Build Interactive HTML Dashboards from R • dashboardr

dashboardr lets you build interactive HTML dashboards from R using a simple, composable grammar. Think of it like building with Lego blocks:

No web development needed - just R code
Interactive charts powered by Highcharts
Beautiful themes from Bootswatch
Flexible layouts with tabs, pages, and navigation

Installation

# From R-Universe (recommended — includes pre-built binaries)
install.packages("dashboardr",
  repos = c("https://favstats.r-universe.dev", "https://cloud.r-project.org"))

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

Optional: Install gssr for tutorials

The tutorials and demos use the gssr package (General Social Survey data). Install it from r-universe:

install.packages('gssr', repos = c('https://kjhealy.r-universe.dev', 'https://cloud.r-project.org'))

# Also recommended: install gssrdoc for documentation
install.packages('gssrdoc', repos = c('https://kjhealy.r-universe.dev', 'https://cloud.r-project.org'))

The Three Layers

Just as ggplot2 builds plots from layers, dashboardr builds dashboards from three layers:

Layer	Purpose	Key Functions
Content	What to show (charts, text)	`create_content()`, `add_viz()`, `add_text()`
Page	Where content lives	`create_page()`, `add_content()`
Dashboard	Final output + config	`create_dashboard()`, `add_pages()`

Each layer flows into the next using pipes (%>%).

Quick Start

library(dashboardr)
library(dplyr)

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

# LAYER 1: Content - what to show
charts <- create_content(data = data, type = "bar") %>%
add_viz(x_var = "cyl_label", title = "Cylinders", tabgroup = "overview") %>%
  add_viz(x_var = "gear", title = "Gears", tabgroup = "overview")

# LAYER 2: Pages - where content lives
home <- create_page("Home", is_landing_page = TRUE) %>%
  add_text("# Car Dashboard", "", "Explore the mtcars dataset.")

analysis <- create_page("Analysis", data = data) %>%
  add_content(charts)

# LAYER 3: Dashboard - final output
create_dashboard(
  title = "Car Explorer",
  output_dir = "my_dashboard",
  theme = "flatly"
) %>%
  add_pages(home, analysis) %>%
  generate_dashboard(render = TRUE, open = "browser")

That’s it! A complete interactive dashboard.

Visualization Types

Function	Description	Use Case
`viz_bar()`	Bar charts	Category comparisons
`viz_histogram()`	Distributions	Age, income, scores
`viz_stackedbar()`	Stacked bars	Likert scales, compositions
`viz_stackedbars()`	Multiple stacked bars	Survey question batteries
`viz_timeline()`	Time series	Trends over time
`viz_heatmap()`	2D heatmap	Correlations, matrices
`viz_scatter()`	Scatter plots	Relationships between variables
`viz_treemap()`	Treemaps	Hierarchical proportions
`viz_map()`	Choropleth maps	Geographic data

Function Overview

dashboardr uses consistent naming so you always know what a function does:

Prefix	Purpose	Examples
`create_*`	Create containers - Start a new dashboard, page, or content collection	`create_dashboard()`, `create_page()`, `create_content()`
`add_*`	Add to containers - Insert visualizations, text, pages, or content	`add_viz()`, `add_text()`, `add_page()`, `add_content()`
`viz_*`	Build visualizations - Create individual charts (bar, histogram, timeline, etc.)	`viz_bar()`, `viz_histogram()`, `viz_timeline()`
`set_*`	Modify properties - Change settings like tab labels	`set_tabgroup_labels()`
`generate_*`	Produce output - Create Quarto files and render to HTML	`generate_dashboard()`
`theme_*`	Apply styling - Set visual themes	`theme_modern()`, `theme_clean()`
`combine_*`	Merge collections - Join multiple content collections	`combine_content()`, `combine_viz()`
`preview()`	Quick look - See content without generating files	`preview()`

The Typical Pattern

# 1. CREATE a container
create_content(data = my_data, type = "bar") %>%
  # 2. ADD elements to it
  add_viz(x_var = "category", title = "My Chart") %>%
  add_text("## Summary", "Key findings here.")

Documentation

Topic	Resource
Getting started	`vignette("getting-started")`
Content & visualizations	`vignette("content-collections")`
Pages	`vignette("pages")`
Dashboards	`vignette("dashboards")`
Advanced features	`vignette("advanced-features")`
Publishing	`vignette("publishing_dashboards")`

Community Gallery

Browse dashboards built with dashboardr by the community:

View the Gallery →

Built something with dashboardr? Submit your dashboard to the gallery and share it with the community!

Live Demos

See dashboardr in action:

Demo	Description
Showcase Dashboard	Advanced features: value boxes, multi-page layouts, themed charts
Tutorial Dashboard	Beginner-friendly introduction to core features
GSS Explorer	Sidebar dashboard with cascading filters
DigIQ Monitor	Real-world production dashboard for Dutch digital competence

Contributing

Contributions welcome! Please submit a Pull Request.

Share your dashboard — Add it to the Community Gallery by opening a Gallery submission.

LLM Support

dashboardr includes a built-in MCP server that gives AI coding assistants full access to dashboardr documentation, function help, and runnable examples. Works with Claude Code, Claude Desktop, VS Code Copilot, Cursor, and any MCP-compatible client.

Setup

First, install the optional dependencies:

install.packages(c("ellmer", "mcptools"))

Then configure your client:

Claude Code:

claude mcp add dashboardr -- Rscript -e "dashboardr::dashboardr_mcp_server()"

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "dashboardr": {
      "command": "Rscript",
      "args": ["-e", "dashboardr::dashboardr_mcp_server()"]
    }
  }
}

VS Code / Cursor (.vscode/mcp.json or Cursor equivalent):

{
  "mcp": {
    "servers": {
      "dashboardr": {
        "type": "stdio",
        "command": "Rscript",
        "args": ["-e", "dashboardr::dashboardr_mcp_server()"]
      }
    }
  }
}

Available Tools

Tool	Description
`dashboardr_guide`	Full package guide — architecture, API overview, quick start
`dashboardr_function_help`	Look up help for any exported function
`dashboardr_list_functions`	List functions by category (viz, content, input, layout, …)
`dashboardr_example`	Get runnable example code for common patterns
`dashboardr_viz_types`	Quick reference of all visualization types and parameters

License

MIT License - see LICENSE.md