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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11To run Python in RStudio, install Python and the R reticulate package, select the intended Python environment before any Python code starts, and then use reticulate to import modules, run scripts, or open a Python console. The key diagnostic command is py_config(), which shows the interpreter RStudio is actually using.
1. Install Python and reticulate
Python must be installed separately from RStudio. If you want reticulate to manage a local Python distribution, Posit documents reticulate::install_miniconda() as a recommended route.
install.packages("reticulate")
library(reticulate)Run these commands in the RStudio Console. Loading reticulate makes Python interoperability available in the current R session.
2. Choose the Python environment before using Python
Reticulate initializes Python lazily. Select the interpreter or environment before calling import(), py_run_file(), repl_python(), or another Python-dependent function.
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Use a specific Python executable
library(reticulate)
use_python("/path/to/python", required = TRUE)Use a virtual environment
use_virtualenv("myenv", required = TRUE)Use a Conda environment
use_condaenv("myenv", required = TRUE)These selectors apply to the active R session. If you change the interpreter after Python has already started, restart the R session and make the selection again before importing anything.
With reticulate 1.41 and later, manually selecting an interpreter is often unnecessary when you declare requirements with py_require(); reticulate can resolve an ephemeral environment automatically. For projects that already depend on a known interpreter, explicit selection remains the clearest option.
3. Confirm what RStudio is using
py_config()Check the output before troubleshooting. It identifies the Python executable, version, and environment visible to the current R session. A terminal may use a different Python installation, so a package that imports successfully there can still be unavailable in RStudio.
4. Install packages into that same environment
After selecting the environment, install Python packages through reticulate:
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5. Four ways to run Python from RStudio
| Method | Use it for | Key function |
|---|---|---|
| Import a module | Calling Python libraries and functions from R | import() |
| Source a script | Loading functions and objects into the R session | source_python() |
| Run a file | Executing a Python file with controlled conversion | py_run_file() |
| Interactive REPL | Exploration and quick Python commands | repl_python() |
Import a Python module
library(reticulate)
np <- import("numpy")
np$array(c(1, 2, 3))The imported object exposes Python modules, classes, and functions to R. Reticulate converts many common Python objects automatically. Use py_to_r() when you need explicit conversion.
Source a Python script
source_python("analysis.py")
result <- calculate_result(data)Functions and objects defined in analysis.py become available in the R session.
Run a Python file
py_run_file("analysis.py", local = FALSE, convert = TRUE)Here, convert = TRUE requests automatic conversion of returned Python objects. You can instead convert objects explicitly with py_to_r().
Open an interactive Python console
repl_python()Objects created in the embedded Python REPL remain available through reticulate’s shared Python state. Exit the REPL using its normal exit command or the RStudio interrupt controls.
6. Mix R and Python in R Markdown
Reticulate provides a Python language engine for R Markdown. An R Markdown document can contain both R and Python chunks, with shared objects and state, so you can use R-specific analysis alongside Python-only libraries in one reproducible report or notebook.
7. Fix the common environment and path problems
Python works in a terminal but not in RStudio
- Run
py_config()in the RStudio Console and note the executable and environment. - If it is not the intended interpreter, restart the R session.
- Call
use_python(),use_virtualenv(), oruse_condaenv()before importing any module. - Install the missing package into that selected environment with
py_install(). - Test the import again from RStudio, not only from a separate terminal.
A script cannot be found
Check RStudio’s working directory and the script name. Use an absolute path when the project directory may vary:
py_run_file("/absolute/path/to/analysis.py", local = FALSE, convert = TRUE)Changing environments appears to have no effect
Python may already be initialized. Restart the R session, select the environment first, and then run py_config() followed by the import or script call.
R and Python objects behave differently
Allow reticulate’s automatic conversion for common types, or make the boundary explicit with py_to_r() when you need predictable R objects.
8. A reliable project workflow
- Install Python and
reticulate. - Open the project in RStudio and select its Python executable, virtualenv, or Conda environment.
- Restart the R session whenever you change that selection.
- Run
py_config()to verify the active interpreter. - Install dependencies with
py_install()in the verified environment. - Import modules, source files, run scripts, or use an R Markdown Python chunk.
The current Posit reference identifies reticulate 1.47.0 for py_install(). Because environment-resolution behavior and helper APIs can change, check the current Posit documentation when writing version-specific setup instructions.
Frequently Asked Questions
Do I need to install Python if RStudio is already installed?
Yes. RStudio is an IDE, while reticulate connects your R session to a separate Python installation or managed environment.
Why does reticulate say a package is missing when it works in my terminal?
The terminal and RStudio are likely using different Python environments. Run py_config(), select the intended environment before importing, and install the package there with py_install().
Can R and Python share data in one R Markdown document?
Yes. Reticulate’s Python engine lets R and Python chunks communicate through shared objects and state.
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