claude-r

name: claude-r description: Execute R code through RStudio via MCP. Use for R programming tasks, statistical analysis, data visualization, or setting up/troubleshooting the RStudio integration with Claude Code or OpenCode.

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:

Open RStudio
File → New Project → Existing Directory → select the project folder → Create Project
- If .Rproj already exists: File → Open Project → select your .Rproj file → Open
Run: library(ClaudeR)
Run: claudeAddin()
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

Get the MCP server script path:

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

Determine which client the user is using. If unclear, ask the user.
Ask the user which Python environment to use for running the MCP server. Options:
- uv (recommended — no pre-install needed)
- System Python (python3)
- Other (conda, venv, pyenv, etc.)
If the user chooses a custom environment, adapt the command/path accordingly. The MCP server script requires httpx and mcp packages.

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

With uv:

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

With system Python (install httpx and mcp first):

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

With custom environment (example: conda):

conda install httpx mcp
claude mcp add -s project r-studio -- \
  /path/to/conda/env/bin/python <script path>

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

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

With uv:

{
  "$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:

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

With custom environment (adapt command to the Python binary path):

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

Docs: https://opencode.ai/docs

Step 3: Start R Server

Relay the following instructions to the user exactly as written:

Open RStudio
File → New Project → Existing Directory → select the project folder → Create Project
- If .Rproj already exists: File → Open Project → select your .Rproj file → Open
Run library(ClaudeR) in the R console
Run claudeAddin() in the R console
Click "Start Server" in the Viewer pane

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".

If the server does not appear or shows "failed", ask the user to restart the client and try again. If the issue persists, see references/troubleshooting.md.

Architecture

Three-layer system:

AI Coding Assistant (Claude Code or OpenCode): Natural language interface
MCP Server: Python bridge (stdio ↔ HTTP)
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