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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

Other data formats


One of things that make R great is the wealth of high-quality add-on packages. As you might expect, there are many of these add-on packages with the ability to import data in a multitude of other formats. Whether it's an arcane markup-language, a proprietary binary file, excel spreadsheet, and so on, there is almost certainly an R package out there for you to handle it. But how to find them?

One way is to browse the community maintained CRAN Task Views (https://cran.r-project.org/web/views/). A task view is a way to browse for packages related to a particular topic, domain, or special interest. The germane Task View, here, is the Web Technologies Task View (https://cran.r-project.org/web/views/WebTechnologies.html). You'll notice that jsonlite and the XML package are mentioned on the first page.

The easiest way to discover these packages, though, is through your favorite web browser. For example, if you are looking for a package to import YAML data (yet another data serialization...

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