# Using MAIDR in Shiny Applications

#### JooYoung Seo

#### 2026-02-18

## Overview

MAIDR integrates seamlessly with Shiny applications, allowing you to create accessible, interactive data visualizations in web apps. This vignette shows how to use MAIDR’s Shiny functions to build accessible dashboards.

## Shiny Integration Functions

MAIDR provides two functions for Shiny integration:

1. `maidr_output()` - UI function to create output container
2. `render_maidr()` - Server function to render plots

These functions work together to display accessible MAIDR plots in your Shiny app.

## Basic Shiny App Example

Here’s a complete minimal Shiny app using MAIDR:

    library(shiny)
    library(ggplot2)
    library(maidr)

    # Define UI
    ui <- fluidPage(
      titlePanel("MAIDR in Shiny - Basic Example"),
      sidebarLayout(
        sidebarPanel(
          selectInput("plot_type",
            "Choose Plot Type:",
            choices = c("Bar Chart", "Histogram", "Scatter")
          ),
          br(),
          p("The plot on the right is fully accessible with keyboard navigation and screen reader support.")
        ),
        mainPanel(
          h3("Interactive Accessible Plot"),
          maidr_output("accessible_plot", height = "500px")
        )
      )
    )

    # Define Server
    server <- function(input, output) {
      output$accessible_plot <- render_maidr({
        if (input$plot_type == "Bar Chart") {
          # Bar chart
          bar_data <- data.frame(
            Category = c("A", "B", "C", "D"),
            Value = c(25, 40, 30, 35)
          )
          ggplot(bar_data, aes(x = Category, y = Value)) +
            geom_bar(stat = "identity", fill = "steelblue") +
            labs(title = "Sample Bar Chart") +
            theme_minimal()
        } else if (input$plot_type == "Histogram") {
          # Histogram
          hist_data <- data.frame(values = rnorm(500, mean = 100, sd = 15))
          ggplot(hist_data, aes(x = values)) +
            geom_histogram(bins = 30, fill = "coral", color = "black") +
            labs(title = "Sample Histogram", x = "Values", y = "Frequency") +
            theme_minimal()
        } else {
          # Scatter plot
          scatter_data <- data.frame(
            x = rnorm(50),
            y = rnorm(50),
            group = sample(c("Group A", "Group B"), 50, replace = TRUE)
          )
          ggplot(scatter_data, aes(x = x, y = y, color = group)) +
            geom_point(size = 3, alpha = 0.7) +
            labs(title = "Sample Scatter Plot") +
            theme_minimal()
        }
      })
    }

    # Run the app
    shinyApp(ui = ui, server = server)

## Interactive Dashboard Example

Here’s a more comprehensive dashboard with multiple plots:

    library(shiny)
    library(shinydashboard)
    library(ggplot2)
    library(maidr)

    # Define UI
    ui <- dashboardPage(
      dashboardHeader(title = "MAIDR Dashboard"),
      dashboardSidebar(
        sidebarMenu(
          menuItem("Bar Charts", tabName = "bar", icon = icon("chart-bar")),
          menuItem("Statistical Plots", tabName = "stats", icon = icon("chart-line")),
          menuItem("Scatter Plots", tabName = "scatter", icon = icon("circle"))
        )
      ),
      dashboardBody(
        tabItems(
          # Bar charts tab
          tabItem(
            tabName = "bar",
            fluidRow(
              box(
                title = "Product Sales",
                status = "primary",
                solidHeader = TRUE,
                width = 6,
                maidr_output("bar_plot", height = "400px")
              ),
              box(
                title = "Regional Comparison",
                status = "primary",
                solidHeader = TRUE,
                width = 6,
                maidr_output("dodged_bar_plot", height = "400px")
              )
            )
          ),

          # Statistical plots tab
          tabItem(
            tabName = "stats",
            fluidRow(
              box(
                title = "Distribution",
                status = "primary",
                solidHeader = TRUE,
                width = 6,
                maidr_output("histogram_plot", height = "400px")
              ),
              box(
                title = "Box Plot Comparison",
                status = "primary",
                solidHeader = TRUE,
                width = 6,
                maidr_output("boxplot_plot", height = "400px")
              )
            )
          ),

          # Scatter plots tab
          tabItem(
            tabName = "scatter",
            fluidRow(
              box(
                title = "Correlation Analysis",
                status = "primary",
                solidHeader = TRUE,
                width = 12,
                maidr_output("scatter_plot", height = "500px")
              )
            )
          )
        )
      )
    )

    # Define Server
    server <- function(input, output) {
      # Bar plot
      output$bar_plot <- render_maidr({
        sales_data <- data.frame(
          Product = c("A", "B", "C", "D", "E"),
          Sales = c(150, 230, 180, 290, 210)
        )

        ggplot(sales_data, aes(x = Product, y = Sales)) +
          geom_bar(stat = "identity", fill = "steelblue") +
          labs(title = "Product Sales", x = "Product", y = "Sales Amount") +
          theme_minimal()
      })

      # Dodged bar plot
      output$dodged_bar_plot <- render_maidr({
        regional_data <- data.frame(
          Region = rep(c("North", "South", "East"), each = 2),
          Quarter = rep(c("Q1", "Q2"), 3),
          Sales = c(100, 120, 150, 180, 90, 110)
        )

        ggplot(regional_data, aes(x = Region, y = Sales, fill = Quarter)) +
          geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
          labs(title = "Regional Sales Comparison") +
          theme_minimal()
      })

