**Reviews**

Statistical Papers March 2017 (Jasmin Wachter)

This clear structure allows for fast orientation and makes the book exceptionally friendly for users.

International Statistical Review August 2016 (Reijo Sund)

The examples concretely show how the simple tools really do the magic, if those are applied in a suitable manner.

JASA August 2016 (Dianne Cook, Jill Wright, and Julia Polak)

This book is a great reference book for a researcher or a consultant to get inspiration about different ways of exploring the features in the analyzed data.

JRSS A June 2016 (Andrey Kostenko)

The book under review does an excellent job of discussing and showing how typical graphical data analysis tasks can be done with R. It will also be a valuable asset for a library and as part of an undergraduate course in applied statistics.

Revolutions blog 8 April 2016 (Joseph Rickert)

I think that many readers will find that Graphical Data Analysis with R illuminates a shadowy corner of statistical analysis and stands a good chance of becoming recognized as a foundational text for GDA.

Statistical Modeling blog 27 February 2016 (Andrew Gelman)

Compared to most books on R, which tend to sprawl in all directions, Unwin’s book is focused, students have a clearly defined set of skills to learn, and these skills are framed statistically.

Significance February 2016 (Jim Albert)

Unwin’s book is an attractive addition to the current statistical graphics texts as it demonstrates what can be learned through graphs.

Biometrical Journal December 2015 (Andreas Krause)

In summary, the strength of this book lies in the profound introduction to the topic of graphical data analysis. The comprehensive sectional introductions and overviews along with the “how-to” might well be regarded as the modern update to Tukey’s landmark book (Tukey, 1977).

Journal Of Educational And Behavioral Statistics December 2015 (Howard Wainer, Michael Friendly, and Pere Millán-Martínez) It is well written, clearly by a practitioner with wide experience, gives generally good (though sometimes opinionated) advice, and includes R code for nearly all examples, as well as nice collections of additional exercises for each chapter.

Beyond the content, Unwin also does an admirable job of conveying enthusiasm for data graphics

Journal of Statistical Software November 2015 (David Zeitler)

This text has the potential of bringing sophisticated visualization to a broad audience

Datendesign mit R October 2015 (Thomas Rahlf)

Für Statistiker und Experten der Datenanalyse ist das Buch ohne Zweifel das neue Referenzwerk zum Thema.

(For statisticians and experts in data analysis, the book is without doubt the new reference work on the subject.)

**Aim**

The main aim of the book is to show, using real datasets, what information graphical displays can reveal in data. Seeing graphics in action is the best way to learn Graphical Data Analysis. Gaining experience in interpreting graphics and drawing your own data displays is the most effective way forward.

**Target readership**

includes anyone carrying out data analyses who wants to understand their data using graphics. The book can be used as the primary textbook for a course in Graphical Data Analysis or as an accompanying text for a statistics course. Prerequisites for the book are an interest in data analysis and some basic knowledge of R.

**Using the book**

Like anything else, using graphics effectively is mostly a matter of practice. Study and criticise the examples. Test out the code yourself—it is all available on the book’s webpage, rosuda.org/GDA. You can experiment with it while you are reading the book, just copy and paste the code into R. Vary the size and aspect ratio of your graphics, vary the scaling and formatting, vary the colours used. Draw lots of graphics, see what you get, and decide what is most effective for you in making it easy to recognise information.

Publisher´s webpage (also for ordering)

How to order otherwise (e.g., amazon.com)