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

Familiarizing yourself with RStudio


Compared to RStudio, the Python development process that has been used in previous chapters has been a bit indirect. Code has been written in a text editor to perform a particular function and then executed as a whole through a separate interface.

Writing code in RStudio is more of an iterative process. Code can be run line by line from the editor, and data and variables are stored continuously within the environment. This means that you can conduct analysis, observe the data, and verify the correctness of your code as you go. The following steps can be used to create an R script in RStudio.

  1. To begin with, open the RStudio program:
  1. From RStudio, you can create a new R script by selecting File | New | Rscript. This will create and open a .R file in the text editor:
  1. This will open the script for editing. You can save the script to your ch6 folder by selecting File | Save. The name of the script is not that important here, but I will name mine r_intro. Note that...