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Practical Data Wrangling

You're reading from   Practical Data Wrangling Expert techniques for transforming your raw data into a valuable source for analytics

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286139
Length 204 pages
Edition 1st Edition
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Author (1):
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Allan Visochek Allan Visochek
Author Profile Icon Allan Visochek
Allan Visochek
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Table of Contents (10) Chapters Close

Preface 1. Programming with Data FREE CHAPTER 2. Introduction to Programming in Python 3. Reading, Exploring, and Modifying Data - Part I 4. Reading, Exploring, and Modifying Data - Part II 5. Manipulating Text Data - An Introduction to Regular Expressions 6. Cleaning Numerical Data - An Introduction to R and RStudio 7. Simplifying Data Manipulation with dplyr 8. Getting Data from the Web 9. Working with Large Datasets

Logistical overview


As in the previous chapter, all exercises will be completed using a single R script called dplyr_intro.R. This chapter will include two demonstrations. The first of these is a comparison of fuel economy and gas prices. The second demonstration is a rewrite of some of the work from Chapter 6 using the dplyr library.

The finished product from this chapter, along with all of the exercises from this book, is available in the code folder of the external resources. All of the external resources in this book can be accessed from the Google Drive folder at the following link: https://goo.gl/8S58ra.

Data

For the demonstrations in this chapter three datasets will be used. The first of these is a dataset of fuel economy data for various vehicle models. The fuel economy dataset is made available by the U.S. Department of Energy. The fuel economy dataset has a large number of non descriptive data variables, so I've also included a link to the description of the data in the external resources...

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