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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 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.
448 pages, Hardcover
Published January 21, 2020
3 people want to read
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November 22, 2024
I soon noticed that the Plotly book is not a typical tutorial. At first (Chapter 2), I thought that the book was poorly structured didactically. Meta-concepts are explained right at the beginning, instead of first providing simple examples to follow, so that, on the one hand, the purpose of the {plotly} package becomes clear and, on the other hand, initial successes are conveyed.
In fact, the book is more of a showcase: It not only shows what is possible with {plotly}, but above all, also what additional possibilities interactive graphics offer. However, it requires extensive knowledge of other program packages that can be used or integrated with {plotly} (packages for geo-computing such as {sf} or {leaflet}, packages for interactive web visualizations such as {htmlwidgets} , or integration with {shiny} for interactive web applications and much more).
The static text must be supplemented in two ways to understand the examples. On the one hand, the
videos on the book website (https://plotly-r.com/) show how the graphics can be used interactively. On the other hand, the links to interactive graphics allow for experimental learning. (For example, a window with the interactive graphic opens either by clicking on the static graphic or by clicking a separate link, which refers to a full-screen interactive graphic.) These additional (interactive) materials were a great help for me. On the other hand, the many interactive options would have been difficult to convey. However, I was unpleasantly struck by the fact that several links no longer work.
Although I have only read half of the book so far, it has already become clear to me that {plotly} opens up fantastic possibilities. The R package {plotly}—which also has a Python counterpart—offers many ways to highlight details from extensive data. It allows the data to be broken down visually using various experiments to understand the data better.
Regardless of the program's great application possibilities, the book should have taken a more systematic didactic approach. In its current form, it is difficult to digest for both beginners and moderately advanced R users (which I count myself among). That is why I am only giving the Plotly book three stars. (The {plotly} program would have received five stars, however!)
In fact, the book is more of a showcase: It not only shows what is possible with {plotly}, but above all, also what additional possibilities interactive graphics offer. However, it requires extensive knowledge of other program packages that can be used or integrated with {plotly} (packages for geo-computing such as {sf} or {leaflet}, packages for interactive web visualizations such as {htmlwidgets} , or integration with {shiny} for interactive web applications and much more).
The static text must be supplemented in two ways to understand the examples. On the one hand, the
videos on the book website (https://plotly-r.com/) show how the graphics can be used interactively. On the other hand, the links to interactive graphics allow for experimental learning. (For example, a window with the interactive graphic opens either by clicking on the static graphic or by clicking a separate link, which refers to a full-screen interactive graphic.) These additional (interactive) materials were a great help for me. On the other hand, the many interactive options would have been difficult to convey. However, I was unpleasantly struck by the fact that several links no longer work.
Although I have only read half of the book so far, it has already become clear to me that {plotly} opens up fantastic possibilities. The R package {plotly}—which also has a Python counterpart—offers many ways to highlight details from extensive data. It allows the data to be broken down visually using various experiments to understand the data better.
Regardless of the program's great application possibilities, the book should have taken a more systematic didactic approach. In its current form, it is difficult to digest for both beginners and moderately advanced R users (which I count myself among). That is why I am only giving the Plotly book three stars. (The {plotly} program would have received five stars, however!)
Displaying 1 of 1 review