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

Adding geographical information


The main purpose of this chapter is to look at the geographical distribution of wages across the US. Mapping this out requires us to first have a map. Fortunately, maps of the US, both at the state-and county-levels, are available in the maps package, and the data required to make the maps can be extracted. We will align our employment data with the map data in this recipe so that the correct data is represented at the right location on the map.

Getting ready

We already have the area dataset imported into R, so we are ready to go.

How to do it…

The following steps will guide you through the process of creating your first map in R:

  1. Let's first look at the data in area:

    head (area)
    

    The output is shown in the following screenshot:

    We see that there is something called area_fips here. Federal Information Processing Standards (FIPS) codes are used by the Census Bureau to designate counties and other geographical areas in the US.

  2. We want to capitalize all the names, according...