Book Image

Hands-On SAS for Data Analysis

By : Harish Gulati
Book Image

Hands-On SAS for Data Analysis

By: Harish Gulati

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.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: SAS Basics
4
Section 2: Merging, Optimizing, and Descriptive Statistics
7
Section 3: Advanced Programming
10
Section 4: SQL in SAS
13
Section 5: Data Visualization and Reporting

Merging

While an appreciation of different ways of combining datasets is necessary, the most important methodology in SAS is merging datasets. It is time to look at one-to-many and many-to-many merges. Along with these two types of merges, we will also look at the concept of BY MATCHING.

By Matching

For performing By Matching, we have the following information about the cost of living in two different datasets, A and B, at hand:

We want to join the two datasets together so that we have one wide dataset with 10 rows of observations for City and nine variables.

Let's use the same form of Merge that we used earlier when generating the output for Merging:

Data Cost_Living;
Merge A B;
Run;

We get the desired output in the following...