Interactive and dynamic graphics for data analysis. With R and GGobi. With contributions by Andreas Buja, Duncan Temple Lang, Heike Hofmann, Hadley Wickham, and Michael Lawrence.

*(English)*Zbl 1154.62006
Use R!. New York, NY: Springer (ISBN 978-0-387-71761-6/pbk). xvii, 188 p. (2007).

The book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. The chapters include clustering, supervised classification, and working with missing values. In its presented analysis, a variety of plots and interaction methods are used. At each step of the analysis, the role of graphical methods is shown not only in the early explanatory phase, but also in the later stages, when comparing and evaluating models. All the examples of the book are based on the freely available software GGobi, for interactive graphics, and on R for static graphics, modelling and programming. The book is augmented by a wealth of material on the web, encouraging the readers to follow the examples themselves. On the web site one can find all the data and the code necessary to reproduce the analyses in the book, along with movies demonstrating the examples.

The book may be used as a text in a class on statistical graphics, exploratory data analysis, visual data mining, or information visualisation. It might also be used as an adjunct text in a course on multivariate data analysis or data mining. Moreover, the book is suitable for an industrial statistician, engineer, bioinformaticist, or computer scientist with some knowledge of basic data analysis and a need to analyse high-dimensional data. Finally, it may be useful to a mathematician who wants to visualise high-dimensional structures.

The book may be used as a text in a class on statistical graphics, exploratory data analysis, visual data mining, or information visualisation. It might also be used as an adjunct text in a course on multivariate data analysis or data mining. Moreover, the book is suitable for an industrial statistician, engineer, bioinformaticist, or computer scientist with some knowledge of basic data analysis and a need to analyse high-dimensional data. Finally, it may be useful to a mathematician who wants to visualise high-dimensional structures.

Reviewer: Christina Diakaki (Chania)

##### MSC:

62-09 | Graphical methods in statistics (MSC2010) |

62-07 | Data analysis (statistics) (MSC2010) |

62-01 | Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics |

62H30 | Classification and discrimination; cluster analysis (statistical aspects) |