What the Package Does (One Line, Title Case) • dashboardr

dashboardr makes it easy to create beautiful, interactive Quarto dashboards in R with minimal code. Perfect for survey data, analytics reports, and data storytelling.

Installation

# install.packages("pak")
pak::pak("favstats/dashboardr")

See some Example Dashboards

dashboardr includes two built-in demo dashboards that showcase its capabilities:

Tutorial Dashboard - A beginner-friendly dashboard demonstrating basic features
Showcase Dashboard - A comprehensive dashboard showcasing advanced features

Both dashboards use real data from the General Social Survey (GSS) and can be generated with a single function call!

The Grammar of Dashboards

Just as ggplot2 gave us a grammar of graphics, dashboardr has the ambitious goal of providing a grammar of dashboards. Instead of wrestling with configuration files and layout code, you compose dashboards from five intuitive building blocks:

Data + Visualizations + Layout + Navigation + Styling = Dashboard

library(dashboardr)

# Prepare Data
data <- mtcars %>%
  mutate(
    cyl_label = paste(cyl, "cylinders"),
    am_label = ifelse(am == 0, "Automatic", "Manual")
  )

# Create Visualizations + # Layout (e.g. via tabgroups)
viz <- create_viz(
  type = "histogram",
  x_var = "mpg"
) %>%
  add_viz(
    x_var = "hp",
    title = "Horsepower",
    tabgroup = "overview"
  )

# Create dashboard + Styling (tabset_theme) + Navigation (pages)
dashboard <- create_dashboard(
  title = "Car Analysis Dashboard",
  output_dir = "my_dashboard",
  tabset_theme = "modern"           
) %>%
  add_page(
    "Analysis",
    data = data,
    visualizations = viz,
    is_landing_page = TRUE
  )

# Generate
generate_dashboard(dashboard)

This creates a complete Quarto dashboard with interactive visualizations!

✨ Features

dashboardr works with pre-rendered static HTMLs which in many cases makes it faster to load than a Shiny application (provided a good internet connection). It also is more robust since no new graphs have to be calculated.

🎨 6 Built-in Tabset Themes - Modern, minimal, pills, classic, underline, segmented
📊 Rich Visualizations - Histograms, timelines, stacked bars, heatmaps, bar charts
🎯 Smart Defaults - Set parameters once, reuse everywhere
🔍 Advanced Filtering - Row-level filters for each visualization
📚 Multi-Dataset Support - Work with multiple datasets seamlessly
🎭 Nested Tabgroups - Create hierarchical tab structures
⚡ Vectorized Creation - Generate multiple visualizations efficiently
🎬 Loading Overlays - 4 animated overlay themes
🧭 Flexible Navigation - Navbar menus, dropdown menus, icons
📱 Responsive Design - Works on all screen sizes

Core Workflow: Data → Visualizations → Dashboard

Creating a dashboard follows a simple three-step pattern:

Step 1: Build Visualizations

Use create_viz() to set shared defaults, then add_viz() for each chart:

library(dashboardr)

# Set defaults once (type, colors, etc.)
my_viz <- create_viz(
  type = "histogram",           # All charts will be histograms
  color_palette = c("#3498DB"),  # All charts use this color
  bins = 30                      # All charts use 30 bins
) %>%
  # Add individual visualizations
  add_viz(
    x_var = "age",              # What to plot
    title = "Age Distribution",  # Chart title
    tabgroup = "overview"        # Which tab group
  ) %>%
  add_viz(
    x_var = "income",
    title = "Income Distribution",
    tabgroup = "overview",
    bins = 50  # Override: this one uses 50 bins
  )

# See what you built
print(my_viz)
#> 
#> ╔══════════════════════════════════════════════════════════════════════════
#> ║ 📊 VISUALIZATION COLLECTION
#> ╠══════════════════════════════════════════════════════════════════════════
#> ║ Total visualizations: 2
#> ║
#> ║ STRUCTURE:
#> ║ └─ 📁 overview
#> ║    ├─ 📉 HISTOGRAM: Age Distribution
#> ║    └─ 📉 HISTOGRAM: Income Distribution
#> ╚══════════════════════════════════════════════════════════════════════════

Key concepts:

create_viz(): Sets defaults that apply to all visualizations
add_viz(): Adds one visualization, can override any default
tabgroup: Organizes visualizations into tabs (e.g., “overview”, “demographics/age”)
print(): Shows the structure before generating

