The reticulate package provides an R interface to Python modules, classes, and functions. For example, this code imports the Python os module and calls some functions within it:
library(reticulate)
os <- import("os")
os$chdir("tests")
os$getcwd()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).
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:
| R | Python | Examples |
|---|---|---|
| Single-element vector | Scalar | 1, 1L, TRUE, "foo" |
| Multi-element vector | List | c(1.0, 2.0, 3.0), c(1L, 2L, 3L) |
| List of multiple types | Tuple | list(1L, TRUE, "foo") |
| Named list | Dict | list(a = 1L, b = 2.0), dict(x = x_data) |
| Matrix/Array | NumPy ndarray | matrix(c(1,2,3,4), nrow = 2, ncol = 2) |
| Function | Python function | function(x) x + 1 |
| NULL, TRUE, FALSE | None, True, False | NULL, 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 reticulate package is compatible with all versions of Python >= 2.7. Integration with NumPy is optional and requires NumPy >= 1.6.
First, install reticulate from CRAN as follows:
install.packages("reticulate")Then, learn the basics by reading the Introduction to reticulate.
The following articles cover the various aspects of using reticulate: