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Data Analysis with R, Second Edition - Second Edition

You're reading from  Data Analysis with R, Second Edition - Second Edition

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Pages 570 pages
Edition 2nd Edition
Languages

Table of Contents (24) Chapters

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
RefresheR The Shape of Data Describing Relationships Probability Using Data To Reason About The World Testing Hypotheses Bayesian Methods The Bootstrap Predicting Continuous Variables Predicting Categorical Variables Predicting Changes with Time Sources of Data Dealing with Missing Data Dealing with Messy Data Dealing with Large Data Working with Popular R Packages Reproducibility and Best Practices Other Books You May Enjoy Index

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 the data to transform, the column that contains the categories to be spread across different columns, and the value...

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