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

Visualizing geographical distributions of pay


We created datasets that contain the data we need to visualize average pay and employment by county and state. In this recipe, we will visualize the geographical distribution of pay by shading the appropriate areas of the map with a color that maps to a particular value or range of values. This is commonly referred to as a chloropleth map; this visualization type has become increasingly popular over the last few years as it has become much simpler to make such maps, especially online. Other geographic visualizations will overlay a marker or some other shape to denote data; there is no need to fill specific shapes with geographically meaningful boundaries.

Getting ready

After the last recipe, you should be ready to use the datasets we created to visualize geographical distributions. We will use the ggplot2 package to generate our visualizations. We will also use the RColorBrewer package, which provides "palettes" of colors that are visually appealing...