“Using R and Python Together with reticulate: A Practical Guide for Data Workflows”

R and Python are often treated as two separate worlds. R is loved for statistics, reporting, and tidy data workflows. Python dominates machine learning, automation, and production pipelines.
But in real projects, you rarely have the luxury of choosing only one.
That’s where reticulate comes in. It gives you a clean, stable way to run Python inside R — in the same script, the same R Markdown file, or the same Quarto document.
In this guide, I’ll show you how to set up reticulate, how to call Python from R, and how to build a simple workflow that mixes both languages without friction.

Why combine R and Python?
There are a few situations where mixing both languages makes your life easier:
· You prefer R for data cleaning but need a Python‑only library
· You want to use pandas or scikit‑learn inside an R Markdown report
· You maintain legacy R scripts but want to integrate Python models
· You want one unified workflow instead of switching between environments
reticulate solves all of these problems by embedding Python directly into R.
Installing and configuring reticulate
Install the package:
install.packages("reticulate")
library(reticulate)
Then check which Python installation reticulate is using:
py_config()

If you want to force a specific Python version:
use_python("/usr/bin/python3", required = TRUE)

Or create a dedicated virtual environment:
virtualenv_create("r-reticulate")
use_virtualenv("r-reticulate", required = TRUE)

This keeps your R–Python workflow clean and isolated.

Calling Python directly from R
reticulate lets you run Python code inside R using the py_run_string() function:
py_run_string("
import numpy as np
x = np.arange(10)
")
The variable x now exists in R:
py$x

This is one of the most powerful features: Python objects become R objects automatically.
Using Python chunks in R Markdown or Quarto.
This is where reticulate shines. You can write Python code directly inside an R Markdown document:

import pandas as pd

df = pd.DataFrame({
    "a": [1, 2, 3],
    "b": [10, 20, 30]
})

df

And then use the result in R:
df <- py$df
summary(df)
This creates a seamless workflow where R handles reporting and Python handles computation.

A simple R + Python hybrid workflow
Here’s a small example that shows the full cycle:

1. Load data in R library(tidyverse)

data <- tibble(
x = rnorm(100),
y = rnorm(100)

1. Send it to Python for a scikit‑learn model py$data <- data

py_run_string("
from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array(data['x']).reshape(-1, 1)
y = np.array(data['y'])

model = LinearRegression()
model.fit(X, y)

coef_ = model.coef_[0]
intercept_ = model.intercept_
")

1. Bring the results back to R coef <- py$coef_ intercept <- py$intercept_

coef
Intercept

1. Use R for visualization library(ggplot2)

ggplot(data, aes(x, y)) +
geom_point() +
geom_abline(slope = coef, intercept = intercept, color = "red") +
theme_minimal()

This is the real power of reticulate: R does what it’s best at, Python does what it’s best at, and you get one unified workflow.
When should you NOT use reticulate?
reticulate is powerful, but not perfect. Avoid it when:
· You need heavy GPU workloads
· You want to deploy a Python model to production
· You need strict reproducibility across machines
· You’re working inside a restricted environment (corporate servers)
For everything else, it’s a fantastic tool.
Final thoughts
R and Python don’t need to compete.
With reticulate, they can work together in a single, clean workflow that feels natural and efficient.
If you already use R Markdown or Quarto, integrating Python becomes effortless.
If you’re a Python user, reticulate gives you access to R’s reporting ecosystem without rewriting your code.
This hybrid approach is becoming more common in real‑world data teams — and it’s a skill that makes you more flexible and valuable as a developer.

Tools and resources:

If you want ready‑made R Markdown templates, dashboards, or automation tools, you can find them here:
· Gumroad: gabrieli112.gumroad.com
· Payhip: payhip.com/CrisDigital