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![Deadpool & Wolverine 2024 Banner](https://pgw.udn.com.tw/gw/photo.php?u=https://uc.udn.com.tw/photo/2024/06/19/0/29846950.jpg&x=0&y=0&sw=0&sh=0&exp=3600&w=930)
# R Interface to Python
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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"/>
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- 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
## We have curated frequently asked questions and common mistakes on Discourse, you can read them here: [FAQ and Common mistakes](https://watching.nwsautodaily.com/zh/)
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
# 📎 Important links
Install the **reticulate** package from CRAN as follows:
```r
install.packages("reticulate")
```
#### Python version
💪 VER AHORA ☛☛
 
https://gitlab.aicrowd.com/aicrowd/loginpass/-/merge_requests/11
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,
💪 ✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/aicrowd/example-grader/-/merge_requests/12
for example:
``` r
💪 ✅➤➤Sub tw HK ➫ ➫https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/575
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
👉 [Challenge page](https://www.aicrowd.com/challenges/airborne-object-tracking-challenge?utm_source=starter-kit&utm_medium=click&utm_campaign=prime-air)
library(reticulate)
use_virtualenv("myenv")
```
See the article on [Python Version
[![Discord](https://img.shields.io/discord/565639094860775436.svg)](https://discord.gg/hAuevqx9Tj)
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](#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](#importing-python-modules) — The
`import()` function enables you to import any Python module and call
it’s functions directly from R.
3) [Sourcing Python scripts](#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](#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](https://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$x` would access an `x` variable created within
Python from R).
4) Access to objects created within R chunks from Python using the `r`
object (e.g. `r.x` would access to `x` variable created within R
from Python)
<div style="clear: both;">
</div>
Built in conversion for many Python object types is provided, including
[NumPy](https://numpy.org/) arrays and
[Pandas](https://pandas.pydata.org/) data frames. For example, you can
use Pandas to read and manipulate data then easily plot the Pandas data
frame using [ggplot2](https://ggplot2.tidyverse.org/):
![](images/rmarkdown_engine_zoomed.png)
Note that the reticulate Python engine is enabled by default within R
Markdown whenever reticulate is installed.
See the [R Markdown Python
Engine](https://rstudio.github.io/reticulate/articles/r_markdown.html)
documentation for additional details.
## Importing Python modules
You can use the `import()` function to import any Python module and call
it from R. For example, this code imports the Python `os` module and
calls the `listdir()` function:
``` r
library(reticulate)
os <- import("os")
os$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:
![](images/reticulate_completion.png)
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
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
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:
![](images/python_repl.png)
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.