      # Histogram
      output$histogram_plot <- render_maidr({
        hist_data <- data.frame(values = rnorm(1000, mean = 100, sd = 15))

        ggplot(hist_data, aes(x = values)) +
          geom_histogram(bins = 30, fill = "coral", color = "black") +
          labs(title = "Score Distribution", x = "Score", y = "Frequency") +
          theme_minimal()
      })

      # Boxplot
      output$boxplot_plot <- render_maidr({
        ggplot(iris, aes(x = Species, y = Petal.Length)) +
          geom_boxplot(fill = "lightblue", alpha = 0.7) +
          labs(title = "Petal Length by Species") +
          theme_minimal()
      })

      # Scatter plot
      output$scatter_plot <- render_maidr({
        scatter_data <- data.frame(
          height = rnorm(100, 170, 10),
          weight = rnorm(100, 70, 8),
          gender = sample(c("Male", "Female"), 100, replace = TRUE)
        )

        ggplot(scatter_data, aes(x = height, y = weight, color = gender)) +
          geom_point(size = 3, alpha = 0.7) +
          labs(
            title = "Height vs Weight Analysis",
            x = "Height (cm)",
            y = "Weight (kg)"
          ) +
          theme_minimal()
      })
    }

    # Run the app
    shinyApp(ui = ui, server = server)

## Reactive Data Example

MAIDR works seamlessly with Shiny’s reactive programming:

    library(shiny)
    library(ggplot2)
    library(maidr)

    ui <- fluidPage(
      titlePanel("Reactive MAIDR Plot"),
      sidebarLayout(
        sidebarPanel(
          sliderInput("n_points",
            "Number of Points:",
            min = 10,
            max = 200,
            value = 50
          ),
          selectInput("distribution",
            "Distribution:",
            choices = c("Normal", "Uniform", "Exponential")
          )
        ),
        mainPanel(
          maidr_output("reactive_plot", height = "500px")
        )
      )
    )

    server <- function(input, output) {
      # Reactive data generation
      plot_data <- reactive({
        set.seed(123) # For reproducibility

        if (input$distribution == "Normal") {
          data.frame(x = rnorm(input$n_points))
        } else if (input$distribution == "Uniform") {
          data.frame(x = runif(input$n_points, -3, 3))
        } else {
          data.frame(x = rexp(input$n_points, rate = 1) - 1)
        }
      })

      # Render plot
      output$reactive_plot <- render_maidr({
        ggplot(plot_data(), aes(x = x)) +
          geom_histogram(bins = 30, fill = "skyblue", color = "black") +
          labs(
            title = paste(input$distribution, "Distribution"),
            subtitle = paste("n =", input$n_points),
            x = "Value",
            y = "Frequency"
          ) +
          theme_minimal()
      })
    }

    shinyApp(ui = ui, server = server)

## Accessibility Features in Shiny

When using MAIDR in Shiny, your plots automatically include:

### 1. Keyboard Navigation

- Users can Tab to reach the plot
- Arrow keys navigate through data points
- Screen readers announce values

### 2. ARIA Labels

- Plot containers have descriptive labels
- Data points are announced properly
- Navigation state is communicated

### 3. High Contrast Support

- Plots work with high contrast modes
- Focus indicators are visible
- Color choices remain accessible

## Best Practices

### 1. Set Appropriate Heights

    # Good: Explicit height
    maidr_output("plot1", height = "500px")

    # Also good: Responsive height
    maidr_output("plot2", height = "auto")

### 2. Add Descriptive Context

    fluidRow(
      box(
        title = "Sales Trend Analysis",
        status = "primary",
        maidr_output("sales_plot"),
        hr(),
        p("This plot shows monthly sales trends. Use arrow keys to navigate through months.")
      )
    )

### 3. Test with Keyboard Only

- Ensure all interactive elements are reachable
- Test Tab navigation order
- Verify plot navigation works

### 4. Provide Alternative Text

    output$my_plot <- render_maidr({
      p <- ggplot(data, aes(x, y)) +
        geom_bar(stat = "identity") +
        labs(
          title = "Clear, Descriptive Title",
          subtitle = "Additional context about the data",
          x = "X-axis label with units",
          y = "Y-axis label with units"
        )
      p
    })

## Performance Considerations

For large Shiny apps with many plots:

1. Use `isolate()` for non-essential reactivity
2. Cache expensive computations with `reactive()`
3. Consider plot complexity - simpler plots load faster
4. Test with real data sizes

## Complete Example Application

See the complete MAIDR dashboard example in:

    system.file("examples", "maidr_dashboard.R", package = "maidr")

This example demonstrates: - Multiple plot types - Sidebar navigation - Accessibility features - Responsive layout - Professional styling

## Deployment

When deploying Shiny apps with MAIDR:

1. Ensure all dependencies are installed
2. Test accessibility in production
3. Monitor performance
4. Provide user documentation

## Troubleshooting

### Common Issues

Plot doesn’t appear: - Check that ggplot object is returned from `render_maidr()` - Verify output ID matches between UI and server

Keyboard navigation not working: - Ensure plot container is visible - Check browser console for JavaScript errors

Screen reader issues: - Test with multiple screen readers - Verify ARIA labels are present

## Learn More

- Getting Started - MAIDR basics
- Plot Types - All supported visualizations
- Shiny documentation - shiny.posit.co
- Accessibility guidelines - WCAG 2.1

## Getting Help

- GitHub issues: maidr/issues
- Package documentation: `?maidr::maidr_output`
- Posit Community: forum.posit.co