Book Image

Practical Data Science Cookbook

By : Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta
Book Image

Practical Data Science Cookbook

By: Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta

Overview of this book

<p>As increasing amounts of data is generated each year, the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data will have a competitive advantage over companies that don't, and this will drive a higher demand for knowledgeable and competent data professionals.</p> <p>Starting with the basics, this book will cover how to set up your numerical programming environment, introduce you to the data science pipeline (an iterative process by which data science projects are completed), and guide 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 in the two most popular programming languages for data analysis—R and Python.</p>
Table of Contents (18 chapters)
Practical Data Science Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Analyzing and understanding football data


Now that we have obtained and cleaned the data, let's take some time to explore it, gain an understanding of what the different fields mean, and learn how we can use them to create something useful.

Getting ready

If you completed the previous recipe, you should have cleaned and formatted offense and defense datasets in preparation for this recipe.

How to do it…

In order to analyze the data, complete the following steps:

  1. The first thing we will do is combine the offense and defense data frames into a data frame called combined. This will get all of our data in one place and make it easier for us to do some exploration:

    combined <- merge(offense, defense, by.x="Team", by.y="Team")
    

    Since some of the offense and defense columns have the same name, we will rename them to avoid confusion later. We'll also get rid of the column from the defense data frame that shows the number of games because it is redundant now that we have combined data:

    colnames(combined...