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Data Analysis with R, Second Edition - Second Edition

You're reading from  Data Analysis with R, Second Edition - Second Edition

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Pages 570 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (24) Chapters close

Title Page
Copyright and Credits
Packt Upsell
Contributors
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. 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 1. Other Books You May Enjoy Index

Using JSON


Javascript Object Notation (JSON) is a standardized human-readable data format that plays an enormous role in communication between web browsers and web servers. JSON was originally borne out of a need to represent arbitrarily complex data structures in Javascript, a web scripting language, but it has since grown into a language agnostic data serialization format.

It is a common need to import and parse JSON in R, particularly when working with web data. For example, it is very common for websites to offer web services that take an arbitrary query from a web browser, and return the response as JSON. We will look at an example of this very use case later in this section.

For our first look into JSON parsing for R, we'll use the jsonlite package to read a small JSON string, which serializes some information about the best musical act in history, The Beatles:

library(jsonlite) 
 
example.json <- ' 
{ 
  "thebeatles": { 
    "formed": 1960, 
    "members": [ 
      { 
        "firstname...
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