Kleinman and Horton have previously published two books, Using SAS Data Management, Statistical Analysis, and Graphics and Using R Data Management, Statistical Analysis, and Graphics, with both books containing a complete description of the statistical methods that can be applied in both SAS and R that are most often used by statistical analysts, researchers and data analysts. This third book provides users with knowledge of SAS and R and also for users with SAS knowledge a familiarity of R programming and vice versa. It is an excellent text that is designed to translate SAS to R. The authors explain that SAS and R are fundamentally distinct and that an enumeration of their differences would be counter-productive. New users need to bear in mind of some of these differences.
For statisticians with knowledge of both SAS and R programming this book provides a useful resource to understand the differences between SAS and R codes and can be used for browsing and for finding particular SAS and R functions to perform common tasks. The book will strengthen the analytical abilities of relatively new users of either system by providing them with a concise reference manual and annotated examples executed in both packages. Professional analysts as well as statisticians, epidemiologists and others who are engaged in research or data analysis will find this book very useful. The book is comprehensive and covers an extensive list of statistical techniques from data management to graphics procedures, cross-referencing, indexing and good worked examples in SAS and R at the end of each chapter.