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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 2. The Shape of Data FREE CHAPTER 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

Reshaping data with tidyr

Finally, we get to turn our attention to the other staple of the tidyverse, tidyr.

Though this package offers more functionality, the main purpose of this package is to reshape data (convert from long to wide format) in a tidy manner.

Let’s recreate long, a long format that contains the play counts for each year/month, using the following code:

> long <- tracks %>%
+ group_by(theyear=year(thedate), themonth) %>%
+ summarise(N=n())
> long
# A tibble: 107 x 3
# Groups: theyear [?]
theyear themonth N
<dbl> <ord> <int>
1 2008 Jan 877
2 2008 Feb 984
3 2008 Mar 1486
4 2008 Apr 1101
...
# ... with 97 more rows

Now let’s get this into wide format with the different month in its own columns.

The tidyr equivalent of the dcast function is spread. As its arguments, it takes...

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