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

Plotting a high quality faceted bar graph


To create publish-quality faceted bar graphs is not that different from creating publish-quality regular bar plots. This recipe will follow the usual steps we had been following until now: grow axes, make labels account full names, and resize texts. Besides, this changes, recipe will also adjust facet labels and colors in general.

Another cool thing is to do whenever your x and fill aesthetics are matching is to set legends to replace the x axis title. This recipe will also demonstrate this.

How to do it...

We proceed with plotting a high quality faceted bar graph:

  1. Draw a basic faceted bar graph to work as the departure point:
> library(ggplot2)
> base <- ggplot(data = as.data.frame(Titanic), 
                aes(x = Survived)) + 
   geom_bar(aes(fill = Survived, weight = Freq), colour = 'black', width = 1) + 
   facet_grid(Sex ~ Age) + theme_bw()
> h1

The base object looks like the following illustration (Figure 7.10):

Figure 7.10 - Starting...