rtopy 0.2.0

Lightweight bridge for calling R functions from Python

rtopy

Lightweight Python bridge for calling R functions with seamless type conversion.

Features

• Simple API: Call R functions with native Python types
• Auto type conversion: Supports int, float, str, bool, list, dict, numpy, pandas
• Minimal dependencies: Only requires R + jsonlite package
• Library support: Use any R package in your functions
• Fast: Direct subprocess execution, no rpy2 overhead

Installation

# Install package
pip install rtopy

# Install with optional numpy/pandas support
pip install rtopy[full]

# Requires R to be installed and in PATH
# Install R from: https://cran.r-project.org/
# Install jsonlite in R: install.packages("jsonlite")

Quick Start

from rtopy import RBridge, call_r

# One-off function call
result = call_r(
    r_code="add <- function(x, y) x + y",
    r_func="add",
    x=5, 
    y=3
)
print(result)  # 8.0

# Reusable bridge
rb = RBridge()

# Statistical analysis
code = '''
library(stats)
analyze <- function(data) {
    list(
        mean = mean(data),
        median = median(data),
        sd = sd(data)
    )
}
'''
stats = rb.call(code, "analyze", return_type="dict", data=[1,2,3,4,5])
print(stats)  # {'mean': 3.0, 'median': 3.0, 'sd': 1.58...}

# Return as pandas DataFrame
code = '''
make_data <- function(n) {
    data.frame(
        x = 1:n,
        y = rnorm(n),
        group = sample(c("A", "B"), n, replace=TRUE)
    )
}
'''
df = rb.call(code, "make_data", return_type="pandas", n=100)

Return Types

• "auto": Automatically infer best type (default)
• "int", "float", "str", "bool": Python scalars
• "list", "dict": Python collections
• "numpy": NumPy array (requires numpy)
• "pandas": pandas DataFrame/Series (requires pandas)
• "raw": Raw JSON-parsed output

Advanced Usage

# Custom timeout and verbose mode
rb = RBridge(timeout=60, verbose=True)

# Use any R package
code = '''
library(dplyr)
library(ggplot2)

process_data <- function(values) {
    df <- data.frame(x = values)
    df %>%
        mutate(squared = x^2) %>%
        summarise(mean_x = mean(x), mean_squared = mean(squared))
}
'''
result = rb.call(code, "process_data", data=[1, 2, 3, 4, 5])

# Pass NumPy arrays and pandas DataFrames
import numpy as np
import pandas as pd

arr = np.array([1, 2, 3, 4, 5])
result = rb.call(code, "my_func", data=arr)

df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
result = rb.call(code, "another_func", df=df)

Requirements

• Python >= 3.7
• R >= 3.6
• R package: jsonlite

Optional:

• numpy >= 1.19 (for numpy return type)
• pandas >= 1.1 (for pandas return type)

License

BSD License Clause Clear