SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition

Ken Kleinman, Nicholas J. Horton

July 17, 2014 by Chapman and Hall/CRC
Reference - 468 Pages - 48 B/W Illustrations
ISBN 9781466584495 - CAT# K19040

was $84.95

USD$67.96

SAVE ~$16.99

Add to Wish List
FREE Standard Shipping!

Features

  • Presents parallel examples in SAS and R to demonstrate how to use the software and derive identical answers regardless of software choice
  • Takes users through the process of statistical coding from beginning to end
  • Contains worked examples of basic and complex tasks, offering solutions to stumbling blocks often encountered by new users
  • Includes an index for each software, allowing users to easily locate procedures
  • Shows how RStudio can be used as a powerful, straightforward interface for R
  • Covers APIs, reproducible analysis, database management systems, MCMC methods, and finite mixture models
  • Incorporates extensive examples of simulations
  • Provides the SAS and R example code, datasets, and more online

Summary

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.

Share this Title