# Build an App

While `querychat_app()` provides a quick way to start exploring data, building bespoke Shiny apps with querychat unlocks the full power of integrating natural language data exploration with custom visualizations, layouts, and interactivity. This guide shows you how to integrate querychat into your own Shiny applications and leverage its reactive data outputs to create rich, interactive dashboards.

querychat is a particularly good fit for Shiny apps that have:

1. A single data source (or a set of related tables that can be joined)
2. Multiple filters that let users slice and explore the data in different ways
3. Several visualizations and outputs that all depend on the same filtered data

In these apps, querychat can replace or augment your filtering UI by allowing users to describe what they want to see in natural language. Instead of building complex filter controls, users can simply ask questions like “show me customers from California who spent over $1000 last quarter” and querychat will generate the appropriate SQL query.

This is especially valuable when:

- Your data has many columns and building a UI for all possible filters would be overwhelming
- Users want to explore ad-hoc combinations of filters that you didn’t anticipate
- You want to make data exploration more accessible to users who aren’t comfortable with traditional filtering UIs

If you have an existing app with a reactive data frame that flows through multiple outputs, querychat can be a natural addition to provide an alternative way to filter that data.

## Starter template

Integrating querychat into a Shiny app requires just three steps:

1. Initialize a `QueryChat` instance with your data
2. Add the UI component (either `$sidebar()` or `$ui()`)
3. Use reactive values like `$df()`, `$sql()`, and `$title()` to build outputs that respond to user queries

Here’s a starter template demonstrating these steps:

    library(shiny)
    library(bslib)
    library(querychat)
    library(DT)
    library(palmerpenguins)

    # Step 1: Initialize QueryChat
    qc <- QueryChat$new(penguins)

    # Step 2: Add UI component
    ui <- page_sidebar(
      sidebar = qc$sidebar(),
      card(
        card_header("Data Table"),
        dataTableOutput("table")
      ),
      card(
        fill = FALSE,
        card_header("SQL Query"),
        verbatimTextOutput("sql")
      )
    )

    # Step 3: Use reactive values in server
    server <- function(input, output, session) {
      qc_vals <- qc$server()

      output$table <- renderDataTable({
        datatable(qc_vals$df(), fillContainer = TRUE)
      })

      output$sql <- renderText({
        qc_vals$sql() %||% "SELECT * FROM penguins"
      })
    }

    shinyApp(ui, server)

You’ll need to call the `qc$server()` method within your server function to set up querychat’s reactive behavior, and capture its return value to access reactive data.

## Deferred data sources

Some data sources, like database connections or reactive calculations, may need to be created within an active Shiny session. To help support this, `QueryChat` allows you to initialize without a data source and provide it later, like this:

    library(shiny)
    library(bslib)
    library(querychat)

    # Global scope - create QueryChat without data source
    qc <- QueryChat$new(NULL, "users")

    ui <- page_sidebar(
      sidebar = qc$sidebar(),
      card(dataTableOutput("table"))
    )

    server <- function(input, output, session) {
      # Server scope - create connection with session credentials
      conn <- get_user_connection(session)
      qc_vals <- qc$server(data_source = conn)

      output$table <- renderDataTable({
        qc_vals$df()
      })
    }

    shinyApp(ui, server)

If your chat client also depends on session-scoped credentials, you can defer that too by passing it to `qc$server(client = ...)` alongside the `data_source`.

This is also a useful pattern when using something like `{pool}` to efficiently manage a pool of database connections (which we strongly recommend for production apps).

## Reactives

There are three main reactive values provided by querychat for use in your app:

### Filtered data

The `$df()` method returns the current filtered and/or sorted data frame. This updates whenever the user prompts a filtering or sorting operation through the chat interface (see Data updating for details).

    qc_vals <- qc$server()

    output$table <- renderDataTable({
      qc_vals$df()  # Returns filtered/sorted data
    })

You can use `$df()` to power any output in your app - visualizations, summary statistics, data tables, and more. When a user asks to “show only Adelie penguins” or “sort by body mass”, `$df()` automatically updates, and any outputs that depend on it will re-render.

