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

Making faceted scatterplots


It's not hard to figure out how a faceted scatterplot can be drawn using the previous recipe just by skipping one single function (geom_smooth()). Nonetheless, this recipe is sailing outer seas while investigating heights (centimeters) and weights (kilos) coming from Australian athletes of different sports and categories.

In order to accomplish this, we're relying on the DAAG::ais data set. To avoid the excess of information, the analysis is narrowed, contemplating a few sports only, not all the ones present in this data frame. 

Getting ready

Look out for the DAAG package:

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

Once it's installed, the recipe can go on.

How to do it...

Let us start with making faceted scatterplot:

  1. Store the data frame in a new variable and narrow it down:
> data_sport <- DAAG::ais
> sports <- c('B_Ball','Field','Row','T_400m')
> data_sport <- data_sport[data_sport$sport %in% sports,]
  1. Draw a scatterplot as usual and sum the...