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Mastering SAS Programming for Data Warehousing

You're reading from   Mastering SAS Programming for Data Warehousing An advanced programming guide to designing and managing Data Warehouses using SAS

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781789532371
Length 494 pages
Edition 1st Edition
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Author (1):
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Monika Wahi Monika Wahi
Author Profile Icon Monika Wahi
Monika Wahi
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Managing Data in a SAS Data Warehouse
2. Chapter 1: Using SAS in a Data Mart, Data Lake, or Data Warehouse FREE CHAPTER 3. Chapter 2: Reading Big Data into SAS 4. Chapter 3: Helpful PROCs for Managing Data 5. Chapter 4: Managing ETL in SAS 6. Chapter 5: Managing Data Reporting in SAS 7. Section 2: Using SAS for Extract-Transform-Load (ETL) Protocols in a Data Warehouse
8. Chapter 6: Standardizing Coding Using SAS Arrays 9. Chapter 7: Designing and Developing ETL Code in SAS 10. Chapter 8: Using Macros to Automate ETL in SAS 11. Chapter 9: Debugging and Troubleshooting in SAS 12. Section 3: Using SAS When Serving Warehouse Data to Users
13. Chapter 10: Considering the User Needs of SAS Data Warehouses 14. Chapter 11: Connecting the SAS Data Warehouse to Other Systems 15. Chapter 12: Using the ODS for Visualization in SAS 16. Assessments 17. Other Books You May Enjoy

Using SAS and R for visualizations

R is a software that can be integrated into reporting SAS data. With R, which is open source, it is possible to set up connections between SAS and R data. But the main difference between making plots and other visualizations in SAS versus doing it in R has to do with data handling. As we have seen with SAS, when using PROCs that create plots, such as PROC UNIVARIATE, SAS typically reads or calculates the relevant values from the entire dataset and plots them. In a scatter plot, this is necessary – but it is not necessary for all plots. Although some SAS PROCs have the ability to take in a summary dataset and visualize it, many SAS PROCs require processing the whole underlying dataset.

Let's think of a box plot for a moment. For a box plot, outliers aside, we technically only need to know five different points in order to create the image of the plot: the minimum, 25th percentile, median, 75th percentile, and maximum. If we were creating...

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