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Data Analysis with R

You're reading from   Data Analysis with R Click here to enter text.

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Product type Paperback
Published in Dec 2015
Publisher
ISBN-13 9781785288142
Length 388 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (15) Chapters Close

Preface 1. RefresheR FREE CHAPTER 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. Predicting Continuous Variables 9. Predicting Categorical Variables 10. Sources of Data 11. Dealing with Messy Data 12. Dealing with Large Data 13. Reproducibility and Best Practices 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 has 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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