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Practical Data Science Cookbook, Second Edition

You're reading from   Practical Data Science Cookbook, Second Edition Data pre-processing, analysis and visualization using R and Python

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787129627
Length 434 pages
Edition 2nd Edition
Languages
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Authors (5):
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 Tattar Tattar
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Tattar
Bhushan Purushottam Joshi Bhushan Purushottam Joshi
Author Profile Icon Bhushan Purushottam Joshi
Bhushan Purushottam Joshi
Sean P Murphy Sean P Murphy
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Sean P Murphy
ABHIJIT DASGUPTA ABHIJIT DASGUPTA
Author Profile Icon ABHIJIT DASGUPTA
ABHIJIT DASGUPTA
Anthony Ojeda Anthony Ojeda
Author Profile Icon Anthony Ojeda
Anthony Ojeda
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Table of Contents (17) Chapters Close

Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
1. Preparing Your Data Science Environment FREE CHAPTER 2. Driving Visual Analysis with Automobile Data with R 3. Creating Application-Oriented Analyses Using Tax Data and Python 4. Modeling Stock Market Data 5. Visually Exploring Employment Data 6. Driving Visual Analyses with Automobile Data 7. Working with Social Graphs 8. Recommending Movies at Scale (Python) 9. Harvesting and Geolocating Twitter Data (Python) 10. Forecasting New Zealand Overseas Visitors 11. German Credit Data Analysis

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...

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