Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By : Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda
Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By: Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Overview of this book

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Importing automobile fuel efficiency data into R


Once you have downloaded and installed everything in the previous recipe, Preparing R for your first project, you can import the dataset into R to start doing some preliminary analysis and get a sense of what the data looks like.

Getting ready

Much of the analysis in this chapter is cumulative, and the efforts of the previous recipes will be used for subsequent recipes. Thus, if you completed the previous recipe, you should have everything you need to continue.

How to do it...

The following steps will walk you through the initial import of the data into the R environment:

  1. First, set the working directory to the location where we saved the vehicles.csv.zip file:
setwd("path") 

Substitute the path for the actual directory.

  1. We can load the data directly from compressed (ZIP) files, as long as you know the filename of the file inside the ZIP archive that you want to load:
vehicles <- read.csv(unz("vehicles.csv.zip", "vehicles.csv"), 
 stringsAsFactors...