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Data Analysis with R

You're reading from  Data Analysis with R

Product type Book
Published in Dec 2015
Publisher
ISBN-13 9781785288142
Pages 388 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Data Analysis with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. RefresheR 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. Predicting Continuous Variables 9. Predicting Categorical Variables 10. Sources of Data 11. Dealing with Messy Data 12. Dealing with Large Data 13. Reproducibility and Best Practices Index

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 dataset UCBAdmissions, which contains the data on graduate school applicants to the University of California Berkeley in 1973.

Before we get started, we have to wrap the dataset in a call to data.frame for coercing it into a data frame type variable—I'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 number of students in each of the following four categories:

  • Accepted female

  • Rejected female

  • Accepted male

  • Rejected male

Do you remember the frequency...

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