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Data Analysis with R, Second Edition

You're reading from   Data Analysis with R, Second Edition A comprehensive guide to manipulating, analyzing, and visualizing data in R

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
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Author (1):
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Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
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Table of Contents (19) Chapters Close

Preface 1. RefresheR FREE CHAPTER 2. The Shape of Data 3. Describing Relationships 4. Probability 5. Using Data To Reason About The World 6. Testing Hypotheses 7. Bayesian Methods 8. The Bootstrap 9. Predicting Continuous Variables 10. Predicting Categorical Variables 11. Predicting Changes with Time 12. Sources of Data 13. Dealing with Missing Data 14. Dealing with Messy Data 15. Dealing with Large Data 16. Working with Popular R Packages 17. Reproducibility and Best Practices 18. Other Books You May Enjoy

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 variableI'll explain why, soon:

ucba <- data.frame(UCBAdmissions) 
head(ucba) 
       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...

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