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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

Sources of Data

The previous two units (Confirmatory Data Analysis and Inferential Statistics and Predictive Analytics) have focused on teaching both theory and practice in ideal data scenarios, so that our more academic quests can be divorced from outside concerns about the veracity or format of the data. To this end, we deliberately stayed away from datasets not already built into R or available from add-on packages. But very few people I know get by in their careers by using R and not importing any data from sources outside of R packages. Well, we very briefly touched upon how to load data into R (the read.* commands) in the very first chapter of this book, did we not? So we should be all set, right?

Here's the rub: I know a few people who can get by using simple CSVs and tab-delimited text locally with the primary read.* commands and can get by not using outside sources...

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