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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

Functional programming as a main tidyverse principle

It would serve us well to point out some of these principles before we get to using dplyr--this will help us contrast these tidy tools with data.table’s approach and add context to the types of manipulations that we’ll be seeing shortly. There is one main principle, in particular, that I believe underlies a lot of the other doctrines of the manifesto: the call to embrace functional programming.

Functional programming is a hot topic in computer science research. There’s a whole lot to know about this programming paradigm, and any attempt to distill it down to a paragraph explanation (which we are about to do) will be an oversimplification. But, mainly, this paradigm strongly advocates for the use of functions as routines that (a) do not modify their arguments, (b) do not modify anything, and (c) whose behavior...

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