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index.Rmd
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---
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# R Interface to Python
[](https://cran.r-project.org/package=reticulate)
[](https://travis-ci.com/github/rstudio/reticulate) [](https://ci.appveyor.com/project/rstudio/reticulate)
The **reticulate** package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:
<img src="images/reticulated_python.png" width=200 align=right style="margin-left: 15px;" alt="reticulated python"/>
- 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
Install the **reticulate** package from CRAN as follows:
```r
install.packages("reticulate")
```
#### Python version
By default, reticulate uses the version of Python found on your `PATH` (i.e. `Sys.which("python")`).
The `use_python()` function enables you to specify an alternate version, for example:
```r
library(reticulate)
use_python("/usr/local/bin/python")
```
The `use_virtualenv()` and `use_condaenv()` functions enable you to specify versions of Python in virtual or Conda environments, for example:
```r
library(reticulate)
use_virtualenv("myenv")
```
See the article on [Python Version Configuration](https://rstudio.github.io/reticulate/articles/versions.html) for additional details.
#### Python packages
You can install any required Python packages using standard shell tools like `pip` and `conda`. Alternately, reticulate includes a set of functions for managing and installing packages within virtualenvs and Conda environments. See the article on [Installing Python Packages](https://rstudio.github.io/reticulate/articles/python_packages.html) for additional details.
#### Calling Python
There are a variety of ways to integrate Python code into your R projects:
1) [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).
2) [Importing Python modules] — The `import()` function enables you to import any Python module and call its functions directly from R.
3) [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).
4) [Python REPL] — The `repl_python()` function creates an interactive Python console within R. Objects you create within Python are available to your R session (and vice-versa).
Each of these techniques is explained in more detail below.
## Python in R Markdown
The **reticulate** package includes a Python engine for [R Markdown](http://rmarkdown.rstudio.com) 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](https://matplotlib.org/).
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)
<div style="clear: both;"></div>
Built in conversion for many Python object types is provided, including [NumPy](http://www.numpy.org/) arrays and [Pandas](https://pandas.pydata.org/) data frames. From example, you can use Pandas to read and manipulate data then easily plot the Pandas data frame using [ggplot2](http://ggplot2.org/):
{.screenshot}
Note that the reticulate Python engine is enabled by default within R Markdown whenever reticulate is installed.
See the [R Markdown Python Engine](https://rstudio.github.io/reticulate/articles/r_markdown.html) documentation for additional details.
## Importing Python modules
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:
```{r, eval=FALSE}
library(reticulate)
os <- import("os")
os$listdir(".")
```
```
[1] ".git" ".gitignore" ".Rbuildignore" ".RData"
[5] ".Rhistory" ".Rproj.user" ".travis.yml" "appveyor.yml"
[9] "DESCRIPTION" "docs" "external" "index.html"
[13] "index.Rmd" "inst" "issues" "LICENSE"
[17] "man" "NAMESPACE" "NEWS.md" "pkgdown"
[21] "R" "README.md" "reticulate.Rproj" "src"
[25] "tests" "vignettes"
```
Functions and other data within Python modules and classes can be accessed via the `$` operator (analogous to the way you would interact with an R list, environment, or reference class).
Imported Python modules support code completion and inline help:
{.screenshot}
See [Calling Python from R](https://rstudio.github.io/reticulate/articles/calling_python.html) for additional details on interacting with Python objects from within R.
## Sourcing Python scripts
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*:
```{python, eval=FALSE}
import pandas
def read_flights(file):
flights = pandas.read_csv(file)
flights = flights[flights['dest'] == "ORD"]
flights = flights[['carrier', 'dep_delay', 'arr_delay']]
flights = flights.dropna()
return flights
```
Then you can source the script and call the `read_flights()` function as follows:
```{r, eval=FALSE}
source_python("flights.py")
flights <- read_flights("flights.csv")
library(ggplot2)
ggplot(flights, aes(carrier, arr_delay)) + geom_point() + geom_jitter()
```
See the `source_python()` documentation for additional details on sourcing Python code.
## Python REPL
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:
{.screenshot}
Enter `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.
## Type conversions
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)`
| Data Frame | Pandas DataFrame | ` data.frame(x = c(1,2,3), y = c("a", "b", "c"))` |
| 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.
## Learning more
The following articles cover the various aspects of using **reticulate**:
- [Calling Python from R](https://rstudio.github.io/reticulate/articles/calling_python.html) — 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](https://rstudio.github.io/reticulate/articles/r_markdown.html) — Provides details on using Python chunks within R Markdown documents, including how call Python code from R chunks and vice-versa.
- [Python Version Configuration](https://rstudio.github.io/reticulate/articles/versions.html) — Describes facilities for determining which version of Python is used by reticulate within an R session.
- [Installing Python Packages](https://rstudio.github.io/reticulate/articles/python_packages.html) — Documentation on installing Python packages from PyPI or Conda, and managing package installations using virtualenvs and Conda environments.
- [Using reticulate in an R Package](https://rstudio.github.io/reticulate/articles/package.html) — Guidelines and best practices for using reticulate in an R package.
- [Arrays in R and Python](https://rstudio.github.io/reticulate/articles/arrays.html) — Advanced discussion of the differences between arrays in R and Python and the implications for conversion and interoperability.
## Why reticulate?
From the [Wikipedia](https://en.wikipedia.org/wiki/Reticulated_python) article on the reticulated python:
> The reticulated python is a species 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.
From the [Merriam-Webster](https://www.merriam-webster.com/dictionary/reticulate) definition of reticulate:
> 1: resembling a net or network; especially : having veins, fibers, or lines crossing a reticulate leaf. 2: being or involving evolutionary change dependent on genetic recombination involving diverse interbreeding populations.
The package enables you to *reticulate* Python code into R, creating a new breed of project that weaves together the two languages.