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Reticulate is a package that allows you to call Python code from R in various ways, such as R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively. It also allows you to translate between R and Python objects, such as Pandas data frames and NumPy arrays, and to bind to different versions of Python, including virtual and Conda environments.
Reticulate is an adjective that means resembling a net or network, or being or involving evolutionary change dependent on genetic recombination. It comes from the Latin word reticulum, meaning "small net". It is used in biology and has examples of usage in sentences.
- DictionaryRe·tic·u·late
verb
- 1. divide or mark (something) in such a way as to resemble a net or network: rare "the numerous canals and branches of the river reticulate the flat alluvial plain"
adjective
- 1. arranged or marked like a net or network; reticulated.
Interface to 'Python' modules, classes, and functions. When calling into 'Python', R data types are automatically converted to their equivalent 'Python' types. When values are returned from 'Python' to R they are converted back to R types. Compatible with all versions of 'Python' >= 2.7.
- Kevin Ushey, JJ Allaire, Yuan Tang
- 2021
reticulate is a package that lets you use Python and R together seamlessly in R code, R Markdown documents, and the RStudio IDE. Learn how to install, configure, and use Python packages, environments, and functions with reticulate.
- Overview
- Python in R Markdown
- Importing Python modules
- Sourcing Python scripts
- Python REPL
- Type conversions
- Learning more
- Why reticulate?
- GeneratedCaptionsTabForHeroSec
The reticulate package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:
•Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.
•Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
•Flexible binding to different versions of Python including virtual environments and Conda environments.
Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability. If you are an R developer that uses Python for some of your work or a member of data science team that uses both languages, reticulate can dramatically streamline your workflow!
Getting started Installation
The reticulate package includes a Python engine for R Markdown with the following features:
1.Run Python chunks in a single Python session embedded within your R session (shared variables/state between Python chunks)
2.Printing of Python output, including graphical output from matplotlib.
3.Access to objects created within Python chunks from R using the py object (e.g. py$x would access an x variable created within Python from R).
4.Access to objects created within R chunks from Python using the r object (e.g. r.x would access to x variable created within R from Python)
Built in conversion for many Python object types is provided, including NumPy arrays and Pandas data frames. For example, you can use Pandas to read and manipulate data then easily plot the Pandas data frame using ggplot2:
You can use the import() function to import any Python module and call it from R. For example, this code imports the Python os module and calls the listdir() function:
Functions and other data within Python modules and classes can be accessed via the $ operator (analogous to the way you would interact with an R list, environment, or reference class).
Imported Python modules support code completion and inline help:
See Calling Python from R for additional details on interacting with Python objects from within R.
You can source any Python script just as you would source an R script using the source_python() function. For example, if you had the following Python script flights.py:
Then you can source the script and call the read_flights() function as follows:
If you want to work with Python interactively you can call the repl_python() function, which provides a Python REPL embedded within your R session. Objects created within the Python REPL can be accessed from R using the py object exported from reticulate. For example:
Enter exit within the Python REPL to return to the R prompt.
When calling into Python, R data types are automatically converted to their equivalent Python types. When values are returned from Python to R they are converted back to R types. Types are converted as follows:
If a Python object of a custom class is returned then an R reference to that object is returned. You can call methods and access properties of the object just as if it was an instance of an R reference class.
The following articles cover the various aspects of using reticulate:
•Calling 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.
•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.
•Python Version Configuration — Describes facilities for determining which version of Python is used by reticulate within an R session.
•Installing Python Packages — Documentation on installing Python packages from PyPI or Conda, and managing package installations using virtualenvs and Conda environments.
•Using reticulate in an R Package — Guidelines and best practices for using reticulate in an R package.
From the Wikipedia article on the reticulated python:
From the Merriam-Webster definition of reticulate:
Reticulate is a package for interoperability between R and Python, with features such as Python in R Markdown, importing modules, sourcing scripts, and REPL. Learn how to install, configure, and use reticulate with examples and documentation.
Reticulate means netted, covered with a network, or having the veins or nerves disposed like the threads of a net. Learn how to use this word in a sentence, see its origin and related terms, and explore its scientific and botanical meanings.
Mar 15, 2019 · Learn how to use reticulate, a package that allows you to run Python code within R, to train a Support Vector Machine for a classification task. The web page shows an example of how to use Python's scikit-learn library and R's recipes package to prepare and fit the model.
