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R Data Visualization Recipes

R Data Visualization Recipes

By : Bianchi Lanzetta
4 (1)
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R Data Visualization Recipes

R Data Visualization Recipes

4 (1)
By: Bianchi Lanzetta

Overview of this book

R is an open source language for data analysis and graphics that allows users to load various packages for effective and better data interpretation. Its popularity has soared in recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions. This book is an update to our earlier R data visualization cookbook with 100 percent fresh content and covering all the cutting edge R data visualization tools. This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization using R. It starts off with the basics of ggplot2, ggvis, and plotly visualization packages, along with an introduction to creating maps and customizing them, before progressively taking you through various ggplot2 extensions, such as ggforce, ggrepel, and gganimate. Using real-world datasets, you will analyze and visualize your data as histograms, bar graphs, and scatterplots, and customize your plots with various themes and coloring options. The book also covers advanced visualization aspects such as creating interactive dashboards using Shiny By the end of the book, you will be equipped with key techniques to create impressive data visualizations with professional efficiency and precision.
Table of Contents (13 chapters)
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Using more suitable colors for geom_dotplot


Speaking about ggplot2 dot plots, there is a sort of a hacking solution to avoid colored points to overlay one another: manually setting up a vector of colors. However, creating such a vector wouldn't be enough, it has to be ordered properly or else a very wrong result shall be outputted. This recipe sticks with the Salaries data set framework in order to demonstrate how dots can be colored this way.

Getting ready

If you are not sure that you have car package installed, run the following code:

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

Time to get hands dirty.

How to do it...

Following steps demonstrates an alternative way of setting colors with geom_dotplot():

  1. Pick the colors to fill the dots and store them into objects:
> color1 <- 'deepskyblue1'
> color2 <- 'darkred'
  1. Create and reorder a vector with colors representing the 'Male' and 'Female' values coming from Salaries$sex:
> library(car)
> color_fill <- ifelse(Salaries$sex...
Visually different images
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R Data Visualization Recipes
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