SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition, 2nd Edition (Hardback) book cover

SAS and R

Data Management, Statistical Analysis, and Graphics, Second Edition, 2nd Edition

By Ken Kleinman, Nicholas J. Horton

Chapman and Hall/CRC

468 pages | 48 B/W Illus.

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Hardback: 9781466584495
pub: 2014-07-17
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An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent Tasks

The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications.

New to the Second Edition

This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples.

Enables Easy Mobility between the Two Systems

Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.


"This book is not only an excellent cross-reference for SAS or R users to find the corresponding code in the opposing language, but also a useful resource for readers to learn statistical programming in both systems. The book is organized into 12 chapters covering a wide range of programming and statistical topics, with both SAS and R code presented for all tasks. … This book is a great resource for users who have a long experience in only one system and need to use the other system. The SAS index at the end of the book is particularly of help for SAS users to look up a task for which they know the SAS code and turn to a page with that SAS code as well as the associated R code. And the R index in the book is used the same way by R users to find the corresponding SAS code for a task."

—Xulei Liu, Vanderbilt University, in Biometrics, December 2017

"The second edition of SAS and R: Data Management, Statistical Analysis, and Graphics has several updates from the first, most notably the addition of three new sections, and the inclusion of R-Studio, which is a more user-friendly version of R. The first new section covers simulating data, the second covers several special topics, such as Bayesian methods and bootstrapping, and the third explores some case studies… This book is not intended to be read cover to cover, but rather as a dictionary of how to do things in both SAS and R. It covers a wide range of topics, including data management, numerical and graphical descriptive summaries, common statistical procedures, regression analyses, and regression generalizations… If you know either SAS or R, but not both, and are looking for a quick reference for common statistical tasks to be performed in the language that you are not familiar with, you will find this book helpful."

—Joshua Landon, The George Washington University, in The American Statistician, August 2016

Praise for the First Edition:

"By placing the R and SAS solutions together and by covering a vast array of tasks in one book, Kleinman and Horton have added surprising value and searchability to the information in their book. … a home run, and it is a book I am grateful to have sitting, dust-free, on my shelf."

—Robert Alan Greevy, Jr, Teaching of Statistics in the Health Sciences, Spring 2013

"Excellent cross-referencing to other topics and end-of-chapter worked examples on the ‘Health evaluation and linkage to primary care’ data set are given with each topic. … users who are proficient in either of the software packages but with the need to use the other will find this book useful."

—Frances Denny, Journal of the Royal Statistical Society, Series A, 2012

"This book provides a very useful bridge between the two packages … . A wide range of procedures are covered and the code, which is generally well explained, is available for download from their website. … this is a very useful book for SAS and R users alike with an excellent overview of a wide range of data management options, statistical analyses and graphics. … full of useful tips and tricks."

—Robin Turner, Statistics in Medicine, 2012

"It is clearly written and code is appropriately highlighted to facilitate readability. … it is a potentially useful reference material for experienced users of one of the two systems, who need to quickly find how to perform a familiar task in the alternative system."

Biometrics, 67, September 2011

"It is an excellent text that is designed to translate SAS to R. … 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."

Significance, July 2011

"As the authors point out in the Introduction, the book functions like an English–French dictionary. The material is organized by task. By looking up a particular task you wish to perform, R and SAS code are presented and briefly explained. … It is easy to find the section in the text which gives several ways to do this in both SAS and R. … Because the authors often present alternative ways to do a task, this book can be a great source of diverse and elegant solutions even to experienced users. Each task is cross-referenced to other tasks. … The book has a comprehensive website containing the code, datasets, a FAQ, blog, and errata list with a link to report new errors. … The end of the book is very useful, where there are good introductions to SAS and R, as well as separate subject, SAS, and R indices. These indices are invaluable for finding a topic when you are unsure of exactly how to phrase it. … there is great breadth and scope of the material in this book. … If you use both SAS and R on a regular basis, get this book. If you know one of the packages and are learning the other … get this book, too."

—Charles E. Heckler, Technometrics, May 2011

"… a convenient reference text to quickly learn by example how to perform common tasks in both software packages. … the book provides a powerful starting point to a wide variety of statistical techniques available in SAS and R. … it facilitates a translation between SAS and R, without getting overly detailed or technical. It is mainly useful as a starting point for those who already know either R or SAS, and want to learn the other language, without going over extensive manuals or introductory texts."

Journal of Statistical Software, January 2011, Volume 37

Table of Contents

Data Input and Output



Data Management

Structure and Meta-Data

Derived Variables and Data Manipulation

Merging, Combining, and Subsetting Datasets

Date and Time Variables

Statistical and Mathematical Functions

Probability Distributions and Random Number Generation

Mathematical Functions

Matrix Operations

Programming and Operating System Interface

Control Flow, Programming, and Data Generation

Functions and Macros

Interactions with the Operating System

Common Statistical Procedures

Summary Statistics

Bivariate Statistics

Contingency Tables

Tests for Continuous Variables

Analytic Power and Sample Size Calculations

Linear Regression and ANOVA

Model Fitting

Tests, Contrasts, and Linear Functions of Parameters

Model Diagnostics

Model Parameters and Results

Regression Generalizations and Modeling

Generalized Linear Models

Further Generalizations

Robust Methods

Models for Correlated Data

Survival Analysis

Multivariate Statistics and Discriminant Procedures

Complex Survey Design

Model Selection and Assessment

A Graphical Compendium

Univariate Plots

Univariate Plots by Grouping Variable

Bivariate Plots

Multivariate Plots

Special Purpose Plots

Graphical Options and Configuration

Adding Elements

Options and Parameters

Saving Graphs


Generating Data

Simulation Applications

Special Topics

Processing by Group

Simulation-Based Power Calculations

Reproducible Analysis and Output

Advanced Statistical Methods

Case Studies

Data Management and Related Tasks

Read Variable Format Files

Plotting Maps

Data Scraping and Visualization

Manipulating Bigger Datasets

Constrained Optimization: The Knapsack Problem

Appendix A: Introduction to SAS


Running SAS and a Sample Session

Learning SAS and Getting Help

Fundamental Elements of SAS Syntax

Work Process: The Cognitive Style of SAS

Useful SAS Background

Output Delivery System

SAS Macro Variables

Appendix B: Introduction to R and RStudio


Running R and Sample Session

Learning R and Getting Help

Fundamental Structures and Objects


Add-ons: Packages

Support and Bugs

Appendix C: The HELP Study Dataset

Background on the HELP Study

Roadmap to Analyses of the HELP Dataset

Detailed Description of the Dataset

Appendix D: References

Appendix E: Indices

Subject Index

SAS Index

R Index

Further Resources and Examples appear at the end of most chapters.

About the Originator

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General