr D, Lewin-Koh N, Maechler M (2008). hexbin: Hexagonal Binning Routines. R package
version 1.14.0, URL http://www.bioconductor.org/.
Cleveland W (1985). The Elements of Graphing Data. Wadsworth.
Friendly M (2002). \Corrgrams: Exploratory Displays for Correlation Matrices." The Amer-
ican Statistician, 56, 316{324.
R Development Core Team (2008). R: A Language and Environment for Statistical Computing.
R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http:
//www.R-project.org/.
Sarkar D (2007). latticeExtra: Extra Graphical Utilities Based on lattice. R package ver-
sion 0.3-1, URL http://CRAN.R-project.org/package=latticeExtra.
Wright K (2006). corrgram: Plot a Correlogram. R package version 0.1, URL http://CRAN.
R-project.org/package=corrgram.
Reviewer:
Gabor Grothendieck
GKX Associates Inc.
E-mail: ggrothendieck@gmail.com
Journal of Statistical Software http://www.jstatsoft.org/
published by the American Statistical Association http://www.amstat.org/
Volume 25, Book Review 2 Published: 2008-05-08
May 2008

Core Team

corrgram: Plot a Correlogram. R package version 0.1, URL http://CRAN.

Corrgrams: Exploratory Displays for Correlation Matrices.&quot;

hexbin: Hexagonal Binning Routines. R package version 1.14.0,

latticeExtra: Extra Graphical Utilities Based on lattice. R package version 0.3-1,

The Elements of Graphing Data.

lattice: Multivariate Data Visualization with R

lattice: Multivariate Data Visualization with R

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Research Papers in Economics