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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
<head>
<title>Keeping an exotic pet in your home!</title>
<meta charset="utf-8" />
<meta name="author" content="Emma Rand" />
<link href="libs/remark-css/default.css" rel="stylesheet" />
<link href="libs/remark-css/rladies.css" rel="stylesheet" />
<link href="libs/remark-css/rladies-fonts.css" rel="stylesheet" />
<script src="libs/kePrint/kePrint.js"></script>
</head>
<body>
<textarea id="source">
background-image: url(pics/useR2019.png)
background-position: 4% 0%
background-size: 250px
class: inverse, right, bottom
# Keeping an exotic pet in your home!
## Taming Python to live in RStudio because<br>sometimes the best language is both!
<br>
### Emma Rand<br>[email protected]<br><br>Materials: https://github.com/3mmaRand/useR2019_tutorial
---
class: center, middle
# But first.....Who am I? <br> and some Thank yous!
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Who I am?
.pull-left[
]
.pull-right[
Lecturer (Assistant Professor), Department of Biology University of York, UK
I'm a biologist by training.
Long time R user, relatively new to Python.
<br>
<br>
<br>
<br>
⬅️ York, twinned with Dijon.
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
]
---
background-image: url(pics/member_institute_logos.png)
background-position: 95% 7%
# Thank yous!
## To the local organisation committee
<small>
* Nathalie Vialaneix (chair), MIAT, INRA
* Sébastien Déjean (vice-chair), Institut de Mathématiques de Toulouse, Université Toulouse 3 Paul Sabatier
* Anne Ruiz-Gazen (vice-chair), Toulouse School of Economics, Université Toulouse 1 Capitole
* Heather Turner (vice-chair), statistical consultant and associate fellow of the Statistics Department at the University of Warwick
* Aurore Archimbaud, Toulouse School of Economics
* Christophe Bontemps, Toulouse School of Economics, INRA
* Robert Faivre, MIAT, INRA
* Xavier Gendre, Institut de Mathématiques de Toulouse, Université Toulouse 3 Paul Sabatier
* Thibault Laurent, Toulouse School of Economics, CNRS
* Élise Maigné, Observatoire du Développement Rural, INRA
* Pierre Neuvial, Institut de Mathématiques de Toulouse, CNRS
* Rémi Servien, InTheRes, INRA
* Matthias Zytnicki, MIAT, INRA
</small>
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Thank yous!
## For funding
.pull-left[
To the organisers and their sponsers
]
.pull-right[
My institution
]
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
background-image: url(pics/leila.jpg)
background-position: 90% 50%
background-size: 400px
# Thank yous!
## Leila Khajavi
.pull-left[
Leila is an American pursuing her PhD in Bioinformatics here in Toulouse, affiliated with both MIAT (INRA) and CPTP (INSERM).
She is very kindly giving her time here today to help out and has already contributed to the session by going through the material and giving some feedback.
But any errors that remain are mine!
]
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Thank yous!
My colleagues at the University of York allowed me to practice on them:
James Chong
Bryden Fields
Martina Stoycheva
Jack Law
Oliver Noble
Rebecca Hall
Evie Farnham
Mike Gray
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
--
And finally...
--
## Everyone here for coming! 😄
---
<iframe src="https://docs.google.com/forms/d/e/1FAIpQLSdHXFrhsSfUXEyIhHAnP40fH5a66eRZm3YZaEcNFT-F08CKjA/viewform?embedded=true" width="640" height="650" frameborder="0" marginheight="0" marginwidth="0">Loading...</iframe>
---
background-image: url(https://upload.wikimedia.org/wikipedia/commons/f/f8/Python_logo_and_wordmark.svg)
background-position: 95% 95%
background-size: 250px
# Why `reticulate`?
You finally found the **perfect** solution to a data problem!
😁
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
--
but it's written in Python
😬
--
You're collaborating with some great people
😁
--
but they mainly use Python!
😬
You want to use existing/available solutions and collaborate more easily.
---
# Why `reticulate`?
You could move to the darkside 🐍...
--
But you're familiar 💙 with R...
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Why `reticulate`?
But you're familiar with R
**and very at home in RStudio...**
&nbsp;
&nbsp;
&nbsp;
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Why `reticulate`?
But you're familiar with R
and very at home in RStudio
**because it's comfortable...**
&nbsp;
&nbsp;
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Why `reticulate`?
But you're familiar with R
and very at home in RStudio
because it's comfortable
**and has many tools you like...**
&nbsp;
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Why `reticulate`?
