Interactive Web-Based Data Visualization with R, plotly, and shiny

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.

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

Carson Sievert is the author and maintainer of the plotly R package, a recipient of the American Statistical Association’s 2017 John Chambers award, and Program Chair of the Section on Statistical Graphics. After receiving a PhD in statistics from Iowa State, Carson joined RStudio as a software engineer to work on software that bridges R and web technologies such as shiny, plotly, and rmarkdown.