How to Run Python Inside RStudio with reticulate

You can run Python directly inside RStudio by leveraging the reticulate R package, which bridges the R and Python ecosystems. Here is how to set up and use Python inside the RStudio IDE.

RStudio’s Python interpreter settings panel

1. Set Up the Python Interpreter

Before writing Python code, configure RStudio to recognize your Python installation.

Open RStudio and navigate to Tools > Global Options (or Preferences on a Mac).
Click Python in the left menu sidebar.
Under Python interpreter, click Select… and choose your preferred environment (e.g., system Python, Anaconda, Miniconda, or a virtual environment).

2. Install the Required R Package

Install and load reticulate within your R console:

install.packages("reticulate")
library(reticulate)

3. Three Ways to Work with Python in RStudio

Depending on your workflow, you can use Python in RStudio in three main ways.

Option A: Native Python Scripts

You can treat RStudio like a standard Python IDE.

Go to File > New File > Python Script.
Write standard Python code and press Ctrl+Enter (or Cmd+Enter on Mac) to run lines one at a time.
Interactive plots (like matplotlib or seaborn) will automatically populate in the Viewer pane.

Option B: Mix R and Python in Quarto or R Markdown

You can build data pipelines where R and Python share data seamlessly in a single document, switching between languages with standard code chunks:

# Step 1: Load an R dataset
library(reticulate)
library(palmerpenguins)
r_data <- palmerpenguins::penguins

# Step 2: Use the R dataset inside a Python chunk via 'r.'
import pandas as pd

# 'r.r_data' fetches the object from the R environment
py_df = pd.DataFrame(r.r_data)
print(py_df.describe())

Option C: Call Python Directly from an R Script

You can import Python modules directly into your native R code with import(). Python attributes are accessed with $ instead of .:

library(reticulate)

# Import a Python library
pd <- import("pandas")

# Call a Python function using R syntax
my_data <- pd$read_csv("dataset.csv")

4. Installing Python Libraries

To use tools like numpy or pandas, install them directly through R using reticulate’s helper function:

reticulate::py_install("pandas")

Alternatively, install them via your system terminal with pip install pandas before starting your RStudio session.

5. Running Python Line-by-Line (REPL Console)

If you want to run Python code line-by-line exactly like you do with R, send lines from a Python script to an interactive Python REPL console.

Step 1: Open a Python Script

Go to File > New File > Python Script.
Save the file with a .py extension (e.g., script.py).

Step 2: Launch the Python REPL Console

Before executing code line-by-line, start the interactive Python environment in your console. Run this single line in your standard R console (bottom-left pane):

reticulate::repl_python()

Your console prompt changes from > (R) to >>> (Python) — the RStudio console is now officially a Python terminal.

Step 3: Execute Code Line-by-Line

Go back to your .py script editor and use the same keyboard shortcuts you use for R:

Run a single line: place your cursor on the line and press Ctrl+Enter.
Run multiple lines: highlight the block of code and press Ctrl+Enter.

The code executes instantly in the >>> console below, and any variables you create show up in the Environment pane.

To exit the Python environment and go back to R at any point, type exit in the console and hit enter.