Step 2: Build Dashboard Structure

Use create_dashboard() to configure, then add_page() for each page:

survey_data <- cars

dashboard <- create_dashboard(
  title = "Employee Survey Dashboard",
  output_dir = "my_first_dashboard", # where to put the qmd files
  tabset_theme = "modern"  # Tab styling
) %>%
  # Landing page with text
  add_page(
    "Home",
    text = md_text(
      "# Welcome!",
      "",
      "This dashboard presents employee survey results.",
      "",
      "Navigate using the tabs above to explore different analyses."
    ),
    is_landing_page = TRUE
  ) %>%
  # Analysis page with data + visualizations
  add_page(
    "Analysis",
    data = survey_data,        # Your data
    visualizations = my_viz,    # The viz you created above
    icon = "ph:chart-line"      # Optional icon
  )
#> Dashboard project initialized at: my_first_dashboard
#> Detected package repo at: /Users/favstats/Dropbox/postdoc/dashboardr
#> Writing output outside the package at: /Users/favstats/Dropbox/postdoc/my_first_dashboard (set allow_inside_pkg = TRUE to disable relocation)

# See the dashboard structure
print(dashboard)
#> 
#> ╔══════════════════════════════════════════════════════════════════════════
#> ║ 🎨 DASHBOARD PROJECT
#> ╠══════════════════════════════════════════════════════════════════════════
#> ║ 📝 Title: Employee Survey Dashboard
#> Detected package repo at: /Users/favstats/Dropbox/postdoc/dashboardr
#> Writing output outside the package at: /Users/favstats/Dropbox/postdoc/my_first_dashboard (set allow_inside_pkg = TRUE to disable relocation)
#> ║ 📁 Output: /Users/favstats/Dropbox/postdoc/my_first_dashboard
#> ║
#> ║ ⚙️  FEATURES:
#> ║    • 🔍 Search
#> ║    • 🗂️  Tabs: modern
#> ║
#> ║ 📄 PAGES (2):
#> ║ ├─ 📄 Home [🏠 Landing]
#> ║ └─ 📄 Analysis [🎯 Icon, 💾 1 dataset]
#> ╚══════════════════════════════════════════════════════════════════════════

Key concepts:

create_dashboard(): Sets dashboard-level options (title, theme, output location)
add_page(): Adds a page to the navbar
md_text(): Creates markdown text blocks (headings, paragraphs, etc.)
data: Attaches your dataset to a page (available to all visualizations on that page)
is_landing_page: Makes this the default page users see first

Step 3: Generate HTML

Use generate_dashboard() to create the actual dashboard:

# Generate QMD files only (fast, for development)
generate_dashboard(dashboard, render = FALSE)

# Generate QMD files AND render to HTML (slower, for final output)
generate_dashboard(dashboard, render = TRUE)

# Generate + render + open in browser
generate_dashboard(dashboard, render = TRUE, open = "browser")

Key concepts:

render = FALSE: Only creates Quarto files (.qmd), doesn’t run Quarto
render = TRUE: Creates files AND renders to HTML (requires Quarto
open = "browser": Opens the dashboard in your browser after rendering

Complete Example

library(dashboardr)
library(dplyr)

# Your data
data <- mtcars %>%
  mutate(cyl_label = paste(cyl, "cylinders"))

# Step 1: Visualizations
viz <- create_viz(type = "histogram") %>%
  add_viz(x_var = "mpg", title = "MPG", tabgroup = "overview") %>%
  add_viz(x_var = "hp", title = "Horsepower", tabgroup = "overview")

# Step 2: Dashboard
dashboard <- create_dashboard(
  title = "Car Dashboard",
  output_dir = "my_dashboard"
) %>%
  add_page("Home", text = md_text("# Welcome!"), is_landing_page = TRUE) %>%
  add_page("Charts", data = data, visualizations = viz)

# Step 3: Generate
generate_dashboard(dashboard, render = TRUE, open = "browser")

That’s it! You now have a complete interactive dashboard.

Composing Visualizations with `+`

You can combine visualization collections using the + operator (like ggplot2):

# Create separate collections for different topics
demographics <- create_viz(type = "histogram") %>%
  add_viz(x_var = "age", title = "Age", tabgroup = "demographics")

feedback <- create_viz(type = "histogram") %>%
  add_viz(x_var = "satisfaction", title = "Satisfaction", tabgroup = "feedback")

# Combine them!
combined <- demographics + feedback

print(combined)
#> 📊 VISUALIZATION COLLECTION
#> Total visualizations: 2
#> STRUCTURE:
#> ├─ 📁 demographics
#> │  └─ 📉 HISTOGRAM: Age
#> └─ 📁 feedback
#>    └─ 📉 HISTOGRAM: Satisfaction

When to use +:

Organize complex dashboards into logical modules
Combine visualizations from different scripts/team members
Keep related visualizations grouped together in your code

Key Features

🎯 Smart Defaults

Set common parameters once and reuse them:

viz <- create_viz(
  type = "stackedbars",
  questions = paste0("Q", 1:5),
  stacked_type = "percent",
  horizontal = TRUE,
  color_palette = c("#E74C3C", "#95A5A6", "#27AE60")
) %>%
  add_viz(title = "Wave 1", filter = ~ wave == 1) %>%
  add_viz(title = "Wave 2", filter = ~ wave == 2) %>%
  add_viz(title = "Wave 3", filter = ~ wave == 3)

⚡ Vectorized Creation

Create multiple visualizations efficiently:

viz <- create_viz(
  type = "bar",
  horizontal = TRUE,
  bar_type = "percent"
) %>%
  add_vizzes(
    x_var = paste0("question_", 1:10),
    title = paste("Question", 1:10),
    .tabgroup_template = "survey/{title}"
  )
# Creates 10 visualizations with one call!