But you're familiar with R
and very at home in RStudio
because it's comfortable
and has many tools you like
**and the sun always shines!**
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Why `reticulate`?
So even though Python has some great tools...
&nbsp;
&nbsp;
&nbsp;
&nbsp;
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Why `reticulate`?
So even though Python has some great tools
**and some very cool people...**
&nbsp;
&nbsp;
&nbsp;
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Why `reticulate`?
So even though Python has some great tools
and some very cool people
**you don't want to move in!**
&nbsp;
&nbsp;
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Why `reticulate`?
Thankfully, there's `Reticulate`
<small>JJ Allaire, Kevin Ushey and Yuan Tang (2018). reticulate: Interface to 'Python'. R package version 1.10. https://CRAN.R-project.org/package=reticulate </small>
&nbsp;
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
class: inverse, center, middle
# Tutorial Overview
---
# Tutorial Overview 🗺
* Context: Very brief background to R 💙and Python 🐍
* Rationale: why use [Reticulate](https://rstudio.github.io/reticulate), what are its key features, how can it be used and set up
* Background: A little Python. Only what you need to start making sense of calling Python from R
* Using Python interactively with `repl_python()`
* Integration in R markdown 🖋: Writing your own `reticulate` tutorial
* Part 1: Building your understanding
* Part 2: Classifying audio segments
What the tutorial won't be: a thorough introduction to Python, R Markdown or machine learning.
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
<iframe src="https://docs.google.com/forms/d/e/1FAIpQLSfFYrr2fN0fVRoKl0yvZUJSM3FwEZuO3ZzKLfqXtT6XvJumvQ/viewform?embedded=true" width="640" height="600" frameborder="0" marginheight="0" marginwidth="0">Loading...</iframe>
---
# Aims
This tutorial was designed for:
* beginner to intermediate R users
* those with little to no experience of Python
* those who may not have experience of Rmarkdown
--
By the end of the tutorial you should be able to:
* pass objects between simultaneous R and Python sessions
* use Python methods in R code
* incorporate Python snippets in to your R workflow even if you don't understand in detail how they work
As long as you know what the Python methods and code snippets are for, and have a good-enough understanding of their inputs and outputs, then you can use `reticulate`.
--
Some of the code is specifically designed to develop understanding of the integration rather than reflect a workflow. The later section comprises an example workflow.
---
# Background
<table>
<thead>
<tr>
<th style="text-align:left;"> </th>
<th style="text-align:left;"> R </th>
<th style="text-align:left;"> Python </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> Released </td>
<td style="text-align:left;"> 1995 </td>
<td style="text-align:left;"> 1991 </td>
</tr>
<tr>
<td style="text-align:left;"> Author </td>
<td style="text-align:left;"> Ihaka &amp; Gentleman, Chambers </td>
<td style="text-align:left;"> van Rossum </td>
</tr>
<tr>
<td style="text-align:left;"> Purpose </td>
<td style="text-align:left;"> User-friendly data analysis and visualisation for 'non-programmers' </td>
<td style="text-align:left;"> Object-oriented,
Readable, general purpose programming language </td>
</tr>
<tr>
<td style="text-align:left;"> Users most likely to be </td>
<td style="text-align:left;"> Statistics graduates, Academics, data scientists </td>
<td style="text-align:left;"> Computer Science graduates, Software engineers </td>
</tr>
<tr>
<td style="text-align:left;"> Features </td>
<td style="text-align:left;"> R Markdown, tidyverse </td>
<td style="text-align:left;"> Integration with web aps, Unified Machine learning API </td>
</tr>
</tbody>
</table>
.footnote[
Materials: https://github.com/3mmaRand/useR2019_tutorial
]
---
# Why use Reticulate
## Speed up your workflow
* Problem solving is the defining feature of a data scientist
* Language should be secondary
* Choice of language driven by early impressions of the data. Change in direction later means lost time in translating
## Facilitate collaboration
* Allows you to leverage the skills expertise of the whole team
* Solves the hardest problem in Data Science - People<sup>1</sup>.
* Many Data Scientists know both and they are happier<sup>2</sup>
.footnote[
[1] Mangano, 2019
[2] Stack Overflow Developers' Survey, NanoMathius, 2018
]
---
# Reticulate
## Key features
1. Ability to call Python from R
2. Translation between R objects and Python objects
3. Flexible binding to different Python environments
---
# Reticulate
## Alllows you to use Python in four ways:
1. Interactively in the console: `repl_python()`
2. Sourcing Python scripts
3. Importing Python modules
4. In R Markdown documents
--
We will start with `repl_python()` to build our understanding.
