This course is for experienced R users who want to apply their existing skills and extend them to the SAS environment. Emphasis is placed on programming and not statistical theory or interpretation. Students in this course should have knowledge of plotting, manipulating data, iterative processing, creating functions, applying functions, linear models, generalized linear models, mixed models, stepwise model selection, matrix algebra, and statistical simulations.

Learn how to

• read and write SAS programs
• import various forms of data
• subset and merge data tables
• do iterative processing and simulate new data
• create new variables and functions
• create and enhance plots of all types
• apply descriptive and inferential procedures including regression, logistic regression, analysis of variance, stepwise model selection, and mixed models
• conduct matrix algebra and statistical simulations in the interactive matrix language (IML)
• call R from SAS to use as a complementary resource.

Who should attend

Experienced R users who want to augment their programming skills with SAS

Formats available	Standard duration
e-Learning:	14 hours/180 day license	System Requirements

Students should have knowledge of plotting, manipulating data, iterative processing, and creating and applying functions. They should also have knowledge of linear models, generalized linear models, mixed models, stepwise model selection, matrix algebra, and statistical simulations.

This course addresses Base SAS, SAS/IML, SAS/STAT software.

Introduction

• introduction
• SAS programming (self-study)
• accessing data in SAS libraries

Importing and Reporting Data

• the DATA step and manual data entry
• importing data
• reporting the data
• enhanced reporting

Creating New Variables, Functions, and Data Tables

• creating new variables
• creating and using functions
• subsetting and concatenating data tables

Random Number Generation and Plotting

• DO loop and random number generation
• single-cell plotting
• multi-cell plotting

Descriptive Procedures, Output Delivery System, and Macros

• CORR, FREQ, MEANS, and UNIVARIATE procedures
• Output Delivery System (ODS)
• creating macro variables
• creating macro programs

Analyzing the Data via Inferential Procedures

• linear models
• generalized linear models
• mixed models
• other procedures

Interactive Matrix Language (IML)

• the basics (self-study)
• modules and subroutines
• calling SAS data sets and procedures
• simulations

A Bridge between SAS and R

• calling R from IML
• calling R from Base SAS Java API (self-study)
• calling R from SAS Enterprise Miner (self-study)