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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


For bothdata.tableanddplyr/tidyr we went on a very substantial walk-through of the functions and abilities they offer, but not of all of them. Feel free to read the documentation on all of these packages to learn about some of the niceties that we didn’t have the space to mention here.

I hope that you, dear reader, got a lot out of this chapter. Even if you don’t eventually end up using data.table or the tidyverse, I hope you’ve gained a better sense of how flexible R is, learned that R is just as powerful at manipulating data as it is analyzing it, and that we don’t have to settle for base R computation speeds if we don’t want to. Most of the functions we’ve seen in this chapter—in both sections—are a full order of magnitude faster than their base R equivalents. This can be (and often is for very large data sets) the difference between your data wrangling taking a few hours and it taking a few minutes or seconds.

 

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