Then use R Markdown.
---
# Reticulate
## Ingredients
You will need
* [RStudio 1.2](https://www.rstudio.com/products/rstudio/)
1.2 is needed for some of the most useful features
--
* Python
[Anaconda 3](https://www.anaconda.com/distribution/)
recommended for data science, includes many useful libraries.
--
* The `reticulate` package. I recommend using the development version
```r
devtools::install_github("rstudio/reticulate")
```
.centre[
]
---
background-image: url(https://upload.wikimedia.org/wikipedia/en/c/cd/Anaconda_Logo.png)
background-position: 10% 30%
background-size: 280px
# This one
--
.pull-right[
**Not this one**
]
---
# Reticulate
## You will also need
* Any other Python modules your Python code depends on (not needed here)
--
* Probably / possibly.....to set to the QT_PLUGIN_PATH environment variable.
In windows: Control Panel -> System and Security -> System then
Advanced System settings -> Environment variables
I have set mine to:
`C:\Program Files\RStudio\bin\plugins; C:\ProgramData\Anaconda3\Library\plugins`
.footnote[
If you can describe and resolve in a better but still minimal way, please get in touch!
]
---
# Reticulate
## In windows: QT_PLUGIN_PATH environment variable
---
# Reticulate
## In windows: QT_PLUGIN_PATH environment variable
If you get this error:
.pull-left[
**This application has failed to start because it could not find or load the qt platform plugin "windows" in "" **
]
.pull-right[
]
Setting the QT_PLUGIN_PATH environment variable as on the previous slide should fix it.
.footnote[
If you can describe and resolve in a better but still minimal way, please get in touch!
]
---
class: inverse, center, middle
# A little Python
---
background-image: url(pics/cute-snake.jpg)
background-position: 95% 7%
# Python fundamentals
Suppose you wanted to created an array of 5 numbers.
In R you might do this as:
```r
r_array <- c(4, 5, 1, 6, 8)
```
--
In Python you might use a list<sup>1</sup>
A list is created like this:
```python
python_list = [4, 5, 1, 6, 8]
```
.footnote[
[1] Python doesn't have a native array data structure
]
--
Python uses `=` for assignment
--
The square brackets denote a list
---
background-image: url(pics/cute-snake.jpg)
background-position: 95% 7%
# Python fundamentals
But lists do not behave as a R user might expect.
For example, what would you expect the output to be?
```python
python_list = [4, 5, 1, 6, 8]
*python_list * 2
```
--
This?
```python
python_list = [4, 5, 1, 6, 8]
python_list * 2
*[8, 10, 2, 12, 16]
```
--
Infact it is this:
```python
python_list = [4, 5, 1, 6, 8]
python_list * 2
*[4, 5, 1, 6, 8, 4, 5, 1, 6, 8]
```
😲
---
background-image: url(pics/cute-snake.jpg)
background-position: 95% 7%
# Python fundamentals
Instead you might use the [NumPy package](http://www.numpy.org/)<sup>1</sup>. NumPy arrays behave like R vectors/arrays.
.footnote[
[1] NumPy is the fundamental package for scientific computing with Python. It is part of the
SciPy ecosystem.
Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific Tools for Python, 2001-, http://www.scipy.org/.
]
--
To make a NumPy array we need to first`import` NumPy, then use it's `array()` function.
This is going to introduce us to several Pythonesque things.
---
background-image: url(pics/cute-snake.jpg)
background-position: 95% 7%
# Python fundamentals
The Python code looks like this:
```python
import numpy as np
python_array = np.array([4, 5, 1, 6, 8])
```
---
background-image: url(pics/cute-snake.jpg)
background-position: 95% 7%
# Python fundamentals
The Python code looks like this:
```python
*import numpy as np
python_array = np.array([4, 5, 1, 6, 8])
```
`import` in Python is the equivalent of `library()` in R
--
To use methods in NumPy (and other modules) we need to use the "dot" notation:
`numpy.method_name()`
To make this quicker to type it is common to use an alias. That's the `as np` bit
---
background-image: url(pics/cute-snake.jpg)
background-position: 95% 7%
# Python fundamentals
```python
import numpy as np
*python_array = np.array([4, 5, 1, 6, 8])
```
The second line of code creates the numpy array (from a list).
