Biometrical Journal 54 (2012) 3, 439–440 DOI: 10.1002/bimj.201210001 439

Book review

Medizinische Statistik mit R und Excel: Einfuhrung in die RExcel- und R-Commander-Oberflachen zurstatistischen Auswertung. R. Muche, S. Lanzinger and M. Rau (2011). Berlin, Heidelberg: SpringerVerlag. ISSN/ISBN: 0937-7433/978-3-642-19483-2

Microsoft Excel is the standard software program that is most frequently chosen for data storageby experimentalists working in different fields of research such as medicine, biology, or psychology.It also is often used for the generation of graphics, calculation of descriptive statistical measures, oreven performance of, for example, t-tests. However, usability of Excel is limited to such types of basicstatistical methods. More sophisticated statistical procedures such as nonparametric statistical tests,analysis of variance, linear and nonlinear regression modeling, or survival analysis are not implementeddirectly in Excel. On the other hand, learning of a complete program-based statistical software, forexample, R or SAS, is usually too complicated and thus not very attractive for researchers who onlyrarely have to conduct small statistical evaluations, for example, medicine students as part of theirPh.D. theses. To facilitate the performance of sound statistical analyses for this specific user group, in2009, the open-source Excel add-in “Rexcel” was developed. It allows to incorporate R and its menu-driven user interface, the “R commander” into Excel to run statistical procedures correctly, eitherdirectly in Excel or by employment of the user-friendly R commander. Running the R commander viathe Excel interface is described in Heidberger and Neuwirth (2009).

The German textbook under review introduces nicely how the German interface of the R commandercan be utilized to carry out basic statistical methods, including descriptive and graphical procedures,correlation and simple linear regression analysis, simple parametric and nonparametric statisticaltests, confidence intervals, one-way analysis of variance, survival analysis, and sample size calculation(limited to t-test and χ2-test). The performance of these statistical techniques in the R commander isdemonstrated by application to a data set gathered from cardiovascular research.

The book starts with a very brief introduction of the RExcel interface (chapters 1 and 2). Datamanagement in Excel and in the R commander, including manual data entry and data transfer fromExcel to R, and vice versa, is discussed in chapters 3 and 4. Chapter 5 describes the basic principlesof the statistical methodologies that are presented in the subsequent chapters (6–12) very briefly andonly at an introductory level, but recommendations regarding continuative literature are given in theappendix. Chapters 6–12 are organized by data type (paired vs. unpaired data, discrete vs. continuousdata). In each of these chapters, several basic statistical tools are applied to the exemplary dataset. Analysis steps that have to be carried out using the R commander interface and results gainedfrom that statistical evaluation are explained in detail and illustrated by many nice and extremelyhelpful screenshots. Conclusions drawn from these analyses are stated in a way understandable alsofor nonstatisticians, thereby accepting some lack of statistical soundness. Some of the tools coveredby this book (e.g., McNemar test, kappa coefficient) are not yet implemented in the R commanderinterface. In these situations, it is illustrated how R code can be used directly to conduct the analysis.The appendix of the book includes an instruction for easy installation of the RExcel add-in, a shortdescription of the exemplary data set, a complete list of all features that are implemented in the Rcommander interface, instructions for loading specific R packages or R plug-ins, a short overview ofthe R syntax (in case a reader who is not familiar with the R code wants to perform an evaluation thatis not supported by the R commander), some recommendations for improving the layout of graphics,and a detailed reference list containing continuative textbooks about R, Excel, and statistics.

C© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

440 Book Review:

The primary audience of this textbook are medicine students having a course in medical biometry,but it will be useful as an accompanying nice textbook for any course in applied statistics, both forstudents and lecturers. The didactic features of the book show that the authors are very experienced inteaching students in applied statistics, especially in medical biometry. Throughout the book, practicalstatistical aspects are always in the front while both the description of the statistical methods and theinterpretation of results lack some statistical soundness. The book is self-contained, easy to read anda well-prepared collection of practical examples illustrating the application of relevant basic statisticalmethods to a medical data set, and thus suitable for private study. Interested readers can easily modifythe analysis, apply it to own data sets and inspect the achieved results. Reading of this book in frontof your computer is therefore, in my opinion, much more effective (and more fun) than reading itin your armchair. I especially enjoyed and appreciate that at several places in the book, the authorsgive practical tips and recommendations gained from their own subjective experience with the RExcelinterface. I warmly recommend this book.

Reference

Heidberger, R. M. and Neuwirth, E. (2009). R Through Excel. Springer Verlag, New York.

Sven Stanzel∗,

Department of BiostatisticsGerman Cancer Research CenterIm Neuenheimer Feld 280D-69009 HeidelbergGermany

∗e-mail: s.stanzel@dkfz.de, Phone: +49-6621-42-2135, Fax: +49-6621-42-2397

C© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.biometrical-journal.com