### SQL query

The `$sql()` method returns the current SQL query as a string. This is useful for displaying the query to users for transparency and reproducibility:

    qc_vals <- qc$server()

    output$current_query <- renderText({
      qc_vals$sql() %||% "SELECT * FROM penguins"
    })

You can also use `$sql()` as a setter to programmatically update the query (see Programmatic filtering below).

### Title

The `$title()` method returns a short description of the current filter, provided by the LLM when it generates a query. For example, if a user asks to “show Adelie penguins”, the title might be “Adelie penguins”.

    qc_vals <- qc$server()

    output$card_title <- renderText({
      qc_vals$title() %||% "All Data"
    })

Returns `NULL` when no filter is active. You can also use `$title()` as a setter to update the title programmatically.

## Custom UI

In the starter template above, we used the `$sidebar()` method for a simple sidebar layout. In some cases, you might want to place the chat UI somewhere else in your app layout, or just more fully customize what goes in the sidebar. The `$ui()` method is designed for this – it returns the chat component without additional layout wrappers.

For example, you might want to create some additional controls to reset filters alongside the chat UI:

    library(querychat)
    library(palmerpenguins)

    qc <- QueryChat$new(penguins)

    ui <- page_sidebar(
      sidebar = sidebar(
        qc$ui(),  # Chat component
        actionButton("reset", "Reset Filters", class = "w-100"),
        fillable = TRUE,
        width = 300
      ),
      # Main content here
    )

Customizing chat UIs

See `{shinychat}`’s docs to learn more about customizing the chat UI component returned by `qc$ui()`.

## Data views

Thanks to Shiny’s support for interactive visualizations with packages like plotly, it’s straightforward to create rich data views that depend on QueryChat data. Here’s an example of an app showing both the filtered data and a bar chart depending on that same data:

`app.R`

    library(shiny)
    library(bslib)
    library(querychat)
    library(DT)
    library(plotly)
    library(palmerpenguins)

    qc <- QueryChat$new(penguins, client = "claude/claude-sonnet-4-5")

    ui <- page_sidebar(
      sidebar = qc$sidebar(),
      card(
        card_header("Data Table"),
        dataTableOutput("table")
      ),
      card(
        card_header("Body Mass by Species"),
        plotlyOutput("mass_plot")
      )
    )

    server <- function(input, output, session) {
      qc_vals <- qc$server()

      output$table <- renderDataTable({
        datatable(qc_vals$df(), fillContainer = TRUE)
      })

      output$mass_plot <- renderPlotly({
        ggplot(qc_vals$df(), aes(x = body_mass_g, fill = species)) +
          geom_density(alpha = 0.4) +
          theme_minimal()
      })
    }

    shinyApp(ui, server)

A more useful, but slightly more involved example like the one below might incorporate other Shiny components like value boxes to summarize key statistics about the filtered data.

`app.R`

    library(shiny)
    library(bslib)
    library(DT)
    library(plotly)
    library(palmerpenguins)
    library(dplyr)
    library(bsicons)
    library(querychat)

    qc <- QueryChat$new(penguins)

    ui <- page_sidebar(
      title = "Palmer Penguins Analysis",
      class = "bslib-page-dashboard",
      sidebar = qc$sidebar(),
      layout_column_wrap(
        width = 1 / 3,
        fill = FALSE,
        value_box(
          title = "Total Penguins",
          value = textOutput("count"),
          showcase = bs_icon("piggy-bank"),
          theme = "primary"
        ),
        value_box(
          title = "Species Count",
          value = textOutput("species_count"),
          showcase = bs_icon("bookmark-star"),
          theme = "success"
        ),
        value_box(
          title = "Avg Body Mass",
          value = textOutput("avg_mass"),
          showcase = bs_icon("speedometer"),
          theme = "info"
        )
      ),
      layout_columns(
        card(
          card_header(textOutput("table_title")),
          DT::dataTableOutput("data_table")
        ),
        card(
          card_header("Species Distribution"),
          plotlyOutput("species_plot")
        )
      ),
      layout_columns(
        card(
          card_header("Bill Length Distribution"),
          plotlyOutput("bill_length_dist")
        ),
        card(
          card_header("Body Mass by Species"),
          plotlyOutput("mass_by_species")
        )
      )
    )