--
To do things with `python_array` we might use a built-in function. These are used in a way that will be familar to you, for example:
```python
type(python_array)
<class 'numpy.ndarray'>
```
--
Python also has methods. Methods are called on objects with the dot notation.
For example:
```python
python_array.max()
8
```
---
class: center, middle
# Enough!<br> <br>Let me code
## We will cover some more Python as we go through the tutorial.
---
class: inverse, center, middle
# Using Python in the console
## `reticulate::repl_python()`
---
# Using Python in the console
## Steps
We are going to use Python interactively in the console.
We will
* Create a new project
* Check our Python environment
* Start a Python session from our R session
* Create a NumPy array
* Use the NumPy array
* find its size (an attribute)
* calculate its mean (a method)
* End the Python session
* Access the NumPy array from our R session
.footnote[
<span style=" font-weight: bold; color: white !important;border-radius: 4px; padding-right: 4px; padding-left: 4px; background-color: purple !important;" >Extra exercise</span> Indicates an optional extra exercise to try while you're waiting for me to move on.
]
---
# Using Python in the console
## Organising ourselves
.pull-left[
We are going to work in a project<sup>1</sup>.
File | New Project | Existing Directory
Choose Browse
And navigate to the "music_ml" folder and Open
]
.pull-right[
]
Choose Create Project
.footnote[
[1] New to projects? [RStudio: Using Projects](https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects)
]
---
background-image: url(pics/reticulated_python.png)
background-position: 95% 7%
# Using Python in the console
We are going to be moving between Python and R sessions.
Which could get confusing!
--
What session you are in **at the start of a slide** is indicated like this:
.pull-left[
## 🐍 _Sub-heading_
You are working interactively with python and your prompt should look like this:
```r
>>>
```
You may **finish** the slide in an R session
]
--
.pull-right[
## 💙 _Sub-heading_
You are working interactively with R and your prompt should look like this:
```r
>
```
You may **finish** the slide in a Python session
]
---
background-image: url(pics/reticulated_python.png)
background-position: 95% 7%
# Using Python in the console
## 💙 Prepare to start a Python session
Load the reticulate package
```r
> library(reticulate)
```
For this section on using `repl_python()` I will show the prompt, either `>` or`>>>` in the code.
---
background-image: url(pics/reticulated_python.png)
background-position: 95% 7%
# Using Python in the console
## 💙 Start a Python session
With `repl_python()`:
```r
> repl_python()
Python 3.7.3 (C:\PROGRA~3\ANACON~1\python.exe)
Reticulate 1.12.0.9003 REPL -- A Python interpreter in R
>>>
```
---
background-image: url(pics/reticulated_python.png)
background-position: 95% 7%
# Using Python in the console
## 🐍 repl_python()
```r
> repl_python()
*Python 3.7.3 (C:\PROGRA~3\ANACON~1\python.exe)
*Reticulate 1.12.0.9003 REPL -- A Python interpreter in R
>>>
```
* You get a message to tell you what version of Python you're using and where it is
---
background-image: url(pics/reticulated_python.png)
background-position: 95% 7%
# Using Python in the console
## 🐍 repl_python()
```r
> repl_python()
Python 3.7.3 (C:\PROGRA~3\ANACON~1\python.exe)
Reticulate 1.12.0.9003 REPL -- A Python interpreter in R
*>>>
```
* You get a message to tell you what version of Python you're using and where it is.
* `>>>` indicates the Python prompt.
--
We need to use Python 3.
By default, `reticulate` uses the version of Python found on your PATH.
--
If Python 3 is not being used (possibly Mac users) we can change it.
---
background-image: url(pics/reticulated_python.png)
background-position: 95% 7%
# Using Python in the console
## 🐍 Change the Python version.
It is not necessary to do this if you're already using Python 3 but it won't hurt if you do.
End the `repl_python` session:
```r
>>> exit
>
```
--
## 💙
Set the version of Python you want to use.
This needs to be where Anaconda3 installed. In my case:
```r
> use_python("C:/ProgramData/Anaconda3/python.exe")
```
---
background-image: url(pics/reticulated_python.png)
background-position: 95% 7%
# Using Python in the console
## 💙 Check the Python version
Confirm it has been set:
```r
> Sys.which("python")
python
"C:\\PROGRA~3\\ANACON~1\\python.exe"
```
On windows machines the paths will be short paths (8+3 components, no spaces) with \ as the path delimiter.
In my case, it is short for `C:/ProgramData/Anaconda3/python.exe`
So I know I'm using Python 3