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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

Product type Paperback

Published in Mar 2018

Publisher Packt

ISBN-13 9781788393720

Length 570 pages

Edition 2nd Edition

Languages

R

Tools

ggplot

Concepts

Data Analysis

Table of Contents (24) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. RefresheR FREE CHAPTER

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

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Index

Wait to optimize

Prominent computer scientist and mathematician Donald Knuth famously stated:

Premature optimization is the root of all evil.

I, personally, hold that money is the root of all evil, but premature optimization is definitely up there!

Why is premature optimization so evil? Well, there are a few reasons. First, programmers can sometimes be pretty bad at identifying what the bottleneck of a program - the routine or routines that have the slowest throughput - is, and optimize the wrong parts of a program. Identification of bottlenecks can most accurately be performed by profiling your code after it's been completed in an un-optimized form.

Secondly, clever tricks and shortcuts for speeding up code often introduce subtle bugs and unexpected behavior. Now, the speedup of the code (if there is any!) must be taken in context with the time it took to complete the bug-finding and bug-fixing expedition; occasionally, a net negative amount of time...

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