Using reticulate in an R Package - Guidelines and best practices for using reticulate in an R package.Īrrays in R and Python - Advanced discussion of the differences between arrays in R and Python and the implications for conversion and interoperability. Installing Python Packages - Documentation on installing Python packages from PyPI or Conda, and managing package installations using virtualenvs and Conda environments. Python Version Configuration - Describes facilities for determining which version of Python is used by reticulate within an R session. R Markdown Python Engine - Provides details on using Python chunks within R Markdown documents, including how call Python code from R chunks and vice-versa. The following articles cover the various aspects of using reticulate:Ĭalling Python from R - Describes the various ways to access Python objects from R as well as functions available for more advanced interactions and conversion behavior. See the R Markdown Python Engine documentation for additional details. Note that the reticulate Python engine is enabled by default within R Markdown whenever reticulate is installed. For example, you can use Pandas to read and manipulate data then easily plot the Pandas data frame using ggplot2: r.x would access to x variable created within R from Python)īuilt in conversion for many Python object types is provided, including NumPy arrays and Pandas data frames. py$x would access an x variable created within Python from R).Īccess to objects created within R chunks from Python using the r object (e.g.

Printing of Python output, including graphical output from matplotlib.Īccess to objects created within Python chunks from R using the py object (e.g. Run Python chunks in a single Python session embedded within your R session (shared variables/state between Python chunks) The reticulate package includes a Python engine for R Markdown with the following features:

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