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

Adjusting your hexagon plot


Previously, we learned how to give life to simple hexagon plots using ggplot2 and coerce them into  plotly. We took a bunch of default settings rather than customized ones. This recipe is going to show the readers some core parameters to tweak. We could, for example, resize the hexagons and/or choose different color scales and breaks.

Getting ready

In order to make the changes very clear, we're going to elaborate on the previous recipe, so the requirements from it still remain. There's no need to go back, as the following code will fulfill our needs if internet connection is available:

> if( !require(robustbase)){ install.packages('robustbase')}
> library(robustbase)
> data(NOxEmissions)

Let's roll.

How to do it...

Following code is changing hexagons size and colors:

  1. We start the same way as in the previous case, but we set up the binwidth parameter and call for an object from the scale_fill group:
> ggplot(data = NOxEmissions, aes( x = LNOx, y = sqrtWS))...