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

Using point geometry to work as dots using ggvis, plotly and ggplot2


Dot plots can be seen as binned scatterplots. Once you realize it, you also realize that the point geometry coming from all three packages (ggplot2, plotly, and ggvis) can be used to draw sort of dot plots (not actual dot plots). ggplot2 can do this very easily. For ggvis and plotly, there are few key steps to follow:

  1. Coerce the categories into numbers, so x will behave as continuous.
  2. Add some little noise to x but not to y.
  3. Draw a scatterplot using x and y.
  4. Relabel the x-axis in order to reference categories.

This recipe is using runif() to create the noise and some tricks to re-label the ticks. Next section tells the requirements.

Getting ready...

Skip to the next section if you are sure about having car package installed. If you don't please run the following code:

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

Having the car package installed is still a requirement.

How it works...

Following steps are crafting alternative visualization...