Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
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...