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R Data Analysis Cookbook, Second Edition

You're reading from   R Data Analysis Cookbook, Second Edition Customizable R Recipes for data mining, data visualization and time series analysis

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787124479
Length 560 pages
Edition 2nd Edition
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Authors (3):
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Kuntal Ganguly Kuntal Ganguly
Author Profile Icon Kuntal Ganguly
Kuntal Ganguly
Shanthi Viswanathan Shanthi Viswanathan
Author Profile Icon Shanthi Viswanathan
Shanthi Viswanathan
Viswa Viswanathan Viswa Viswanathan
Author Profile Icon Viswa Viswanathan
Viswa Viswanathan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Acquire and Prepare the Ingredients - Your Data 2. What's in There - Exploratory Data Analysis FREE CHAPTER 3. Where Does It Belong? Classification 4. Give Me a Number - Regression 5. Can you Simplify That? Data Reduction Techniques 6. Lessons from History - Time Series Analysis 7. How does it look? - Advanced data visualization 8. This may also interest you - Building Recommendations 9. It's All About Your Connections - Social Network Analysis 10. Put Your Best Foot Forward - Document and Present Your Analysis 11. Work Smarter, Not Harder - Efficient and Elegant R Code 12. Where in the World? Geospatial Analysis 13. Playing Nice - Connecting to Other Systems

Using the split-apply-combine strategy with plyr

A common analytical pattern is to split data into pieces, apply some function to each piece, and then combine the results back together. The plyr package provides simple functions to apply this pattern, while simplifying the specification of the object types through systematic naming of the functions.

The plyr function name has three parts, XYply, where X specifies what sort of input you're giving , Y specifies the sort of output you want and ply part is common to all function names. X and Y represent one of the following options:

  • a = array
  • d = data.frame
  • l = list
  • _ = no output; only valid for Y; for example, useful when you're operating
    on a list purely for the side effects, making a plot, or sending output to screen/file

ddply has its input and output as data frames, and ldply takes a
list as input and produces a data...

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