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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

Making faceted scatterplots


It's not hard to figure out how a faceted scatterplot can be drawn using the previous recipe just by skipping one single function (geom_smooth()). Nonetheless, this recipe is sailing outer seas while investigating heights (centimeters) and weights (kilos) coming from Australian athletes of different sports and categories.

In order to accomplish this, we're relying on the DAAG::ais data set. To avoid the excess of information, the analysis is narrowed, contemplating a few sports only, not all the ones present in this data frame. 

Getting ready

Look out for the DAAG package:

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

Once it's installed, the recipe can go on.

How to do it...

Let us start with making faceted scatterplot:

  1. Store the data frame in a new variable and narrow it down:
> data_sport <- DAAG::ais
> sports <- c('B_Ball','Field','Row','T_400m')
> data_sport <- data_sport[data_sport$sport %in% sports,]
  1. Draw a scatterplot as usual and sum the...
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