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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 2. The Shape of Data FREE CHAPTER 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 dplyr and tidyr to manipulate data

It’s perhaps a little unfair to say that data.table is opinionated. If it were, though, its main point of view is that the potential pitfalls of call by reference are sometimes a more than fair price to pay for unparalleled speed and memory-efficiency. It could probably also be said that data.table also prefers conciseness and prefers to (sometimes drastically) modify the semantics of a function by the use of arguments, instead of creating other functions.

There is, however, a new approach to non-base-R data manipulation on the scene. This package also puts efficiency at a premium, but not at the expense of code safety, readability, consistency, and interpretability. (This, by the way, is not to say, that data.table is unreadable, uninterpretable, or inconsistent; it’s just that this other package very explicitly states that...

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