---
title: "Creating Custom Modules"
author: "NEST CoreDev"
output:
  rmarkdown::html_vignette:
    toc: true
vignette: >
  %\VignetteIndexEntry{Creating Custom Modules}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Introduction

The `teal` framework provides a large catalog of plug-in-ready analysis modules that can be incorporated into `teal` applications.
However, it is also possible to create your own modules using the `module` function, which leverages `shiny` modules.
Each custom teal module is built as a [`shiny` module](https://shiny.posit.co/r/articles/improve/modules/), combining `shiny`'s reactive capabilities with modularized UI and server logic to encapsulate functionality.
This design enables a structured and reusable approach to creating interactive components that integrate seamlessly within the teal ecosystem.

In this guide, we will use the simple histogram below as an example, and demonstrate how to convert this histogram function into a robust `teal` module step-by-step:

```{r setup, include=FALSE}
library(teal)
```

```r
my_plot <- hist(
  dataset[[vars]],
  las = 1,
  main = paste("Histogram of", vars),
  xlab = vars,
  col = "lightblue",
  border = "black"
)
```

This module will allow users to dynamically select datasets and variables to create histograms within a `teal` application.
We will cover best practices, including:

* Setting up dynamic inputs.
* Structuring server logic.
* Using the `teal_data` object to ensure reactivity and reproducibility.

## Understanding the Inputs and Requirements

When developing a custom `teal` module for visualizations, we will first identify the primary inputs that users will interact with:

* **Dataset Input** (`dataset`): Allows users to select which dataset to explore.
* **Variable Input** (`vars`): Allows users to choose a specific numeric variable from the chosen dataset, ensuring only appropriate columns are available for plotting.

These inputs are dynamically populated based on the available datasets and variables in the `teal_data` object, which we will cover later.

## Setting Up the `teal` Module UI

The UI function defines the controls and display area for the histogram.
For this module, we will use:

- **`selectInput` for Dataset**: Enables users to select a dataset from the list of available datasets.
- **`selectInput` for Variable**: Allows users to choose a numeric variable from the chosen dataset, dynamically filtering out any non-numeric variables from the choices.
- **`plotOutput` for Histogram**: Displays the histogram once both dataset and variable inputs are selected.
- **`verbatimTextOutput` for Code**: Automatically displays code that generated the plot based on the user input.

Here's the code for the `histogram_module_ui` function:

```{r module_ui}
library(teal)

# UI function for the custom histogram module
histogram_module_ui <- function(id) {
  ns <- shiny::NS(id)
  shiny::tagList(
    shiny::selectInput(
      ns("dataset"),
      "Select Dataset",
      choices = c("iris", "mtcars")
    ),
    shiny::selectInput(
      ns("variable"),
      "Select Variable",
      choices = c(names(iris), names(mtcars))
    ),
    shiny::plotOutput(ns("histogram_plot")),
    shiny::verbatimTextOutput(ns("plot_code")) # To display the reactive plot code
  )
}
```

## Setting Up the `teal` Module Server

The server function is where the main logic of a `teal` module is handled.
For our histogram module, the server function will handle user interactions and manage the reactive `teal_data` object, which allows the module to dynamically respond to user inputs.

### Passing the `data` Argument to the Server Function

To begin, it's essential to include the `data` argument in the server function definition.

This `data` argument holds the reactive `teal_data` object, which contains your datasets after applying any active filtering by the filter panel. By including `data`, we can ensure:

- The server function receives a reactive version of `teal_data`, allowing it to automatically respond to changes.
- The server can access the filtered datasets directly.

The correct function definition for the server function is:

```r
histogram_module_server <- function(id, data) {
  moduleServer(id, function(input, output, session) {
    # Server logic goes here
  })
}
```

If you need a refresher on the `teal_data` object, please visit the [teal.data package documentation](https://insightsengineering.github.io/teal.data/latest-tag/articles/teal-data.html).

### Understanding `teal_data` as a Reactive Object in Server Logic

When used in the server logic of a `teal` module, the `teal_data` object becomes a **reactive data container**.
This means that to access its contents, you need to call it like a function, using parentheses: `data()`.

This syntax triggers reactivity, ensuring that the data within `teal_data` stays up-to-date with any filters or changes applied elsewhere in the application.

> **Note**: The `teal_data` object behaves as a reactive data container only when used within the server logic. If accessed outside of the server, it will not be reactive.

### Using `names()` to Access Dataset Names in `teal_data` object

The `teal_data` object can contain multiple datasets. To retrieve the names of these datasets, use the `names()` function:

```r
names(data())
```

This will return a character vector of the dataset names contained in `teal_data`.
You can then use these names to dynamically populate input controls, like a dataset selection drop-down.

### Accessing Specific Datasets with Double Brackets (`[[ ]])`

To access an individual dataset from `teal_data`, use double brackets (`[[ ]]`) along with the dataset name. This allows you to extract the specific dataset as a data frame:

```r
data()[[input$dataset]]
```

Here, `input$dataset` represents the name of the dataset selected by the user. This syntax is highly flexible because it dynamically references whichever dataset the user has chosen. You can further subset or manipulate this extracted data frame as needed.

### Setting Up Server Logic Using `teal_data` and Dynamic Variable Injection

In this updated server function, we will perform the following:

1. **Create `new_data`** as a modified version of `data()` using `within()`, dynamically injecting `input$dataset` and `input$variable`.
2. **Render the Plot**: `renderPlot()` displays the plot by referencing the plot stored in the updated `teal_data` object, `new_data`.

