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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesUse RStudio’s reticulate package to embed Python in the active R session. Install Python and reticulate, select the intended interpreter before Python starts, verify it with py_config(), then import modules, run scripts, open a Python REPL, or combine Python and R in an R Markdown document.
Prerequisites and first setup
You need a working Python installation and the R package reticulate. In the RStudio Console, run:
install.packages("reticulate")
library(reticulate)Posit also documents reticulate::install_miniconda() as a recommended route when you want reticulate to manage a local Miniconda distribution. Install Python before trying to import a module or execute a Python file.
Choose the Python environment before using Python
Reticulate initializes its Python bindings lazily. Make the environment selection your first Python-related operation in a new R session—before import(), py_run_file(), or similar calls.
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library(reticulate)
# Select a specific interpreter
use_python("/path/to/python", required = TRUE)
# Or select a virtualenv
use_virtualenv("myenv", required = TRUE)
# Or select a Conda environment
use_condaenv("myenv", required = TRUE)Replace the path or environment name with the one used by your project. Selection applies to the current R session. If Python has already been initialized, restart the R session and select the interpreter again.
Automatic environment resolution with py_require()
With reticulate 1.41 and later, declaring requirements with py_require() can let reticulate resolve an ephemeral environment automatically, so manual selection is often unnecessary. Use explicit selectors when a project must run against a particular existing interpreter or environment.
Verify the interpreter RStudio is using
py_config()Check the reported Python executable and environment before diagnosing missing packages, path errors, or differences between RStudio and a terminal.
Install Python packages into that same environment
Install dependencies through reticulate or the environment’s documented package manager, making sure the target environment is the one selected by the R session.
py_install(c("numpy", "pandas"), envname = "myenv")py_install() installs into a virtualenv or Conda environment. If you omit envname, reticulate uses the environment selected by RETICULATE_PYTHON_ENV, or the r-reticulate environment when that variable is unset.
When a package exists in several environments, call use_virtualenv() or use_condaenv() first, restart if Python was already initialized, and then install or import the package. A successful terminal import does not prove that the RStudio session sees the same environment.
Four ways to run Python from RStudio
| Method | Use it when | Example |
|---|---|---|
import() |
You need to call functions, classes, or objects from a Python module. | np <- import("numpy") |
source_python() |
You want definitions from a Python script exposed directly in the R session. | source_python("analysis.py") |
py_run_file() |
You want to execute a Python file and control conversion and execution scope. | py_run_file("analysis.py", local = FALSE, convert = TRUE) |
repl_python() |
You want an interactive Python prompt embedded in the RStudio session. | repl_python() |
Import a module and call it
library(reticulate)
np <- import("numpy")
np$array(c(1, 2, 3))import() exposes Python modules, classes, and functions to R. Reticulate converts many common Python objects to R automatically. For an explicit conversion, use py_to_r().
values <- np$array(c(1, 2, 3))
values_r <- py_to_r(values)Load functions from a Python script
source_python("analysis.py")
result <- calculate_result(data)Functions and objects defined in analysis.py become available in the R session after source_python() returns.
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py_run_file("analysis.py", local = FALSE, convert = TRUE)Set convert = TRUE when you want automatic conversion of returned objects. You can instead convert individual objects explicitly with py_to_r(). Use an absolute path or confirm the RStudio working directory if the file cannot be found.
Open an interactive Python REPL
repl_python()Objects created in the embedded REPL remain in reticulate’s shared Python state and can be accessed from the R session through reticulate.
Use Python and R together in R Markdown
Reticulate provides a Python language engine for R Markdown. Python and R chunks can communicate through shared objects and state, letting one reproducible document use Python-specific libraries alongside R analysis.
Keep the environment choice reproducible: select or resolve the intended Python environment before the first Python chunk, and install its dependencies there. If a rendered document behaves differently from an interactive session, inspect py_config() in the session that performs the render.
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- Inspect the active configuration. Run
py_config()in the RStudio Console and note the executable and environment. - Restart Python in the R session. Use RStudio’s session restart, then call
use_python(),use_virtualenv(), oruse_condaenv()before any import or file execution. - Install into the reported environment. Run
py_install()with the intendedenvname, or use that environment’s virtualenv/Conda installer. - Test the import inside RStudio. For example, run
import("numpy")in the same Console or document that will use it. - Check script paths. Confirm the working directory with R’s path tools or pass an absolute path to
source_python()orpy_run_file().
The key distinction is that RStudio embeds Python in the current R process; it does not automatically use whichever Python your separate terminal happens to invoke.
Which reticulate approach should you use?
- Call a library repeatedly: use
import()and keep the returned module object. - Reuse a Python utility script: use
source_python()when its functions should become ordinary R-session objects. - Run a complete file: use
py_run_file(), especially when you need explicit control over conversion or execution scope. - Explore interactively: use
repl_python(). - Publish a mixed-language report: use Python chunks in R Markdown and document the environment and dependencies.
Version note
Posit’s current py_install() reference identifies reticulate version 1.47.0. Environment resolution and helper APIs can change, so check the current Posit reticulate documentation when an instruction depends on a specific version.
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