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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

Using optimized packages

Many of the functionalities in base R have alternative implementations available in contributed packages. Quite often, these packages offer a faster or less memory-intensive substitute for the base R equivalent. For example, in addition to adding a ton of extra functionality, the glmnet package performs regression far faster than glm, in my experience.

For faster data import, you might be able to use fread from the data.table package, or the read_* family of functions from the readr package. It is not uncommon for data import tasks that used to take several hours to take only a few minutes with these read functions.

For common data manipulation tasks, such as merging (joining), conditional selection, sorting, and so on, you will find that the data.table and dplyr packages offer incredible speed improvements. Both of these packages have a ton of users who swear by them, and the community support is solid. You'd be well advised to become proficient in one of these packages...

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