Today, computers play a major role in our daily activities as applied statisticians. With the improvement of computers, i.e. higher and more efficient computer power, applied statistics has blossomed. R is a statistical programming environment based on the S language. Besides being enormously popular in academic circles, it is very inviting to students as well, since it is free of cost. One can freely download the software and start using it.
There are many books published in applied statistics that explain the R language. However, the book under review stands out due to its versatility and because it is easy to follow and understand the context. To be clear — this is not a book for advanced users of R. Rather, it is an excellent book for novice, beginning or intermediate users of R.
From the table of contents it is clear that the authors cover a wide range of topics. Theoretical exposition is set to a minimum, just enough to be able to explain the input and output of the R code. Thus, this book focuses on R rather than theory. To fully take an advantage of the computational aspects of the book one should have a good knowledge of statistical methods at an upper undergraduate level.
The writing is very clear and easy to follow. The R code is presented in details along with graphical output. One should keep in mind that this book is like a bridge between beginning and advanced use of R in statistics. Naturally, there is much more to R than is presented in the book as well as much more to statistics. Hence, readers should take this book as a means to enable them to learn and be familiar with R and some statistics theory. Also, it will provide the reader with enough knowledge to go further to more advanced texts.
Overall, I would recommend this book to all statistics students (both undergraduate and graduate) and applied statistics researchers. It will be useful to students writing school projects in the area of statistical analysis. The book could serve as a main text to a computational statistics course. Exercises are provided at the end of each chapter and there are quite a number of them.
Preface; 1. A brief introduction to R; 2. Styles of data analysis; 3. Statistical models; 4. An introduction to formal inference; 5. Regression with a single predictor; 6. Multiple linear regression; 7. Exploiting the linear model framework; 8. Generalized linear models and survival analysis; 9. Time series models; 10. Multi-level models and repeated measures; 11. Tree-based classification and regression; 12. Multivariate data exploration and discrimination; 13. Regression on principal component or discriminant scores; 14. The R system - additional topics; Epilogue - models; References; Index of R symbols and functions; Index of terms; Index of names.