- 12 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Interactive Web-Based Data Visualization with R, plotly, and shiny
About This Book
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:
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- Convert static ggplot2 graphics to an interactive web-based form
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- Link, animate, and arrange multiple plots in standalone HTML from R
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- Embed, modify, and respond to plotly graphics in a shiny app
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- Learn best practices for visualizing continuous, discrete, and multivariate data
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- 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.
Frequently asked questions
Information
1
Introduction
1.1 Why interactive web graphics from R?
R
offers the opportunity to scale and automate tasks, document and track them, and reliably reproduce their output. That power, however, typically comes at the cost of increasing the amount of cognitive load involved relative to a GUI-based system.1 R
packages like the tidyverse have been incredibly successful due to their ability to limit cognitive load without removing the benefits of performing analysis via code. Moreover, the tidyverseās unifying principles of designing for humans, consistency, and composability make iteration within and between these stages seamless ā an important but often overlooked challenge in exploratory data analysis (EDA) (Tidyverse team, 2018).http://statgraphics.org/movies/
, documenting the use of interactive statistical graphics for tasks that otherwise wouldnāt have been easy or possible using numerical summaries and/or static graphics alone. Roughly speaking, these tasks tend to fall under three categories:DataDesk
https://datadescription.com/, GGobi
http://www.ggobi.org/, Mondrian
http://www.theusrus.de/Mondrian/, JMP
https://www.jmp.com, Tableau
https://www.tableau.com/. Although these GUI-based systems have nice properties, they donāt gel with a code-based workflow: any tasks you complete through a GUI likely canāt be replicated without human intervention. That means, if at any point, the data changes, and analysis outputs must be regenerated, you need to remember precisely how to reproduce the outcome, which isnāt necessarily easy, trustworthy, or economical. Moreover, GUI-based systems are typically āclosedā systems that donāt allow themselves to be easily customized, extended, or integrated with another system.displ
) versus miles per gallon (hwy
) using the mpg
dataset, you might wonder: āwhat are these cars with an unusually high value of hwy
given their displ?ā
. Rather than trying to write code to query those observations, it would be easier and more intuitive to draw an outline around the points to query the data behind them.Table of contents
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Dedication
- Table of Contents
- 1 Introduction
- I Creating views
- II Publishing views
- III Combining multiple views
- IV Linking multiple views
- V Event handling in JavaScript
- VI Various special topics
- Bibliography
- Index