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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
Languages
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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

Designing a three-dimensional plot with plotly


True three-dimensional plots can be drawn using plotly. There are a wide range of types available, from 3D scatter plots, to 3D lines, 3D surfaces, and 3D meshes. There is a great thing about 3D plots made with plotly--the user is able to drag the illustration, see it from different angles, and zoom in and out.

Getting ready

Make sure that the MASS package is already downloaded and installed:

> if( !require(MASS)){ install.packages('MASS')}

If it's missing and internet connection is fine, above code will do the job for you.

How to do it...

Here is how we design a 3 dimensional plot with plotly:

  1. To begin with, have your data created using MASS::kde2d(): 
> cars_d <- MASS::kde2d(cars$speed, cars$dist, n =50)
  1. Call plot_ly() and use add_surface() to create a 3D surface:
> library(plotly)
> plot_ly(x = cars_d$x, 
          y = cars_d$y, 
          z = cars_d$z) %>% 
   add_surface()

A snapshot from the original output can be seen at the following...

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