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Data Analysis with R

You're reading from  Data Analysis with R

Product type Book
Published in Dec 2015
Publisher
ISBN-13 9781785288142
Pages 388 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Data Analysis with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
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. Predicting Continuous Variables 9. Predicting Categorical Variables 10. Sources of Data 11. Dealing with Messy Data 12. Dealing with Large Data 13. Reproducibility and Best Practices Index

Version control


A very compelling benefit to our neat hierarchical organization scheme is that it lends itself to easy integration with version control systems. Version control systems, at a basic level, allow one to track changes/revisions to a set of files, and easily roll back to previous states of the set of files.

A simple (and inadequate) approach is to compress your analysis project at regular intervals, and post-fix the filename of each compressed copy with a timestamp. This way, if you make a mistake, and would like to revert to a previous version, all you have to do is delete your current project and un-compress the project from the time you want to roll back to.

A far more sane solution is to use a remote file synchronization service that features revision tracking. The most popular of these services at the time of writing is Dropbox, though there are others such as TeamDrive and Box. These services allow you to upload your project into the cloud. When you make changes to your local...

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