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

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...