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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

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Index

Using parallelization

As we saw in this chapter's introduction, one of the limitations of R (and most other programming languages) was that it was created before commodity personal computers had more than one processor or core. As a result, by default, R runs only one process and thus, makes use of one processor/core at a time.

If you have more than one core on your CPU, it means that when you leave your computer alone for a few hours during a long-running computation, your R task is running on one core while the others are idle. Clearly this is not ideal; if your R task took advantage of all the available processing power, you can get massive speed improvements.

Parallel computation (of the type we'll be using) works by starting multiple processes at the same time. The operating system then assigns each of these processes to a particular CPU. When multiple processes run at the same time, the time to completion is only as long as the longest process, as opposed to the time to complete all...

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