🎭 Nested Tabgroups

Create hierarchical structures:

viz <- create_viz(
  type = "stackedbar",
  x_var = "response",
  stack_var = "category"
) %>%
  # Wave 1 tabs
  add_viz(title = "Overall", tabgroup = "survey/wave1", filter = ~ wave == 1) %>%
  add_viz(title = "By Age", tabgroup = "survey/wave1/age", filter = ~ wave == 1) %>%
  add_viz(title = "By Gender", tabgroup = "survey/wave1/gender", filter = ~ wave == 1) %>%
  # Wave 2 tabs
  add_viz(title = "Overall", tabgroup = "survey/wave2", filter = ~ wave == 2) %>%
  add_viz(title = "By Age", tabgroup = "survey/wave2/age", filter = ~ wave == 2) %>%
  add_viz(title = "By Gender", tabgroup = "survey/wave2/gender", filter = ~ wave == 2) %>%
  # Custom labels
  set_tabgroup_labels(list(
    survey = "📊 Survey Results",
    wave1 = "Wave 1 (2024)",
    wave2 = "Wave 2 (2025)"
  ))

📚 Multi-Dataset Support

dashboard %>%
  add_page(
    "Analysis",
    data = list(
      sales = sales_data,
      customers = customer_data,
      products = product_data
    ),
    visualizations = viz
  )

🎬 Loading Overlays

dashboard %>%
  add_page(
    "Reports",
    data = large_dataset,
    visualizations = viz,
    overlay = TRUE,
    overlay_theme = "glass",
    overlay_text = "Loading reports..."
  )

Visualization Types

Function	Description	Use Case
`create_histogram()`	Distribution visualization	Age, income, scores
`create_bar()`	Grouped/clustered bars	Category comparisons
`create_stackedbar()`	Single stacked bar	Likert scales, compositions
`create_stackedbars()`	Multiple stacked bars	Multiple questions
`create_timeline()`	Time series	Trends over time
`create_heatmap()`	2D heatmap	Correlations, matrices

Themes

Choose from 6 built-in themes:

modern - Clean, centered tabs with subtle shadows
minimal - Simple, flat design
pills - Rounded pill-shaped tabs
classic - Traditional tabbed interface
underline - Underlined active tabs
segmented - Segmented control style

dashboard <- create_dashboard(
  title = "My Dashboard",
  output_dir = "output",
  tabset_theme = "modern",
  tabset_colors = list(
    active_bg = "#3498DB",
    active_text = "#FFFFFF"
  )
)

Documentation

Getting Started: vignette("getting-started")
Advanced Features: vignette("advanced-features")
Specific Visualizations:

Real-World Use Cases

🎓 Academic Research - Present study results interactively
📊 Survey Analysis - Visualize Likert-scale responses across waves
📈 Business Analytics - Track KPIs over time by department
💼 Consulting Reports - Create client-ready dashboards
📱 Data Journalism - Build interactive data stories

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE.md for details.

Citation

citation("dashboardr")
#> To cite package 'dashboardr' in publications use:
#> 
#>   Last F (2025). _dashboardr: What the Package Does (One Line, Title
#>   Case)_. R package version 0.0.0.9000,
#>   <https://favstats.github.io/dashboardr/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {dashboardr: What the Package Does (One Line, Title Case)},
#>     author = {First Last},
#>     year = {2025},
#>     note = {R package version 0.0.0.9000},
#>     url = {https://favstats.github.io/dashboardr/},
#>   }

dashboardr

Installation

See some Example Dashboards

The Grammar of Dashboards

✨ Features

Core Workflow: Data → Visualizations → Dashboard

Step 1: Build Visualizations

Step 2: Build Dashboard Structure

Step 3: Generate HTML

Complete Example

Composing Visualizations with +

Key Features

🎯 Smart Defaults

⚡ Vectorized Creation

🎭 Nested Tabgroups

📚 Multi-Dataset Support

🎬 Loading Overlays

Visualization Types

Themes

Documentation

Real-World Use Cases

Contributing

License

Citation

Composing Visualizations with `+`