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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

Leave a review - let other readers know what you think

Index

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 is always the same given the same inputs. The functional approach, as a consequence of its ideals, lends itself to the creation and...

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