README.md

In rpodcast/shinylearning: Illustrate the evolution of a Shiny application from prototype to finished product

shinybrain

Ever since the shiny package emerged in the R community, we have seen many terrific applications shared throughout many industries and the overall R community. It is always inspiring when you see an amazing application, often wanting to learn more about the author's development approach to the final product. Imagine a way we could not only see that polished final version, but the actual stages of development from a simple prototype to a robust application ready for production? This is where {shinybrain} can help!

The goal of {shinybrain} is to provide a simple procedure to create multiple snapshots of an overall Shiny application built upon the excellent {brochure} package authored by Colin Fay, where you can bundle these snapshots as individual Shiny applications inside a single overall application.

TODO: Add animated GIF of shinysaurus example

Basic Usage

To get started, you can simply create a template application powered by {shinybrain}:

library(shinybrain)
add_shinybrain("my_brain_app")

If you are using the RStudio IDE, a new project is automatically created for the template application. Once you have the application structure created, it is easy to run the application in the default template. A {shinybrain} application contains the typical app.R which can easily be run inside your preferred development environment, such as RStudio or Visual Studio Code. In addition, you can run an application with the convenient brainpower() function:

brainpower("path/to/my_brain_app")

The default {shinybrain} template provides a single Shiny application (referred as a page) with placeholders of output, and an introduction page with rendered markdown. To show a next stage of an application's development progression, you can create a new page with the snapshot_shinybrain() function:

snapshot_shinybrain("path/to/my_brain_app")

There is no limit to how many snapshots your {shinybrain} application can have. The default behavior is to create a new snapshot from the last available snapshot, but you can create a snapshot from a specific one by setting use_current_snapshot = FALSE and giving the name of the desired snapshot with the snapshot_name parameter. If you want to simply view the contents of a single snapshot without having to run the entire set, you can use the brainsnap() function:

brain_snap("page1", path = "path/to/my_brain_app")

Deploying a {shinybrain} application is no different than a typical Shiny application, since the overall structure is self-contained with the root of the app directory containing app.R.

TODO: Add a directory tree listing of an example app folder

Feedback

The {shinylearning} package was directly inspired by the R community, and I welcome all feedback to make this package better. If you have a specific bug report or request for a new feature, please file a new issue. If you have a more general topic you would like to discuss, please create a new post on the Discussions board.

Contributions

TODO: Add a contributing guide markdown file

Installation

You can install the development version of shinybrain from GitHub with:

# install.packages("remotes")
remotes::install_github("rpodcast/shinybrain")

Code of Conduct

Please note that the shinybrain project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

rpodcast/shinylearning documentation built on April 4, 2022, 10:44 p.m.