Quarto Dashboards: Publish Data Dashboards From Code
What it is: Quarto Dashboards is a feature of Quarto, the free, open-source scientific and technical publishing system, that turns a single Markdown document with embedded R, Python, Julia, or Observable JS code into a laid-out dashboard - complete with a grid of cards, value boxes, and tabs. Instead of a separate dashboarding product, it's a rendering target: the same Quarto document format used for reports and articles can output a dashboard instead, using simple Markdown headers to define the layout.
What It Does Best
Layout from plain Markdown. Dashboard rows, columns, and tabs are defined with Markdown headers and a small YAML config, so building a grid layout doesn't require a separate UI or JavaScript layout code.
Multi-language flexibility. A single dashboard can pull in R, Python, Julia, or Observable JS code blocks, which suits teams that don't want to standardize on one language for every visualization.
Static or lightly interactive output. Dashboards render to a self-contained HTML file that can be hosted anywhere - no server or database connection required at view time unless you explicitly build one in with Observable JS or Shiny.
Key Features
Markdown-based layout: rows, columns, and tabs defined with header levels and simple YAML options
Value boxes and cards: built-in components for KPI tiles and chart/table containers
Multi-engine code execution: knitr (R), Jupyter (Python/Julia), and Observable JS in one document
Static site output: renders to portable, self-contained HTML for easy hosting
Shiny integration: can embed live, server-backed Shiny components for real interactivity when needed
Pricing: Completely Free and Open Source
Quarto, including the Dashboards feature, is entirely free and open source (developed by Posit under an open-source license) with no paid tier for the tool itself. Because dashboards render to static HTML by default, hosting can be as simple as any static file host or GitHub Pages at no additional cost - the only potential costs are if you choose to add live compute via Shiny/Posit Connect for a dynamic backend, which follows Posit's separate commercial hosting pricing.
Quarto and Quarto Dashboards: free, open source, no license fees
Static hosting: can be free (GitHub Pages, Netlify, S3, etc.) since output is self-contained HTML
Optional live backend: adding Shiny/Posit Connect for dynamic dashboards follows Posit's separate commercial pricing
When to Use It
✅ You already write analysis in Quarto, R Markdown, or Jupyter and want a dashboard output for free
✅ You want dashboards defined in plain text/Markdown that live in version control
✅ Your dashboard can be mostly static, refreshed on a schedule rather than needing live database queries
✅ Your team spans R, Python, and/or Julia and wants one dashboarding approach across all three
✅ You want zero-cost, portable hosting with no dedicated dashboard server
When NOT to Use It
❌ You need real-time, live-query dashboards with constant database polling (pair with Shiny or a BI tool instead)
❌ You want a drag-and-drop, no-code dashboard builder for non-technical users
❌ You need enterprise governance, row-level security, or user management built in
❌ You want a hosted SaaS product rather than something you render and deploy yourself
❌ Your team has no familiarity with Markdown, R, Python, or Julia
Common Use Cases
Scheduled reporting dashboards: rebuilt on a cadence (daily/weekly) and republished as static HTML
Research and academic dashboards: sharing analysis results as an interactive-feeling but static, citable artifact
Internal team dashboards: quick, free dashboards for teams already writing Quarto or R Markdown reports
Portfolio and public dashboards: free-to-host dashboards published via GitHub Pages or similar static hosts
Quarto Dashboards vs Alternatives
vs Streamlit: Streamlit builds live, server-backed Python apps that re-run on every interaction; Quarto Dashboards defaults to static, pre-rendered output, which is cheaper to host but less dynamic unless you add Shiny for live interactivity.
vs Evidence: both are free, code-first, Markdown-based approaches to dashboards; Evidence is SQL/warehouse-centric and JavaScript-rendered, while Quarto Dashboards is built around R/Python/Julia analysis code and Quarto's broader publishing system.
Rule of thumb: use Quarto Dashboards when your dashboard grows naturally out of R/Python/Julia analysis and can be mostly static; use Streamlit or Shiny when you need a live, constantly interactive app backed by a running server.
Unique Strengths
Zero cost, no vendor: completely free and open source, with no paid tier gating core dashboard functionality.
Multi-language in one dashboard: mixes R, Python, Julia, and Observable JS code blocks in a single document.
Grows out of existing reports: teams already using Quarto for reports can add a dashboard output with minimal new tooling.
Bottom line: Quarto Dashboards is the best free option for teams already writing R, Python, or Julia analysis who want a dashboard output without adopting a new paid tool or server. It's not built for live, constantly-refreshing data - for that, pair it with Shiny or reach for Streamlit/Dash instead.