rpy-bridge

rpy-bridge is a Python-controlled R execution orchestrator (not a thin rpy2 wrapper). It delivers deterministic, headless-safe R startup; project-root inference; out-of-tree renv activation; isolated script namespaces; and robust Python↔R conversions with dtype/NA normalization. Use it when you need reproducible R execution from Python in production pipelines and CI.

Latest release: rpy-bridge on PyPI

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What this is (and is not)

rpy-bridge is not a thin rpy2 wrapper. Key differences:

• Infers R project roots via markers (.git, .Rproj, renv.lock, DESCRIPTION, .here)
• Activates renv even when it lives outside the calling directory
• Executes from the inferred project root so relative paths behave as R expects
• Runs headless by default (no GUI probing), isolates scripts from globalenv()
• Normalizes return values for Python (NAs, dtypes, data.frames) and offers comparison helpers

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Quickstart

Call a package function (no scripts):

from rpy_bridge import RFunctionCaller

rfc = RFunctionCaller()
samples = rfc.call("stats::rnorm", 5, mean=0, sd=1)
median_val = rfc.call("stats::median", samples)

Call a function from a local script with renv (out-of-tree allowed):

from pathlib import Path
from rpy_bridge import RFunctionCaller

project_dir = Path("/path/to/your-r-project")
script = project_dir / "scripts" / "example.R"

rfc = RFunctionCaller(path_to_renv=project_dir, scripts=script)
result = rfc.call("some_function", 42, named_arg="value")

Core capabilities

1. R execution orchestration

• Embeds R via rpy2 with deterministic startup behavior
• Disables interactive and GUI-dependent hooks for headless execution
• Loads R scripts into isolated namespaces (not globalenv())

2. Project root inference and path stability

• Infers R project roots using markers such as: .git, .Rproj, renv.lock, DESCRIPTION, .here
• Executes R code from the inferred project root regardless of Python CWD
• Preserves relative-path behavior expected by R scripts
• Supports R code using here::here() or project-local data

3. Out-of-tree renv activation

• Activates renv projects located outside the calling Python directory
• Sources .Rprofile and .Renviron to reproduce R startup semantics
• Does not require R scripts and renv to live in the same directory

4. Python ↔ R data conversion

• Converts Python scalars, lists, dicts, and pandas objects into R equivalents
• Converts R vectors, lists, and data.frames back into Python-native types
• Handles nested structures, missing values, and mixed types robustly

5. Data normalization and diagnostics

• Post-processes R data.frames to fix dtype, timezone, and NA semantics
• Normalizes column types for reliable Python-side comparison
• Supports structured mismatch diagnostics between Python and R data

6. Function invocation across scripts and packages

• Calls functions defined in sourced R scripts, base R, or installed packages
• Supports qualified function names (e.g. stats::median)
• Executes functions within the active project and library context

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Calling base R functions and managing packages

In addition to sourcing local R scripts, rpy-bridge supports calling functions from base R and installed packages directly from Python.

Current support includes:

• Calling base R functions without a local R script
• Executing functions from installed R packages within the active environment

Planned extensions (roadmap):

• Programmatic installation of R packages into the active renv or system environment when explicitly enabled
• Declarative package requirements at the Python call site
• Safe, opt-in package installation for CI and ephemeral environments

Package installation is intentionally not automatic by default to preserve reproducibility and avoid side effects during execution.

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Installation

Prerequisites

• System R installed and available on PATH
• Python 3.11+ (tested on 3.11–3.12)

From PyPI

Install rpy-bridge with rpy2 for full R support:

python3 -m pip install rpy-bridge rpy2

Using uv:

uv add rpy-bridge rpy2

Development install

python3 -m pip install -e .

or:

uv sync

Required Python dependencies

• rpy2
• pandas
• numpy

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Usage

See Quickstart above and examples in examples/basic_usage.py.

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Round-trip Python ↔ R behavior

rpy-bridge attempts to convert Python objects to R and back. Most objects used in scientific and ML pipelines round-trip cleanly, but some heterogeneous Python structures may be wrapped or slightly altered due to differences in R’s type system.

Python type	Round-trip fidelity	Notes
int, float, bool, str	High	Scalars convert directly
Homogeneous list of numbers/strings	High	Converted to atomic R vectors
Nested homogeneous lists	High	Converted to nested R lists
pandas.DataFrame / pd.Series	High	Converted to data.frame and normalized on return
Mixed-type list or dict	Partial	May be wrapped in single-element vectors
None / pd.NA	High	Converted to R NULL

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R setup helpers

Helper scripts are provided in examples/r-deps/ to prepare R environments.

• Install system R dependencies (macOS / Homebrew):

bash examples/r-deps/install_r_dev_deps_homebrew.sh

• Initialize an renv project:

source("examples/r-deps/setup_env.R")

• Restore the environment on a new machine:

renv::restore()

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Who this is for

rpy-bridge is designed for:

• Python-first pipelines that rely on mature R code
• Teams where R logic must remain authoritative
• CI or production systems that cannot rely on interactive R sessions
• Multi-repo or multi-directory projects with non-trivial filesystem layouts

It is not intended as a convenience wrapper for exploratory R usage.

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Licensing

• rpy-bridge is released under the MIT License © 2025 Victoria Cheung
• Depends on rpy2, licensed under the GNU GPL (v2 or later)

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Acknowledgements

This package was spun out of internal tooling I wrote at Revolution Medicines. Thanks to the team there for supporting its open-source release.