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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

Selecting a graphics device

R can send its output to several different graphic devices to display graphics in different formats. By default, R prints to the screen. However, we can save graphs in the following file formats as well: PostScript, PDF, PNG, JPEG, Windows metafile, Windows BMP, and so on.

Getting ready

If you have not already done so, download the data files for this chapter, ensure that the auto-mpg.csv file is available in your R environment's working directory, and run the following commands:

> auto <- read.csv("auto-mpg.csv")
> cylinders <- factor(cylinders, levels = c(3,4,5,6,8), labels = c("3cyl", "4cyl", "5cyl", "6cyl", "8cyl"))
...
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