Book Image

R Data Analysis Cookbook - Second Edition

By : Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan
Book Image

R Data Analysis Cookbook - Second Edition

By: Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan

Overview of this book

Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.
Table of Contents (14 chapters)

Creating dummies for categorical variables

In situations where we have categorical variables (factors) but need to use them in analytical methods that require numbers (for example, K nearest neighbors (KNN), Linear Regression), we need to create dummy variables.

Getting ready

Read the data-conversion.csv file and store it in the working directory of your R environment. Install the dummies package. Then read the data:

> install.packages("dummies") 
> library(dummies)
> students <- read.csv("data-conversion.csv")

How to do it...

Create dummies for all factors in the data frame:

> <-, sep = ".") 
> names(

[1] "Age" "State.NJ" "State.NY" "State.TX" "State.VA"
[6] "Gender.F" "Gender.M" "Height" "Income"

The data frame now contains all the original variables and the newly added dummy variables. The function has created dummy variables for all four levels of State and two levels of Gender factors. However, we will generally omit one of the dummy variables for State and one for Gender when we use machine learning techniques.

We can use the optional argument all = FALSE to specify that the resulting data frame should contain only the generated dummy variables and none of the original variables.

How it works...

The function creates dummies for all the factors in the data frame supplied. Internally, it uses another dummy() function which creates dummy variables for a single factor. The dummy() function creates one new variable for every level of the factor for which we are creating dummies. It appends the variable name with the factor level name to generate names for the dummy variables. We can use the sep argument to specify the character that separates them; an empty string is the default:

> dummy(students$State, sep = ".") 

State.NJ State.NY State.TX State.VA
[1,] 1 0 0 0
[2,] 0 1 0 0
[3,] 1 0 0 0
[4,] 0 0 0 1
[5,] 0 1 0 0
[6,] 0 0 1 0
[7,] 1 0 0 0
[8,] 0 0 0 1
[9,] 0 0 1 0
[10,] 0 0 0 1

There's more...

In situations where a data frame has several factors, and you plan on using only a subset of them, you create dummies only for the chosen subset.

Choosing which variables to create dummies for

To create a dummy only for one variable or a subset of variables, we can use the names argument to specify the column names of the variables we want dummies for:

> students.new1 <-,     names = c("State","Gender") , sep = ".")