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https://github.com/rpodcast/shinybrain


https://github.com/rpodcast/shinybrain

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# shinybrain



Ever since the [shiny](https://shiny.rstudio.com) 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}`](https://github.com/ColinFay/brochure) package authored by [Colin Fay](https://github.com/ColinFay), 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}`:



```r

library(shinybrain)

add_shinybrain("my_brain_app")

```



If you are using the [RStudio](https://rstudio.com) 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:



```r

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:



```r

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:



```r

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](https://github.com/rpodcast/shinybrain/discussions) board.



## Contributions



TODO: Add a contributing guide markdown file

## Installation



You can install the development version of shinybrain from

[GitHub](https://github.com/) with:



``` r

# 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](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.