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

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...