    server <- function(input, output, session) {
      qc_vals <- qc$server()

      output$count <- renderText({
        nrow(qc_vals$df())
      })

      output$species_count <- renderText({
        length(unique(qc_vals$df()$species))
      })

      output$avg_mass <- renderText({
        avg <- mean(qc_vals$df()$body_mass_g, na.rm = TRUE)
        paste0(round(avg, 0), "g")
      })

      output$table_title <- renderText({
        qc_vals$title() %||% "All Penguins"
      })

      output$data_table <- DT::renderDataTable({
        DT::datatable(
          qc_vals$df(),
          fillContainer = TRUE,
          options = list(
            scrollX = TRUE,
            pageLength = 10,
            dom = "ti"
          )
        )
      })

      output$species_plot <- renderPlotly({
        plot_ly(
          count(qc_vals$df(), species),
          x = ~species,
          y = ~n,
          type = "bar",
          marker = list(color = c("#1f77b4", "#ff7f0e", "#2ca02c"))
        )
      })

      output$bill_length_dist <- renderPlotly({
        plot_ly(
          qc_vals$df(),
          x = ~bill_length_mm,
          type = "histogram",
          nbinsx = 30,
          marker = list(color = "#1f77b4", opacity = 0.7)
        )
      })

      output$mass_by_species <- renderPlotly({
        plot_ly(
          qc_vals$df(),
          x = ~species,
          y = ~body_mass_g,
          color = ~sex,
          type = "box",
          colors = c("#1f77b4", "#ff7f0e")
        )
      })
    }

    shinyApp(ui = ui, server = server)

## Programmatic updates

querychat’s reactive state can be updated programmatically. For example, you might want to add a “Reset Filters” button that clears any active filters and returns the data table to its original state. You can do this by setting both the SQL query and title to their default values. This way you don’t have to rely on both the user and LLM to send the right prompt.

    ui <- page_sidebar(
      sidebar = sidebar(
        qc$ui(),
        hr(),
        actionButton("reset", "Reset Filters")
      ),
      # Main content
      card(dataTableOutput("table"))
    )

    server <- function(input, output, session) {
      qc_vals <- qc$server()

      output$table <- renderDataTable({
        qc_vals$df()
      })

      observeEvent(input$reset, {
        qc_vals$sql("")
        qc_vals$title(NULL)
      })
    }

    shinyApp(ui, server)

This is equivalent to the user asking the LLM to “reset” or “show all data”.

## Multiple tables

Currently, you have two options for exploring multiple tables in querychat:

1. Join the tables into a single table before passing to querychat
2. Use multiple querychat instances in the same app

The first option makes it possible to chat with multiple tables inside a single chat interface, whereas the second option requires a separate chat interface for each table.

### Multiple filtered tables

We do intend on supporting multiple filtered tables in a future release – if you’re interested in this feature, please upvote the relevant issue

`app.R`

    library(shiny)
    library(bslib)
    library(palmerpenguins)
    library(titanic)
    library(querychat)

    qc_penguins <- QueryChat$new(penguins)
    qc_titanic <- QueryChat$new(titanic_train)

    ui <- page_navbar(
      title = "Multiple Datasets",
      sidebar = sidebar(
        id = "sidebar",
        conditionalPanel(
          "input.navbar == 'Penguins'",
          qc_penguins$ui()
        ),
        conditionalPanel(
          "input.navbar == 'Titanic'",
          qc_titanic$ui()
        )
      ),
      nav_panel(
        "Penguins",
        card(dataTableOutput("penguins_table"))
      ),
      nav_panel(
        "Titanic",
        card(dataTableOutput("titanic_table"))
      ),
      id = "navbar"
    )

    server <- function(input, output, session) {
      qc_penguins_vals <- qc_penguins$server()
      qc_titanic_vals <- qc_titanic$server()

      output$penguins_table <- renderDataTable({
        qc_penguins_vals$df()
      })

      output$titanic_table <- renderDataTable({
        qc_titanic_vals$df()
      })
    }

    shinyApp(ui, server)

## See also

- Greet users - Create welcoming onboarding experiences
- Provide context - Help the LLM understand your data better
- Tools - Understand what querychat can do under the hood