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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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Anthony Ojeda Anthony Ojeda
Author Profile Icon Anthony Ojeda
Anthony Ojeda
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
ABHIJIT DASGUPTA ABHIJIT DASGUPTA
Author Profile Icon ABHIJIT DASGUPTA
ABHIJIT DASGUPTA
Sean P Murphy Sean P Murphy
Author Profile Icon Sean P Murphy
Sean P Murphy
Bhushan Purushottam Joshi Bhushan Purushottam Joshi
Author Profile Icon Bhushan Purushottam Joshi
Bhushan Purushottam Joshi
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Table of Contents (12) Chapters Close

Preface 1. Preparing Your Data Science Environment 2. Driving Visual Analysis with Automobile Data with R FREE CHAPTER 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

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.

  1. We want to capitalize all the names...
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