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Data Analysis with R, Second Edition

You're reading from   Data Analysis with R, Second Edition A comprehensive guide to manipulating, analyzing, and visualizing data in R

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
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Author (1):
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Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
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Table of Contents (19) 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. 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 18. Other Books You May Enjoy

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...

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