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

Summarizing data by category


The summarize() function reduces the columns of a dataframe to a summary. The arguments to the summarize() function are expressions which create new variables a function of the rows of other columns. Here are a couple examples of possible arguments to the summarize() function:

  • avg.column.1 = mean(column.1) 
  • sum.column.2 = sum(column.2)

The group_by() function causes all of the subsequent operations to be performed by group. The arguments to the group_by() function are the names of columns that the result should be grouped by. When the group_by() function is followed by the summarize() function, the summary is applied to each unique group. 

The best way to understand the group_by() function is with a demonstration. In the following continuation of dplyr_intro.R, the fuel economy data is grouped by year and summarized by the mean value of barrels08. Additionally, the filter() function is used to  filter the data to include only Toyota Camry models.

Note

The barrels08...