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

Drawing publish-quality scatterplots


Drawing a publication quality scatterplot doesn't require stacking up all that we've seen until now. It's usually the other way round. Telling a good history means sticking with the right tools and not deploying unnecessary ones. Unnecessary usually is synonymous to mixed signals. The history you need to tell with your plot may be a short or long one, may request few or many devices. This decision is up to you, but there are general things to look for that improves almost any scatterplot. 

All graphics brought until now by this chapter may be considered good results if those were made only for exploratory purposes. However, on the other hand, they can be considered unfinished work when it comes to publish quality standards-there is still a pretty run to make.

Jeff Leek stresses that defaults in ggplot2 are pretty enough that might trick you into thinking the graph is production ready by using only defaults. Each context will request a different amount of...