BoxPlotR

This is the repository for the Shiny application presented in "BoxPlotR: a web tool for generation of box plots" (Spitzer et al. 2014).

Installation and Run Options

1) Run Natively via Docker (Recommended Isolated Deployment)

Deploy the fully-configured modern version natively without installing R dependencies directly onto your host system:

# Build the Docker image
docker build -t boxplotr:latest .

# Run the container (maps the container server to port 3838)
docker run -d -p 3838:3838 boxplotr:latest

Access the application in your web browser at: http://localhost:3838

2) Running the Isolated Test Suite

The container comes equipped with testthat to run the project's automated test suite inside the same isolated sandbox:

docker run --rm boxplotr:latest Rscript -e "library(testthat); test_dir('/srv/shiny-server/tests')"

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3) Launch Natively from R and GitHub

Before running natively, ensure you have the latest versions of R and RStudio installed:

1. Launch R / RStudio Console.
2. Install the necessary packages:

   install.packages(c("shiny", "beeswarm", "vioplot", "beanplot", "RColorBrewer", "readxl", "sm", "testthat"))

3. Start the application directly:

   shiny::runGitHub("BoxPlotR.shiny", "jwildenhain")

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4) Install Natively on Shiny Server

To run BoxPlotR as a service on a dedicated Linux host (e.g. Ubuntu):

1. Install Shiny Server system dependencies:

   sudo apt-get update
   sudo apt-get install gdebi-core R-base

2. Download and install POSIT's Shiny Server from posit.co/download/shiny-server/.
3. Pull the BoxPlotR repository into your Shiny server apps directory (e.g., /srv/shiny-server/ or your custom SHINY_APP_HOME).
4. Install all required R packages system-wide:

   sudo R -e 'install.packages(c("shiny", "beeswarm", "vioplot", "beanplot", "RColorBrewer", "readxl", "sm"), repos="https://cloud.r-project.org/")'

5. Restart the server service:

   sudo systemctl restart shiny-server

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5) Model Context Protocol (MCP) Server Integration

This repository includes a native, stdio-compliant Model Context Protocol (MCP) server (boxplotr_mcp_server.py) written in Python with no external library dependencies. It allows large language models (LLMs) to call BoxPlotR's plotting engine directly via standard JSON-RPC tools!

Running the MCP Server Natively

Make sure you have Python 3 and Rscript installed on your machine:

./boxplotr_mcp_server.py

Integrating with Claude Desktop / LLM Clients

To configure the BoxPlotR MCP Server in Claude Desktop, add the following entry to your claude_desktop_config.json (usually located at ~/.config/Claude/claude_desktop_config.json on Linux/macOS or %APPDATA%/Claude/claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "boxplotr": {
      "command": "python3",
      "args": ["/home/jw/Source/BoxPlotR.shiny/boxplotr_mcp_server.py"]
    }
  }
}

Once integrated, any LLM configured with MCP can generate publication-grade box plots, violin plots, and bean plots automatically by processing user commands and passing data directly to BoxPlotR!