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R Data Analysis Cookbook, Second Edition

You're reading from   R Data Analysis Cookbook, Second Edition Customizable R Recipes for data mining, data visualization and time series analysis

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787124479
Length 560 pages
Edition 2nd Edition
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Authors (3):
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Kuntal Ganguly Kuntal Ganguly
Author Profile Icon Kuntal Ganguly
Kuntal Ganguly
Shanthi Viswanathan Shanthi Viswanathan
Author Profile Icon Shanthi Viswanathan
Shanthi Viswanathan
Viswa Viswanathan Viswa Viswanathan
Author Profile Icon Viswa Viswanathan
Viswa Viswanathan
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Table of Contents (14) Chapters Close

Preface 1. Acquire and Prepare the Ingredients - Your Data FREE CHAPTER 2. What's in There - Exploratory Data Analysis 3. Where Does It Belong? Classification 4. Give Me a Number - Regression 5. Can you Simplify That? Data Reduction Techniques 6. Lessons from History - Time Series Analysis 7. How does it look? - Advanced data visualization 8. This may also interest you - Building Recommendations 9. It's All About Your Connections - Social Network Analysis 10. Put Your Best Foot Forward - Document and Present Your Analysis 11. Work Smarter, Not Harder - Efficient and Elegant R Code 12. Where in the World? Geospatial Analysis 13. Playing Nice - Connecting to Other Systems

Creating spatial data frames by combining regular data frames with spatial objects

Often we have data that has some geographical aspect to it (such as postal codes) but does not have sufficient geographic coordinate information to plot. In order to display such information on a map representation, we will need to embellish the basic data with enough geographic coordinate information to plot.

The sp package has several SpatialXXXDataFrame classes to represent geographic information along with additional descriptive data. This recipe shows you how we can create and plot such objects. In this recipe, we demonstrate how to get a map from the maps package and convert it to a SpatialPolygons object. We then add data from a normal data frame to create a SpatialPolygonsDataFrame object, which we then plot.

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