Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell

Relationships between two categorical variables

Describing the relationships between two categorical variables is done somewhat less often than the other two broad types of bivariate analyses, but it is just as fun (and useful)!

To explore this technique, we will be using the UCBAdmissions dataset, which contains the data on graduate school applicants to the University of California Berkeley (UCB) in 1973.

Before we get started, we have to wrap the dataset in a call to data.frame to coerce it into a data frame type variable—I'll explain why, soon:

ucba <- data.frame(UCBAdmissions) 
       Admit Gender Dept Freq 
  1 Admitted   Male    A  512 
  2 Rejected   Male    A  313 
  3 Admitted Female    A   89 
  4 Rejected Female    A   19 
  5 Admitted   Male    B  353 
  6 Rejected   Male    B  207 

Now, what we want is a count of the frequencies of number of students in each of the following four categories:

  • Accepted female
  • Rejected female
  • Accepted male
  • Rejected male

Do you remember the...