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Hands-On SAS for Data Analysis

By : Harish Gulati

Buy this Book

Hands-On SAS for Data Analysis

By: Harish Gulati

Buy this Book

Overview of this book

SAS is one of the leading enterprise tools in the world today when it comes to data management and analysis. It enables the fast and easy processing of data and helps you gain valuable business insights for effective decision-making. This book will serve as a comprehensive guide that will prepare you for the SAS certification exam. After a quick overview of the SAS architecture and components, the book will take you through the different approaches to importing and reading data from different sources using SAS. You will then cover SAS Base and 4GL, understanding data management and analysis, along with exploring SAS functions for data manipulation and transformation. Next, you'll discover SQL procedures and get up to speed on creating and validating queries. In the concluding chapters, you'll learn all about data visualization, right from creating bar charts and sample geographic maps through to assigning patterns and formats. In addition to this, the book will focus on macro programming and its advanced aspects. By the end of this book, you will be well versed in SAS programming and have the skills you need to easily handle and manage your data-related problems in SAS.

Preface

Who this book is for

What this book covers

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Free Chapter

Section 1: SAS Basics

Introduction to SAS Programming

SAS dataset fundamentals

SAS programming language – basic syntax

SAS LOG

Dataset options

SAS operators

Formats

Subsetting datasets

Dictionary tables

Summary

Data Manipulation and Transformation

Length of a variable

Case conversion and alignment

String identification

Dealing with blanks

Missing and multiple values

Interval calculations

Concatenation

Logic and control

Number manipulation

Summary

Section 2: Merging, Optimizing, and Descriptive Statistics

Combining, Indexing, Encryption, and Compression Techniques Simplified

Introduction to combining

Concatenation

Interleaving

Merging

Indexing

Encryption

Summary

Power of Statistics, Reporting, Transforming Procedures, and Functions

Proc Freq

Proc Univariate

Proc Means and Summary

Proc Corr

Proc REG

Proc Transpose

Summary

Section 3: Advanced Programming

Advanced Programming Techniques - SAS Macros

What are macros?

Macro variable processing

Macro resolution tracking

Macro definition processing

Comparing positional and keywords parameters

Data-driven programming

Leveraging automatic global macro variables

Macros that evaluate

Writing efficient macros

Summary

Powerful Functions, Options, and Automatic Variables Simplified

NOMPREPLACE and MREPLACE

NOMCOMPILE and NCOMPILE

MCOMPILENOTE

NOMEXECNOTE and MEXECNOTE

MAUTOCOMPLOC

MACRO and NOMACRO

Exchanging values between the DATA step and macro variables

CALL EXECUTE

Altering the CALL SYMPUT example

Resolving macro variables

Macro quoting

Summary

Section 4: SQL in SAS

Advanced Programming Techniques Using PROC SQL

Comparing data steps and Proc SQL

Proc SQL joins

Proc SQL essentials

Dictionary tables

Summary

Deep Dive into PROC SQL

SAS views in Proc SQL

Making changes with Proc SQL

Identifying duplicates using Proc SQL

Creating an index in Proc SQL

Macros and Proc SQL

Summary

Section 5: Data Visualization and Reporting

Data Visualization

The role of data visualization in analytics

Histograms

Line plots

Vertical and horizontal bar charts

Scatter charts

Box plot

Summary

Reporting and Output Delivery System

Proc Tabulate

Specifying the ODS destination

Formatting ODS files

ODS Excel charts

Summary

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Proc Transpose

We have seen how to utilize some powerful procedures for statistical analysis. As a data user, the transformation of data from horizontal to vertical or regrouping between columns and rows is an important tactical step. This step could be necessary for forming the input to the modeling dataset or as an output to produce a report or showcase insights. You may want to transpose all the variables or just some of them. This is also an effective way to present variables in a grouped manner, without having to perform any mathematical aggregation.

We will use the variables from the following dataset to learn about transposing:

Data Base;
Input CustID Year Avg_Credit Avg_Debit Spend_Indicator $;
Datalines;
1010 16 235 245 R
1010 17 230 220 A
1010 18 235 200 G
1010 19 254 220 G
1011 16 653 650 A
1011 17 650 610 G
1011 18 640 620 G
1011 19 650 656 A
1012 16 569 569 R
1012 17 560 550...