R for SAS and SPSS Users Paperback - 2016

From the rear cover

R is a powerful and free software system for data analysis and graphics, with over 4,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 50 programs written in all three packages, comparing and contrasting the packages' differing approaches.

The glossary defines R terms using SAS/SPSS terminology and again using R terminology. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. The second edition adds 216 pages of new topics.

"I found the book extremely helpful...The material is laid out in a way that makes it very accessible. Because of this I recommend this book to any R user regardless of his or her familiarity with SAS or SPSS...For new R users it will demystify many aspects, and for existing R users it will have many answers to those questions you have been too afraid to ask in public."

--The American Statistician

"... an excellent introduction to R...the book meticulously covers data management, data structures, programming, graphics and basic statistical analysis in R. The prose is clear, the examples tied to their SPSS and SAS analogs. The handling of both traditional and newer "ggplot2" graphics is comprehensive: SPSS and SAS users will undoubtedly find lots to like. "

--Information Management

"As a long time SAS user this book makes the task of transition to R much more palatable and appealing. It also greatly reduces the time to get up andrunning in R effectively."

--Technometrics

"It is great to see this book in a second edition. It serves nicely as an introduction to R, irrespective of whether they are familiar with SAS or SPSS. I have long been a fan of programming by example and the book is full of excellent ones."

--Graham Williams, Author, Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery