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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
Tools
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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

Crafting a publication quality contour plot


Several steps might be required in order to achieve publication quality. The very least ones would be reworking text sizes (legends, labels, titles) and growing axes. Besides covering these changes, this recipe also makes the contours thicker, selects another default theme, picks another color scale, and move legends to the inside.

How to do it...

Let us now start with crafting a publication quality contour plot:

  1. Create a contour plot to work as a departure point using stat_density_2d():
> library(ggplot2)
> h1 <- ggplot(cars, aes(x = speed, y = dist)) +
   stat_density_2d(aes(colour = ..level..), size = 1.2) +
   theme_minimal()
  1. Change the color scale by calling scale_colour_distiller():
> h2 <- h1 + 
  scale_colour_distiller(direction = 1, name = 'density', 
                         breaks = seq(0.0002,0.0014,0.0002), 
                         labels = format(seq(0.0002,0.0014,0.0002),
                                         scientific...
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