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

2. ARIA Labels

3. High Contrast Support

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

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 Help