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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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Toc

Table of Contents (14) Chapters Close

Preface 1. Acquire and Prepare the Ingredients - Your Data 2. What's in There - Exploratory Data Analysis FREE CHAPTER 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

Introduction

Base R's graphics provide plots to feed ranges of data as x and y elements. They manipulate colors, scale dimensions, and present other parts of the graph as graphical elements or options. However, they lack advanced plotting or visualization features. The ggplot2 library implements the grammar of graphics, a coherent system for describing and building graphs. The grammar of graphics is designed to help you in separating and identifying each step of the charting process to better decide on the best way to visualize data. A ggplot2 graph is built up from a few basic elements:

  • Data: The raw data for visualization
  • Geometries (geom_): The geometric shapes that will represent the data
  • Aethetics (aes): The aesthetics of the geometric and statistical objects, such as color, size, shape, and position
  • Scales (scale_): This what lies defines the mapping between the data...
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