Book Image

Practical Data Wrangling

By : Allan Visochek
Book Image

Practical Data Wrangling

By: Allan Visochek

Overview of this book

Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them. You’ll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You’ll work with different data structures and acquire and parse data from various locations. You’ll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases. The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the end of the book, you’ll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way.
Table of Contents (16 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Chaining operations together


Part of the expressive power of dplyr comes from its ability to chain data processing operations one after the other. The %>% symbol can be used with dplyr functions to chain together operations. The way it works is that the result of all of the expression before the %>% symbol is used as the first argument of the function that comes after the %>% symbol. In the following demonstration, I use the %>% symbol to put together the select and arrange operations:

vehicles.product.arranged <- as_tibble(
  vehicles %>% ## start with the original data
  select(make,model,year,cylinders) %>% ## select the columns
  arrange(make,model,year) ## arrange the rows
)

The previous chain of operations starts with the vehicles dataframe containing the original data. The vehicles dataframe is then followed by the %>% symbol, so it is passed as the first argument to the following function, which is the select() function. As such, the first argument of the select...