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

Introduction


There are two general ways in which one variable plots are often used:

  • As chief car of a single variable investigation
  • To support multiple variables investigations

However, it certainly does not stop there. A wonderful usage of single variable plots is to make visual stands for hypothesis acceptance paradigmas as well to visualize Monte Carlo simulations results. In fact there are some stories about 1938 nobelist, the physicist Enrico Fermi. Before running experiments he would address Monte Carlo approximations to predict outcomes. Fermi reported the predictions to his friends; as the story goes, his predictions were remarkably accurate.

Think about it, Fermi was making Monte Carlo approximations before computacional power was a thing. This chapter is about to demonstrate how to run a simulation and make visual stands from it. Why not to predict and report your research results? Why not to run Morte Carlo simulations and summarize it graphically? It's very geek-cool and a well advised...