run-r 0.2.4

A lightweight Python plugin to execute R scripts and retrieve workspace variables

run-r

A lightweight partially vibecoded Python module to execute R scripts (with optional inputs) and retrieve all workspace variables. I wanted to avoid rpy2 as installing it on my machine led to a compatibility/dependency nightmare.

Features

• Simple Interface: Execute R scripts with a single function call
• Workspace Extraction: Automatically captures all variables from the R global environment
• Bidirectional Data Transfer: Pass Python data to R and get results back
• Type Handling: Handles various R data types (vectors, data frames, matrices, lists, S4 objects)
• No Heavy Dependencies: Uses only standard library - no need for rpy2
• Cross-Platform: Works on Windows, Linux, and macOS
• Statistical Models: Full support for complex R objects like lme4 mixed-effects models

Requirements

• Python 3.7+
• R installation (the plugin will auto-detect R on Windows)
• R package jsonlite (will be automatically installed if missing)

Installation

pip install run-r

Quick Start

Basic Usage

from run_r import run_r_script

# Run an R script and get all workspace variables
variables = run_r_script("my_script.R")

# Access variables
print(variables['my_variable'])
print(variables['my_dataframe'])

Pass Data from Python to R

import pandas as pd
from run_r import run_r_script

# Create a pandas DataFrame
df = pd.DataFrame({
    'x': [1, 2, 3, 4, 5],
    'y': [2, 4, 6, 8, 10]
})

# Pass it to R along with other parameters
result = run_r_script(
    "analysis.R",
    input_data={
        "data": df,
        "threshold": 5,
        "method": "linear"
    }
)

print(result['model_summary'])

Work with Statistical Models

import numpy as np
import pandas as pd
from run_r import run_r_script

# Generate sample data
df = pd.DataFrame({
    'subject_id': np.repeat(range(20), 30),
    'predictor': np.random.randn(600),
    'response': np.random.binomial(1, 0.5, 600)
})

# Fit a mixed-effects model in R
result = run_r_script(
    "fit_glmer.R",
    input_data={"study_data": df}
)

# Access model results
print(result['model']['fixed_effects'])
print(result['model']['fit_stats'])

Supported Data Types

Python to R

• Basic types: int, float, str, bool, list, dict
• NumPy arrays: Converted to R vectors/matrices
• Pandas DataFrames: Converted to R data.frames
• Pandas Series: Converted to R vectors

R to Python

• Scalars: Numbers, strings, booleans
• Vectors: Numeric, character, logical vectors
• Data Frames: Converted to dictionaries of lists
• Lists: Nested R lists
• Matrices: Stored with dimension information
• S4 Objects: Complex objects like lme4 models with full metadata
• Functions/Environments: Captured as string representations

Command Line Usage

run-r path/to/script.R

Or using Python:

python -m run_r path/to/script.R

Advanced Usage

Custom R Executable

from run_r import RScriptRunner

runner = RScriptRunner(r_executable="/custom/path/to/Rscript")
variables = runner.run_script("analysis.R")

Control Output Verbosity

# Suppress R output
variables = run_r_script("script.R", verbose=False)

# Enable debug mode (keeps temporary files)
runner = RScriptRunner()
variables = runner.run_script("script.R", debug=True)

Reusable Runner Instance

from run_r import RScriptRunner

# Create runner once
runner = RScriptRunner()

# Run multiple scripts
result1 = runner.run_script("script1.R")
result2 = runner.run_script("script2.R", input_data={"x": 10})
result3 = runner.run_script("script3.R", verbose=False)

Examples

Example 1: Basic R Script

my_script.R:

x <- 1:10
y <- x^2
result <- sum(y)

Python:

from run_r import run_r_script

vars = run_r_script("my_script.R")
print(vars['x'])      # [1, 2, 3, ..., 10]
print(vars['y'])      # [1, 4, 9, ..., 100]
print(vars['result']) # 385

Example 2: Pass DataFrame to R

analysis.R:

# Input data is automatically loaded
model <- lm(y ~ x, data = df)
predictions <- predict(model)
r_squared <- summary(model)$r.squared

Python:

import pandas as pd
from run_r import run_r_script

df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [2, 4, 6, 8]})
result = run_r_script("analysis.R", input_data={"df": df})

print(result['predictions'])
print(result['r_squared'])

Example 3: Mixed-Effects Model

fit_model.R:

library(lme4)

# Fit generalized linear mixed-effects model
model <- glmer(
    response ~ predictor + (1|subject_id),
    data = study_data,
    family = binomial()
)

Python:

import pandas as pd
import numpy as np
from run_r import run_r_script

# Generate sample data
np.random.seed(42)
df = pd.DataFrame({
    'subject_id': np.repeat(range(20), 30),
    'predictor': np.random.randn(600),
    'response': np.random.binomial(1, 0.5, 600)
})

# Fit model in R
result = run_r_script("fit_model.R", input_data={"study_data": df})

# Access extracted model components
model = result['model']
print("Fixed effects:")
print(model['fixed_effects'])
print("\nModel fit:")
print(f"AIC: {model['fit_stats']['AIC']}")
print(f"BIC: {model['fit_stats']['BIC']}")

How It Works

1. Script Execution: The plugin sources your R script in a fresh R environment
2. Data Transfer: Input data is serialized to JSON and loaded into R
3. Variable Capture: After execution, all objects are collected from the global environment
4. Serialization: Variables are converted to JSON-compatible format using R's jsonlite
5. Return: Variables are returned as a Python dictionary

Error Handling

try:
    variables = run_r_script("script.R")
except FileNotFoundError as e:
    print(f"Script not found: {e}")
except RuntimeError as e:
    print(f"R execution error: {e}")

Design Philosophy

This plugin uses a subprocess-based approach rather than rpy2 for several reasons:

1. Lightweight: No heavy dependencies or compiled extensions
2. Isolation: Each script runs in a clean R process
3. Simplicity: Easy to understand and modify
4. Portability: Works anywhere R is installed
5. Auto-detection: Automatically finds R on Windows in common installation locations

Platform Notes

Windows

• Automatically searches for R in common Windows locations
• jsonlite package auto-installed on first run if missing

Linux/macOS

• Ensure Rscript is in your PATH
• Install R from your package manager or from https://cran.r-project.org/

Building and Publishing

To build the package:

cd package
python -m build

To install locally for testing:

pip install -e .

To upload to PyPI:

python -m twine upload dist/*

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Links

• GitHub: https://github.com/bsgarcia/run-r
• PyPI: https://pypi.org/project/run-r/
• Issues: https://github.com/bsgarcia/run-r/issues