Claude-R - Execute R through RStudio

Connect an AI coding assistant (Claude Code or OpenCode) to RStudio, enabling agentic R programming. RStudio acts as the execution engine; the assistant generates and runs the code.

OpenCode discovers this skill from .claude/skills/ just like Claude Code (docs). The MCP config is written to .mcp.json (Claude Code) or opencode.jsonc (OpenCode).

Note: All commands in this document assume the working directory is the project root.

Quick Start

Prerequisites Check

Verify RStudio server is running:

uv run .claude/skills/claude-r/scripts/check_r_connection.py
# or with system Python (if httpx installed)
python .claude/skills/claude-r/scripts/check_r_connection.py

If not running:

1. •Open RStudio
2. •File -> New Project -> Existing Directory -> select the project folder -> Create Project (If .Rproj already exists, instead: File -> Open Project -> select your .Rproj file -> Open)
3. •Run: library(ClaudeR)
4. •Run: claudeAddin()
5. •Click "Start Server" in Viewer pane

Basic Usage

Execute R code:

x <- 1:10
mean(x)

Generate plots:

library(ggplot2)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3) +
  theme_minimal()

Available MCP Tools

execute_r

Execute R code and return text output.

execute_r_with_plot

Execute R code that generates graphics. Returns PNG image.

get_r_info

Query R environment:

• •what: "packages" - Installed packages
• •what: "variables" - Workspace objects
• •what: "version" - R version
• •what: "all" - Complete summary

get_active_document

Retrieve currently open file in RStudio.

modify_code_section

Modify code in active RStudio document by pattern or line range.

Setup and Installation

Step 1: Install R and Dependencies

bash .claude/skills/claude-r/scripts/setup_claude-r.sh

This installs:

• •R via Homebrew
• •System dependencies (harfbuzz, libtiff, libgit2, etc.)
• •R packages (renv, devtools, ClaudeR)

Step 2: Configure MCP Client

1. •

   Get the MCP server script path:

   bash

   Rscript -e 'cat(system.file("scripts/persistent_r_mcp.py", package="ClaudeR"))'
   

2. •

   Determine which client the user is using. If unclear, ask the user.

3. •

   Register the MCP server:

   Claude Code (claude mcp add writes to .mcp.json):

   With uv (recommended, no pre-install needed):

   bash

   claude mcp add -s project r-studio -- \
     uv run --with httpx --with mcp python <script path>
   

   With system Python (install httpx and mcp first):

   bash

   python3 -m pip install --user httpx mcp
   claude mcp add -s project r-studio -- \
     python3 <script path>
   

   Docs: https://code.claude.com/docs/en/mcp

   OpenCode (write opencode.jsonc in the project root):

   With uv (recommended):

   jsonc

   {
     "$schema": "https://opencode.ai/config.json",
     "mcp": {
       "r-studio": {
         "type": "local",
         "command": [
           "uv", "run",
           "--with", "httpx",
           "--with", "mcp",
           "python", "<script path>"
         ],
         "enabled": true
       }
     }
   }
   

   With system Python:

   jsonc

   {
     "$schema": "https://opencode.ai/config.json",
     "mcp": {
       "r-studio": {
         "type": "local",
         "command": ["python3", "<script path>"],
         "enabled": true
       }
     }
   }
   

   Docs: https://opencode.ai/docs/mcp-servers/

Step 3: Start R Server

1. •Open RStudio
2. •File -> New Project -> Existing Directory -> select the project folder -> Create Project (If .Rproj already exists, instead: File -> Open Project -> select your .Rproj file -> Open)
3. •library(ClaudeR)
4. •claudeAddin()
5. •Click "Start Server"

Step 4: Verify

Claude Code: Run /mcp in Claude Code, or claude mcp list from the terminal. "r-studio" should show "connected".

OpenCode: Run /mcps in OpenCode, or opencode mcp list from the terminal. "r-studio" should show "connected".

Architecture

Three-layer system:

1. •AI Coding Assistant (Claude Code or OpenCode): Natural language interface
2. •MCP Server: Python bridge (stdio ↔ HTTP)
3. •ClaudeR HTTP Server: R execution in RStudio (port 8787)

RStudio acts passively. Users interact with the assistant, which generates and executes R code. RStudio's Environment, Plots, and Console panes show results.

See references/architecture.md for details.

Troubleshooting

If R code execution fails, check connection:

uv run .claude/skills/claude-r/scripts/check_r_connection.py

Common issues:

• •RStudio server not running
• •MCP configuration incorrect
• •Python dependencies missing

See references/troubleshooting.md for complete guide.

Key Features

• •State persistence: R session persists across executions
• •RStudio integration: View variables, plots, and console in RStudio
• •Agentic: Generate and execute R code through conversation

Limitations

• •Single-threaded: Sequential execution only
• •Local only: Server binds to localhost
• •RStudio required: Must be running with server active
• •No streaming: Results return after complete execution

Resources

• •Troubleshooting Guide - Connection issues, setup errors
• •Architecture - System design and data flow
• •Setup Script - Automated installation
• •Connection Checker - Verify server status
• •ClaudeR GitHub - Original project

MCP Documentation

• •Claude Code MCP docs
• •OpenCode MCP docs