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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
Toc

Table of Contents (24) Chapters close

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 Index

R scripting


The absolute first thing you should know about standard R workflows is that programs are not generally written directly at the interactive R interpreter. Instead, R programs are usually written in a text file (with an .r or .R file extension). These are usually referred to as R scripts. When these scripts are completed, the commands in this text file are usually executed all at once - we'll get to see how, soon. During development of the script, however, the programmer usually executes portions of the script interactively to get feedback and confirm proper behavior. This interactive component to R scripting allows for building each command or function iteratively.

I've known some serious R programmers who copy and paste from their favorite text editor into an interactive R session to achieve this effect. To most people, particularly beginners, the better solution is to use an editor that can send R code from the script that is actively being written to an interactive R console...

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