Here's the code:

```{r module_server}
# Server function for the custom histogram module with injected variables in within()
histogram_module_server <- function(id, data) {
  moduleServer(id, function(input, output, session) {
    # Update dataset choices based on available datasets in teal_data
    shiny::observe({
      shiny::updateSelectInput(
        session,
        "dataset",
        choices = names(data())
      )
    })

    # Update variable choices based on selected dataset, only including numeric variables
    observeEvent(input$dataset, {
      req(input$dataset) # Ensure dataset is selected
      numeric_vars <- names(data()[[input$dataset]])[sapply(data()[[input$dataset]], is.numeric)]
      shiny::updateSelectInput(session, "variable", choices = numeric_vars)
    })

    # Create a reactive `teal_data` object with the histogram plot
    result <- reactive({
      req(input$dataset, input$variable) # Ensure both dataset and variable are selected

      # Create a new teal_data object with the histogram plot
      new_data <- within(
        data(),
        {
          my_plot <- hist(
            input_dataset[[input_vars]],
            las = 1,
            main = paste("Histogram of", input_vars),
            xlab = input_vars,
            col = "lightblue",
            border = "black"
          )
        },
        input_dataset = as.name(input$dataset), # Replace `input_dataset` with input$dataset
        input_vars = input$variable # Replace `input_vars` with input$variable
      )
      new_data
    })

    # Render the histogram from the updated teal_data object
    output$histogram_plot <- shiny::renderPlot({
      result()[["my_plot"]] # Access and render the plot stored in `new_data`
    })

    # Reactive expression to get the generated code for the plot
    output$plot_code <- shiny::renderText({
      teal.code::get_code(result()) # Retrieve and display the code for the updated `teal_data` object
    })
  })
}
```

Let's review what we've done so far:

1. **Dynamic Variable Injection with `within()`**:
    - `input_dataset = as.name(input$dataset)` passes the dataset name dynamically as `input_dataset`.
    - `input_vars = input$variable` passes the selected variable name directly as `input_vars`.
    - Inside `within()`, `my_plot` uses these injected variables to dynamically generate the histogram plot.

2. **Rendering the Plot**:
    - `output$histogram_plot` uses `renderPlot()` to display the plot stored in `new_data` by referencing `result()[["my_plot"]]`.

3. **Plot Code Display**:
    - The `output$plot_code` render function displays the dynamically generated code using `teal.code::get_code(result())`, allowing users to see the exact code used to generate the plot reactively.

## Creating the Custom `teal` Module Function

The `teal::module()` function allows you to encapsulate your UI and server logic into a `teal` module, making it reusable and ready to integrate into any `teal` application.

By setting `datanames = "all"`, you give the module access to all datasets specified in the `teal_data` object.
Datasets which names start with `.` won't be included (see [Hidden datasets](including-data-in-teal-applications.html#hidden-datasets) section).

```{r app_module}
# Custom histogram module creation
create_histogram_module <- function(label = "Histogram Module") {
  teal::module(
    label = label,
    ui = histogram_module_ui,
    server = histogram_module_server,
    datanames = "all"
  )
}
```

## Integrating the Custom `teal` Module into a `teal` App

With the custom `teal` module set up, it can now be integrated into a `teal` app.
We'll use `init()` from `teal` to specify the datasets and modules used in the app, then run the app to test the newly created module.

```{r app_init}
# Define datasets in `teal_data`
data_obj <- teal_data(
  iris = iris,
  mtcars = mtcars
)

# Initialize the teal app
app <- init(
  data = data_obj,
  modules = modules(create_histogram_module())
)

# Run the app
if (interactive()) {
  shiny::shinyApp(ui = app$ui, server = app$server)
}
```

**Congratulations! You just created a custom teal module and used it in a teal app!**

This setup provides a fully dynamic, user-controlled `teal` module that allows for interactive data exploration and code visibility, enhancing both usability and transparency.

```{r shinylive_iframe, echo = FALSE, out.width = '150%', out.extra = 'style = "position: relative; z-index:1"', eval = requireNamespace("roxy.shinylive", quietly = TRUE) && knitr::is_html_output() && identical(Sys.getenv("IN_PKGDOWN"), "true")}
code <- paste0(c(
  "interactive <- function() TRUE",
  knitr::knit_code$get("module_ui"),
  knitr::knit_code$get("module_server"),
  knitr::knit_code$get("app_module"),
  knitr::knit_code$get("app_init")
), collapse = "\n")

url <- roxy.shinylive::create_shinylive_url(code)
knitr::include_url(url, height = "800px")
```


## What's next?

Now that you've mastered the essentials of building and integrating modules in `teal`, you're ready to explore more advanced features.
`teal` offers a wide range of capabilities to enhance your module's functionality and user experience.

### Adding reporting to a module

Enhance your custom `teal` module with reporting features! Dive into [this vignette](adding-support-for-reporting.html) to see just how simple it is to add powerful reporting capabilities and elevate your module's impact.

### Using standard widgets in your custom module
The [`teal.widgets`](https://insightsengineering.github.io/teal.widgets/latest-tag/) package provides various widgets which can be leveraged to quickly create standard elements in your custom `teal` module.
