Computational skills are kept to a minimum in the book by including R script programs. Students are not required to master the writing of R script programs, but explanations of how the programs work and program output are included in each chapter. R is a statistical package with an extensive library of functions that offers flexibility in writing customized statistical routines. The R script commands are run in the R Studio software which is a graphical user interface for Windows. The R Studio software makes accessing R programs, viewing output from the exercises, and graph displays easier for the student. This book is intended as a guide to data analysis with the R system for statistical computing. R is an environment incorporating an implementation of the S programming language, which is powerful, flexible and has excellent graphical facilities. In the book we aim to give relatively brief and straightforward descriptions of how to conduct a range of statistical analyses using R. Each chapter deals with the analysis appropriate for one or several data sets. A brief account of the relevant statistical background is included in each chapter along with appropriate references, but our prime focus is on how to use R and how to interpret results. We hope the book will provide students and researchers in many disciplines with a self-contained means of using R to analyse their data. R is an open-source project developed by dozens of volunteers for more than ten years now and is available from the Internet under the General Public Licence. R has become the lingua franca of statistical computing. Increasingly, implementations of new statistical methodology first appear as R add-on packages. In some communities, such as in bioinformatics, R already is the primary workhorse for statistical analyses. Because the sources of the R system are open and available to everyone without restrictions and because of its powerful language and graphical capabilities, R has started to become the main computing engine for reproducible statistical research. For a reproducible piece of research, the original observations, all data preprocessing steps, the statistical analysis as well as the scientific report form a unity and all need to be available for inspection, reproduction and modification by the readers. Reproducibility is a natural requirement for textbooks such as the book and therefore this book is fully reproducible using an R version greater or equal to 2.4.0. All analyses and results, including figures and tables, can be reproduced by the reader without having to retype a single line of R code. The goal of this book is to introduce basic statistical principles, techniques, and concepts in a relatively simple and concise manner. The book is designed for a one-semester course aimed at non statisticians.
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