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R Data Visualization Recipes

You're reading from   R Data Visualization Recipes A cookbook with 65+ data visualization recipes for smarter decision-making

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788398312
Length 366 pages
Edition 1st Edition
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Author (1):
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Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Author Profile Icon Vitor Bianchi Lanzetta
Vitor Bianchi Lanzetta
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Table of Contents (13) Chapters Close

Preface 1. Installation and Introduction FREE CHAPTER 2. Plotting Two Continuous Variables 3. Plotting a Discrete Predictor and a Continuous Response 4. Plotting One Variable 5. Making Other Bivariate Plots 6. Creating Maps 7. Faceting 8. Designing Three-Dimensional Plots 9. Using Theming Packages 10. Designing More Specialized Plots 11. Making Interactive Plots 12. Building Shiny Dashboards

Introduction

There are two general ways in which one variable plots are often used:

  • As chief car of a single variable investigation
  • To support multiple variables investigations

However, it certainly does not stop there. A wonderful usage of single variable plots is to make visual stands for hypothesis acceptance paradigmas as well to visualize Monte Carlo simulations results. In fact there are some stories about 1938 nobelist, the physicist Enrico Fermi. Before running experiments he would address Monte Carlo approximations to predict outcomes. Fermi reported the predictions to his friends; as the story goes, his predictions were remarkably accurate.

Think about it, Fermi was making Monte Carlo approximations before computacional power was a thing. This chapter is about to demonstrate how to run a simulation and make visual stands from it. Why not to predict and report...

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