Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On SAS for Data Analysis

You're reading from   Hands-On SAS for Data Analysis A practical guide to performing effective queries, data visualization, and reporting techniques

Arrow left icon
Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781788839822
Length 346 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Harish Gulati Harish Gulati
Author Profile Icon Harish Gulati
Harish Gulati
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: SAS Basics FREE CHAPTER
2. Introduction to SAS Programming 3. Data Manipulation and Transformation 4. Section 2: Merging, Optimizing, and Descriptive Statistics
5. Combining, Indexing, Encryption, and Compression Techniques Simplified 6. Power of Statistics, Reporting, Transforming Procedures, and Functions 7. Section 3: Advanced Programming
8. Advanced Programming Techniques - SAS Macros 9. Powerful Functions, Options, and Automatic Variables Simplified 10. Section 4: SQL in SAS
11. Advanced Programming Techniques Using PROC SQL 12. Deep Dive into PROC SQL 13. Section 5: Data Visualization and Reporting
14. Data Visualization 15. Reporting and Output Delivery System 16. Other Books You May Enjoy

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...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image