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

✍ Scribed by Ken Kleinman, Nicholas J. Horton

• Publisher: Chapman and Hall/CRC
• Year: 2014
• Tongue: English
• Leaves: 425
• Edition: 2
• Category: Library

✦ Synopsis

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 analyzes demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book's website.

✦ Table of Contents

Data Input and Output
Input
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

Simulation
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
Installation
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
Installation
Running R and Sample Session
Learning R and Getting Help
Fundamental Structures and Objects
Functions
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.