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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

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
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Author (1):
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Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
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Table of Contents (19) Chapters Close

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 18. Other Books You May Enjoy

R projects

Although rare, there are some cases where a single R script contains the totality of your research/analyses. This may happen if you are doing simulation studies, for example. For most cases, an analysis will consist of a script (or scripts), and at least one data set. I refer to any R analysis that uses at least two files as an R project.

In R projects, special attention must be paid to how the files are stored relative to each other. For example, if we stored the file SAT_Scores_NYC_2010.csv on our desktop, the data import line would have read:

read.csv("/Users/bensisko/Desktop/SAT_Scores_NYC_2010.csv") 

If you want to send this analysis to a contributor to be replicated, we would send them the script and the data file. Even if we instructed them to place the file on their desktop, the script would still not be reproducible. Our collaborators on Windows and...

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