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Data Analysis with R, Second Edition - Second Edition

You're reading from  Data Analysis with R, Second Edition - Second Edition

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Pages 570 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (24) Chapters close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. RefresheR 2. The Shape of Data 3. Describing Relationships 4. Probability 5. Using Data To Reason About The World 6. Testing Hypotheses 7. Bayesian Methods 8. The Bootstrap 9. Predicting Continuous Variables 10. Predicting Categorical Variables 11. Predicting Changes with Time 12. Sources of Data 13. Dealing with Missing Data 14. Dealing with Messy Data 15. Dealing with Large Data 16. Working with Popular R Packages 17. Reproducibility and Best Practices 1. Other Books You May Enjoy Index

Summary


This chapter began with a discussion on relation databases. You've learned that the DBI package defines a standard interface on which various database drivers build upon. You then learned how to query these types of databases, and to load the results in R.

Next, you gained an appreciation for JSON and XML (right?), and how to approach the importing of data from these formats. We then put our chops to the test by wielding data provided to us by two different web service APIs.

I stealthily snuck in some fancy new R constructs in this chapter. For example, prior to this chapter, we never explicitly worked with lists before.

Finally, you learned about how to look for information beyond which this chapter can provide, and some other places from which you can get data to play around with.

In the next chapter, we won't be talking about how to load data from different sources; we'll be talking about how to deal with disorderly data that is already loaded.

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