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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

Chapter 10. 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 data sets not already built-into R or available from add-on packages. But very few people I know get by in their careers using R by not importing any data from sources outside 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 almost as few people that can get by using simple CSVs and tab-delimited text locally with the primary read.* commands as can get by not using outside sources of data at all! The unfortunate fact is that many introductory analytics texts largely disregard this...

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