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

Interleaving

To showcase how Interleave handles variations in length, data type, and additional variables, we have modified the data we used earlier for introducing interleave. The program that we used to interleave is still the same:

Data A;
Input Index City $1. Sample Past;
Datalines;
45 A 500 43
56 B 500 50
65 C 600 58
75 D 600 68
85 E 600 82
90 F 500 94
;

Data B;
Input Index City $2. Sample $;
Datalines;
35 AA 600
41 BB 500
48 CC 500
65 DD 600
83 EE 600
83 FF 600
;

Let's review the datasets that were produced:

The preceding dataset's Index variable only has the same attributes. City is a character variable type in both datasets but the length is different. For Sample, the variable type is different in the datasets. The Past variable only exists in dataset A.

When we run the interleaving code, we get the following error:

101 Data Interleave_AB;
102 Set A B;
ERROR: Variable Sample has...