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The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more.

Key Features:

Convert static ggplot2 graphics to an interactive web-based form
Link, animate, and arrange multiple plots in standalone HTML from R
Embed, modify, and respond to plotly graphics in a shiny app
Learn best practices for visualizing continuous, discrete, and multivariate data
Learn numerous ways to visualize geo-spatial data
This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.



Table of Contents
Introduction

Why interactive web graphics from R?

What you will learn

What you won't learn (much of)

Web technologies

djs

ggplot

Graphical data analysis

Data visualization best practices

Prerequisites

Run code examples

Getting help and learning more

Acknowledgements

Colophon

I Creating views

Overview

Intro to plot_ly()

Intro to plotlyjs

Intro to ggplotly()

Scattered foundations

Markers

Alpha blending

Colors

Symbols

Stroke and span

Size

Dotplots & error bars

Lines

Linetypes

Segments

Density plots

Parallel Coordinates

Polygons

Ribbons

Maps

Integrated maps

Overview

Choropleths

Custom maps

Simple features (sf)

Cartograms

Bars & histograms

Multiple numeric distributions

Multiple discrete distributions

Boxplots

D frequencies

Rectangular binning in plotlyjs

Rectangular binning in R

Categorical axes

D charts

Markers

Paths

Lines

Axes

Surfaces

II Publishing views

Introduction

Saving and embedding HTML

Exporting static images

With code

From a browser

Sizing exports

Editing views for publishing

III Combining multiple views

Arranging views

Arranging plotly objects

Recursive subplots

Other approaches & applications

Arranging htmlwidgets

Flexdashboard

Bootstrap grid layout

CSS flexbox

Arranging many views

Animating views

Animation API

Animation support

IV Linking multiple views

Introduction

Client-side linking

Graphical queries

Highlight versus filter events

Linking animated views

Examples

Querying facetted charts

Statistical queries

Statistical queries with ggplotly()

Geo-spatial queries

Linking with other htmlwidgets

Generalized pairs plots

vi Contents

Querying diagnostic plots

Limitations

Server-side linking with shiny

Embedding plotly in shiny

Your first shiny app

Hiding and redrawing on resize

Leveraging plotly input events

Dragging events

D events

Edit events

Relayout vs restyle events

Scoping events

Event priority

Handling discrete axes

Accumulating and managing event data

Improving performance

Partial plotly updates

Partial update examples

Advanced applications

Drill-down

Cross-filter

A draggable brush

Discussion

V Event handling in JavaScript

Introduction

Working with JSON

Assignment, subsetting, and iteration

Mapping R to JSON

Adding custom event handlers

Supplying custom data

Leveraging web technologies from R

Web infrastructure

Modern JS & React

VI Various special topics

Is plotly free & secure?

Improving performance

Controlling tooltips

plot_ly() tooltips

ggplotly() tooltips

Styling

Control the modebar

Remove the entire modebar

Remove the plotly logo

Remove modebar buttons by name

Add custom modebar buttons

Control image downloads

Working with colors

Working with symbols and glyphs

Embedding images

Language support

LaTeX rendering

MathJax caveats

The data-plot-pipeline

Improving ggplotly()

Modifying layout

Modifying data

Leveraging statistical output

Translating custom ggplot geoms