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RDesk

RDesk hex logo

CRAN status Lifecycle: experimental R-CMD-check pkgdown MIT licenseWindows

Package R analyses into self-contained desktop applications with native webviews.

RDesk packages your R analysis into a standalone desktop application for Windows (with future support planned for macOS and Linux). Instead of running an HTTP server and opening a browser tab, RDesk uses a native launcher plus an embedded webview so the app runs offline with local IPC only.

Quick Start 

# Install from CRAN
install.packages("RDesk")

# Scaffold an interactive app
RDesk::rdesk_create_app("MyApp")

# Run it immediately
source("MyApp/app.R")

When you are ready to ship:

RDesk::build_app(
  app_dir         = "MyApp",
  app_name        = "MyApp",
  build_installer = TRUE
)

# Windows -> dist/MyApp-1.0.0-setup.exe (or dist/MyApp-1.0.0-windows.zip)

Windows produces a standalone installer or portable zip distribution (with macOS and Linux packaging in active development). No separate R installation or browser server is required on the target machine when using the bundled runtime.

Development version

devtools::install_github("Janakiraman-311/RDesk")

How RDesk Compares

There are different architectures for deploying and distributing R applications depending on your needs:

Deployment Model Typical Use Case Frontend Layer Backend Communication Distribution & Footprint
Hosted Shiny (Posit Connect, shinyapps.io) Multi-user web applications & dashboards Shiny reactive UI (pure R DSL) Centralized R server over WebSocket Web URL (no local install required)
Electron + Shiny (e.g. electricShine, DesktopDeployR) Desktop wrapper around existing Shiny apps Shiny reactive UI in embedded Chromium Local R server on loopback port Installer / bundle (~400–600 MB)
WebR / Shinylive Serverless in-browser execution Shiny reactive UI in browser R compiled to WebAssembly (client-side) Static web hosting (no native OS access)
RDesk Standalone local desktop tools Standard web frontend (HTML/CSS/JS) Local R process via native IPC pipes Standalone bundle or installer (~100–200 MB)

When to choose RDesk vs. Shiny

  • Choose Shiny if you want to write your UI entirely in R using reactive expressions, or if your application will primarily be deployed on a shared server or web portal.
  • Choose RDesk if you want to ship a self-contained desktop app with a smaller footprint (leveraging the operating system’s native webview rather than bundling Chromium), or need direct desktop integration (native menus, file pickers) without running a local web server. Note that RDesk uses an event-driven model (messages passed between HTML/JS and R handlers) rather than a reactive graph.

Core Features

  • Zero-port IPC - R and the UI communicate through native stdin/stdout pipes and platform webview bindings, without opening network ports.
  • Async by default - Non-blocking background work using mirai and callr with support for progress updates, loading states, and cancellation.
  • Version-safe runtime - build_app() bundles a matching R runtime so the shipped app and its packages stay aligned.
  • Modern web UI - Build the interface with plain HTML, CSS, and JavaScript while keeping the backend in R.
  • Automated scaffolding - rdesk_create_app() generates a working application template with handlers, structure, and theme support.
  • Native packaging - Produce platform-specific bundles and installers without adopting a browser-server deployment model.

Who It’s For

RDesk is built for R developers who need to package a local statistical or data tool for non-R users.

  • Pharma and clinical - distribute review or validation tools for offline execution.
  • Consulting - package analytical models into branded tools without exposing source code.
  • Internal teams - replace complex spreadsheet macros with structured R applications.
  • Restricted environments - ship local tools where listening ports are disallowed or heavily scrutinized.

Example Apps

Two apps ship with RDesk demonstrating different complexity levels.

CarsAnalyser - minimal dashboard

app_dir <- system.file("apps/mtcars_dashboard", package = "RDesk")
source(file.path(app_dir, "app.R"))

Data Intelligence Studio - full-featured data profiling tool

app_dir <- system.file("apps/data_studio", package = "RDesk")
source(file.path(app_dir, "app.R"))

Documentation

Full documentation is available at janakiraman-311.github.io/RDesk.

Guide What it covers
Getting Started From install.packages() to your first native app
Coming from Shiny Side-by-side mapping of common Shiny patterns to RDesk
Async Guide Background tasks, progress overlays, cancellation
Cookbook Practical desktop-app recipes
Why RDesk? Project background and architecture

License

MIT (c) Janakiraman G.