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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

Multivariate data


In this chapter, we are going to describe relationships and begin working with multivariate data, which is a fancy way of saying samples containing more than one variable.

The troublemaker reader might remark that all the datasets that we've worked with thus far (mtcars and airquality) have contained more than one variable. This is technically true but only technically. The fact of the matter is that we've only been working with one of the dataset's variables at any one time. Note that multivariate analytics is not the same as doing univariate analytics on more than one variable—multivariate analyses and describing relationships involve several variables at the same time.

To put this more concretely, in the last chapter we described the shape of, say, the temperature readings in the airquality dataset:

head(airquality) 
Ozone Solar.R Wind Temp Month Day 
1    41     190  7.4   67     5   1 
2    36     118  8.0   72     5   2 
3    12     149 12.6   74     5   3 
4    18 ...
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