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R Data Analysis Cookbook - Second Edition

You're reading from  R Data Analysis Cookbook - Second Edition

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787124479
Pages 560 pages
Edition 2nd Edition
Languages
Authors (3):
Kuntal Ganguly Kuntal Ganguly
Profile icon Kuntal Ganguly
Shanthi Viswanathan Shanthi Viswanathan
Profile icon Shanthi Viswanathan
Viswa Viswanathan Viswa Viswanathan
Profile icon Viswa Viswanathan
View More author details
Toc

Table of Contents (14) Chapters close

Preface 1. Acquire and Prepare the Ingredients - Your Data 2. What's in There - Exploratory Data Analysis 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

Slicing, dicing, and combining data with data tables

R provides several packages to do data analysis and data manipulation. Over and above the apply family of functions, the most commonly used packages are plyr, reshape, dplyr, and data.table. In this recipe, we will cover data.table, which processes large amounts of data very efficiently, without our having to write detailed procedural code.

Getting ready

Download the files for this chapter and store the auto-mpg.csv, employees.csv, and departments.csv files in your R working directory. Read the data and create factors for cylinders in auto-mpg.csv:

> auto <- read.csv("auto-mpg.csv", stringsAsFactors=FALSE) 
> auto$cylinders <- factor(auto$cylinders...
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