The reticulate package provides a comprehensive set of tools for interopability between Python and R. The package includes facilites for:
Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
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.
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!
First, install reticulate from GitHub as follows:
Then, depending on your requirements, choose one or more of the following ways of integrating Python code into your R project:
Python in R Markdown — A new Python language engine for R Markdown that supports bi-directional communication between R and Python (R chunks can access Python objects and vice-versa).
Sourcing Python scripts — The
source_python() function enables you to source a Python script the same way you would
source() an R script (Python functions and objects defined within the script become directly available to the R session).
Each of these techniques is explained in more detail below.
The reticulate package includes a Python engine for R Markdown with the following features:
Run Python chunks in a single Python session embedded within your R session (shared variables/state between Python chunks)
Printing of Python output, including graphical output from matplotlib.
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).
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. From example, you can use Pandas to read and manipulate data then easily plot the Pandas data frame using ggplot2:
Note that the reticulate Python engine is enabled by default within R Markdown whenever reticulate is installed.
See the R Markdown Python Engine documentation for additional details.
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 some functions within it:
 ".git" ".gitignore" ".Rbuildignore" ".RData"  ".Rhistory" ".Rproj.user" ".travis.yml" "appveyor.yml"  "DESCRIPTION" "docs" "external" "index.html"  "index.Rmd" "inst" "issues" "LICENSE"  "man" "NAMESPACE" "NEWS.md" "pkgdown"  "R" "README.md" "reticulate.Rproj" "src"  "tests" "vignettes"
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).
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:
source_python() documentation for additional details on sourcing Python code.
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:
exit within the Python REPL to return to the R prompt.
Note that Python code can also access objects from within the R session using the
r object (e.g.
r.flights). See the
repl_python() documentation for additional details on using the embedded Python REPL.
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:
|List of multiple types||Tuple||
|Data Frame||Pandas DataFrame||
|NULL, TRUE, FALSE||None, True, False||
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:
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.
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.
Python Version Configuration — Describes facilities for determining which version of Python is used by reticulate within an R session.
Using reticulate in an R Package — Guidelines and best practices for using reticulate in an R package.
Arrays in R and Python — Advanced discussion of the differences between arrays in R and Python and the implications for conversion and interoperability.
The reticulated python is a speicies of python found in Southeast Asia. They are the world’s longest snakes and longest reptiles…The specific name, reticulatus, is Latin meaning “net-like”, or reticulated, and is a reference to the complex colour pattern.
The reticulate package enables a new flavor of “reticulated” Python code that is weaved closely together with R.