Book Image

R Data Visualization Recipes

By : Vitor Bianchi Lanzetta
Book Image

R Data Visualization Recipes

By: Vitor 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 (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Crafting a simple tile plot with ggplot2


Tiles areessentially rectangles. Actually, the documentation of ggplot2 stresses that both geom_rect() and geom_tile()  "do the same thing but are parameterized differently". Imagine seeing a roof from the top and each color of tile stands for a different value of z, this is tile plots.

Function geom_tile() draws rectangles, often the filling colors stands for some continuous variables. The usual purpose they are used with is to represent 3D surfaces in the two dimensions plane. Using cars data set, let's see how ggplot2 can pull out a tile plot from it.

How to do it...

Use stat_bin_2d() to compute a third variable and output a tile plot:

> library(ggplot2)
> ggplot(data = cars, aes(x = speed, y = dist)) +
   stat_bin_2d(aes(fill = ..count..), 
               binwidth = c(5,15),
               colour = 'green',
               size = 1.05)

A tile plot can be seen at the following illustration (Figure 8.7):

Figure 8.7 - Tile plot draw using ggplot2

Now...