# Quick Start with VizModules

## See the modules

Start with the hosted gallery to explore what each module can do: https://j-andrews7-VizModules.share.connect.posit.cloud/

You can also run the same gallery locally from this package:

    library(shiny)
    shiny::runApp(system.file("apps/module-gallery", package = "VizModules"))

## Drop a module into your app

All modules follow the same pattern: `*InputsUI()` for controls, `*OutputUI()` for the plot, and `*Server()` for the logic. Here is a minimal scatter plot example using the `dittoViz_scatterPlot` module:

    library(VizModules)

    ui <- fluidPage(
        sidebarLayout(
            sidebarPanel(
                dittoViz_scatterPlotInputsUI("cars",
                    mtcars,
                    defaults = list(
                        x.by = "wt",
                        y.by = "mpg",
                        color.by = "cyl"
                    )
                )
            ),
            mainPanel(dittoViz_scatterPlotOutputUI("cars"))
        )
    )

    server <- function(input, output, session) {
        dittoViz_scatterPlotServer("cars", data = reactive(mtcars))
    }

    shinyApp(ui, server)

## Set defaults and hide controls

- Defaults: Pass a named list to the `defaults` argument of `*InputsUI()` to pre-fill inputs. Names match the underlying plot function arguments (e.g., `defaults = list(fill.color = "steelblue")`).
- Reactive defaults: An entry may be a `reactive()` instead of a fixed value, so an input follows your app’s state (e.g. `defaults = list(color.by = reactive(input$colour_col))`). See `vignette("defaults-and-hiding", package = "VizModules")`.
- Hide inputs: Use `hide.inputs` in the server call to remove controls while still initializing their values. This is useful when your app sets certain parameters itself or wants to hide control of various elements while still passing their initial values.
- Hide tabs: Use `hide.tabs` to remove whole groups of controls (e.g., `"Plotly"` or `"Legend"` in `scatterPlot`).

    dittoViz_scatterPlotServer(
        "cars",
        data = reactive(mtcars),
        hide.inputs = c("split.by", "rows.use"),
        hide.tabs = c("Plotly")
    )

Hidden inputs and tabs still feed their values into the plot, so the module stays fully configured while exposing only what your users need.

## App factory with `createModuleApp()`

To enable simple, consistent testing of any module, we provide an app factory function that returns a full standalone app with data import, a filterable data table, and dataset switching for any module - `createModuleApp()`:

    library(VizModules)

    app <- createModuleApp(
        inputs_ui_fn = plotthis_BarPlotInputsUI,
        output_ui_fn = plotthis_BarPlotOutputUI,
        server_fn    = plotthis_BarPlotServer,
        data_list    = list("cars" = mtcars),
        title        = "My Bar Plot"
    )
    if (interactive()) runApp(app)

All built-in `*App()` convenience functions (e.g. `plotthis_BarPlotApp()`, `linePlotApp()`) are thin wrappers around `createModuleApp()` with sensible default data. You can also pass custom wrapper module functions to `createModuleApp()` for rapid prototyping.

## Export Summary Data

We provide `collect_source_data()` to assemble a compact record of the plotted data, stats, UI inputs, and the rendered plot, and `create_source_download_handler()` to turn that record into a downloadable `.zip`. `collect_source_data()` requires a reactive plotly plot; the output summary can be optionally enriched by both a stats reactive and a UI inputs reactive. `create_source_download_handler()` also accepts a named list of summaries (one per plot), which is how the Panel Builder bundles every plot on its canvas into a single download.

    if (interactive()) {
        library(shiny)
        library(plotly)

        ui <- fluidPage(
            plotlyOutput("plot"),
            downloadButton("download_summary", "Download Summary")
        )

        server <- function(input, output, session) {
            # A reactive plotly plot
            plot_reactive <- reactive({
                plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter", mode = "markers")
            })

            # Optional: a reactive returning a stats data.frame
            stats_reactive <- reactive({
                data.frame(
                    metric = c("mean_mpg", "sd_mpg"),
                    value  = c(mean(mtcars$mpg), sd(mtcars$mpg))
                )
            })

            # Optional: capture all UI inputs as a named list
            AllInputs <- reactive({
                reactiveValuesToList(input)
            })

            output$plot <- renderPlotly(plot_reactive())

            # Assemble the summary, then wire up the download handler.
            plot_summary_reactive <- reactive({
                collect_source_data(
                    plot_reactive   = plot_reactive,
                    stats_reactive  = stats_reactive,
                    inputs_reactive = AllInputs()
                )
            })

            output$download_summary <- create_source_download_handler(
                data_list     = plot_summary_reactive,
                filename_base = "my_plot_summary"
            )
        }

        shinyApp(ui, server)
    }

## Which plot parameters are exposed?

Modules wrap plotting functions from dittoViz, plotthis, and native plotting functions. To see which arguments are available in a module:

1. Open the module input help page, e.g., `?dittoViz_scatterPlotInputsUI` or `?plotthis_AreaPlotInputsUI`. The Details section notes which arguments from the underlying plot function are wired through and any that are intentionally omitted.
2. Cross-reference the base plot documentation (`?dittoViz::scatterPlot`, `?plotthis::AreaPlot`, etc.). Input names in `defaults` line up with those function arguments when they are supported.

If an argument is listed as missing or non-functional in the module docs, it has been intentionally hidden because it does not round-trip well in the interactive Plotly output.