Book Image

Machine Learning with R Cookbook, Second Edition - Second Edition

By : Yu-Wei, Chiu (David Chiu)
Book Image

Machine Learning with R Cookbook, Second Edition - Second Edition

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.
Table of Contents (21 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Converting attributes to factor


In R, since nominal, ordinal, interval, and ratio variable are treated differently in statistical modeling, we have to convert a nominal variable from a character or int into a factor.

Getting ready

You need to have completed the previous recipe, that is, viewing the airquality dataset using the str and head commands, to get an idea about what it contains.

How to do it...

Perform the following steps to convert the types of character variables:

  1. Assign the airquality dataset to mydata:
> mydata <- airquality
  1. To transform the variable from the int numeric type to the factor categorical type, you can cast factor:
> mydata$Month = factor(mydata$Month)
  1. Print out the variable with the str function and again, you can see that Month is now transformed into the factor as follows:
        > str(mydata)
        Output
        'data.frame': 153 obs. of 6 variables:$ Ozone : 
        int 41 36 12 18 NA 28 23 19 8 NA ...
         $ Solar.R: int 190 118 149 313 NA NA 299...