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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

Concepts

Table of Contents (24) Chapters

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

Leave a review - let other readers know what you think

Index

Exercises

Practice the following exercises to revise the concepts learned in this chapter:

How did we waste computation in the similarity_matrix function?
Both the Last.fm and the MusicBrainz API have a count value associated with each tag, which can be taken to represent the extent to which the tag applied to the artist. By ignoring this field, in both cases we implicitly used a count of 1 for every tag, making well-fitting tags just as important as relatively less well-fitting ones. Rewrite the code to take count into account, and weigh each tag proportionally to its count value. This will be challenging, but it will be invaluable for understanding the material. It will also boost your confidence as an R programmer once you finish. Go you!
How else might you be able to extend and improve upon our ragtag recommender system?
The efficient market hypothesis posits that since the price of financial instruments reflects all the relevant information about its value at any given time, it is impossible...

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