Reticulate

class: center, middle, inverse, title-slide

# Reticulate
## RaukR 2021 • Advanced R for Bioinformatics
### <b>Nina Norgren</b>
### NBIS, SciLifeLab

---

exclude: true
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---
name: contents

## Contents

* [Learning outcomes](#LOs)
* [Introduction](#intro)
* [Introducing reticulate](#reticulate)
* [Importing modules](#importing_modules)
* [Sourcing scripts](#sourcing_scripts)
* [Executing Python code](#execute_code)
* [Python in R Markdown](#python_rmarkdown)
* [Type conversions](#type_conversions)
* [Examples](#examples)

---
name: LOs

## Learning outcomes

<br>

In this session we will learn to:

* Understand the concepts needed for running Python in R
* Understand the different object classes in Python and their equivalent in R
* Apply our knowledge to:
  + Import Python functions into R
  + Use R objects as input to Python functions
  + Translate between Python and R objects

---
name: intro

## Introduction

<br><br>
.largest.center[**R versus Python**]
###.center[The ultimate fight!]
--
name: intro2

<br><br><br>
###.center[Not anymore!]

---
name:reticulate

## Introducing reticulate

* Combine Python and R code
* Use R classes in Python functions and vice versa
* Import Python functions into R code and run from R
* Add Python code chunks to markdown documents

```r
library(reticulate)
```

---
name:importing_modules

## Importing Python modules

```r
datetime <- import("datetime")
todays_r_date <- datetime$datetime$now()
```

```r
todays_r_date
class(todays_r_date)
```

```
## [1] "2021-06-14 06:53:28 UTC"
## [1] "POSIXct" "POSIXt"
```

--
Objects are automatically converted to R types, unless otherwise specified

```r
datetime <- import("datetime", convert = FALSE)
todays_py_date <- datetime$datetime$now()
```

```r
todays_py_date
class(todays_py_date)
```

```
## 2021-06-14 08:53:28.930472
## [1] "datetime.datetime"     "datetime.date"         "python.builtin.object"
```

---
## Importing built-in Python functions

Access Python's built-in functions directly in R

```r
builtins <- import_builtins()
r_vec <- c(1, 5, 3, 4, 2, 2, 3, 2)
str(r_vec)
```

```
##  num [1:8] 1 5 3 4 2 2 3 2
```
r_vec is an R object.

```r
builtins$len(r_vec)
builtins$max(r_vec)
```

```
## [1] 8
## [1] 5
```
Python built-in functions still working on R objects

```r
max(r_vec)
```

```
## [1] 5
```
Normal R way

---
name: sourcing_scripts

## Sourcing scripts

Import your own python functions for use in R. File `python_functions.py`:

```python
def add(x, y):
  return x + y
```

--
R code:

```r
source_python("python_functions.py")
class(4)
res <- add(4,5)
res
class(res)
```

```
## [1] "numeric"
## [1] 9
## [1] "numeric"
```
--
Type `numeric` in and type `numeric` out. But what happens in between?

--
File `python_functions.py`:

```python
def add_with_print(x, y):
  print(x, 'is of the python type ', type(x))
  return x + y
```

```r
res2 <- add_with_print(4,5)
py_capture_output(add_with_print(4,5))
str(res2)
```

```
## [1] "4.0 is of the python type  <class 'float'>\n\n"
##  num 9
```

---
name: execute_code

## Execute Python code

Run python string:

```r
py_run_string("result = [1,2,3]*2")
py$result
```

```
## [1] 1 2 3 1 2 3
```
All objects created by python are accessible using the `py` object exported by reticulate

Run python script `my_python_script.py`:

```python
def add(x, y):
  return x + y

def multiply_by_3(x):
  return x*3

def run_all():
  x = 5
  y = 8
  added = add(x, y)
  final = multiply_by_3(added)
  return final

final = run_all()
```

```r
py_run_file("my_python_script.py")
py$final
```

```
## [1] 39
```

---
name: python_rmarkdown

## Python in R Markdown

In R Markdown it is possible to mix in Python chunks:

````
```{python}
import pandas as pd

movies = get_all_movies()
print(type(movies))
``` 
````

```
## <class 'pandas.core.frame.DataFrame'>
```

---
## Python in R Markdown

Access the movie object using the `py` object, which will convert movies to an R object:

```r
movies_r <- py$movies
movies_r <- as_tibble(movies_r)
subset <- movies_r %>% select(5:6, 8:10)
```

---
## Python in R Markdown

Access the movie object using the `py` object, which will convert movies to an R object:

```r
movies_r <- py$movies
movies_r <- as_tibble(movies_r)
subset <- movies_r %>% select(5:6, 8:10)
knitr::kable(subset[1:7,],'html')
```

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> originalTitle </th>
   <th style="text-align:left;"> startYear </th>
   <th style="text-align:right;"> runtimeMinutes </th>
   <th style="text-align:left;"> genres </th>
   <th style="text-align:right;"> averageRating </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Kate &amp; Leopold </td>
   <td style="text-align:left;"> 2001 </td>
   <td style="text-align:right;"> 118 </td>
   <td style="text-align:left;"> Comedy,Fantasy,Romance </td>
   <td style="text-align:right;"> 6.4 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> The Brain That Wouldn't Die </td>
   <td style="text-align:left;"> 1962 </td>
   <td style="text-align:right;"> 82 </td>
   <td style="text-align:left;"> Horror,Sci-Fi </td>
   <td style="text-align:right;"> 4.4 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> The Fugitive Kind </td>
   <td style="text-align:left;"> 1960 </td>
   <td style="text-align:right;"> 119 </td>
   <td style="text-align:left;"> Drama,Romance </td>
   <td style="text-align:right;"> 7.1 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Les yeux sans visage </td>
   <td style="text-align:left;"> 1960 </td>
   <td style="text-align:right;"> 90 </td>
   <td style="text-align:left;"> Drama,Horror </td>
   <td style="text-align:right;"> 7.7 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> À bout de souffle </td>
   <td style="text-align:left;"> 1960 </td>
   <td style="text-align:right;"> 90 </td>
   <td style="text-align:left;"> Crime,Drama </td>
   <td style="text-align:right;"> 7.8 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 13 Ghosts </td>
   <td style="text-align:left;"> 1960 </td>
   <td style="text-align:right;"> 85 </td>
   <td style="text-align:left;"> Horror,Mystery </td>
   <td style="text-align:right;"> 6.1 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> The Alamo </td>
   <td style="text-align:left;"> 1960 </td>
   <td style="text-align:right;"> 162 </td>
   <td style="text-align:left;"> Adventure,Drama,History </td>
   <td style="text-align:right;"> 6.8 </td>
  </tr>
</tbody>
</table>
---
## Python in R Markdown

Continue working with the now converted R object in R

```r
ggplot(movies_r, aes(x=startYear)) + geom_bar() + 
                                     theme(axis.text.x = element_text(angle = 90)) +
                                     ggtitle('Number of movies per year')
```

---
## Python in R Markdown

Continue working with the now converted R object in R

---
name: type_conversions

## Type conversions

When calling python code from R, R data types are converted to Python types, and vice versa, when values are returned from Python to R they are converted back to R types.

####.center[**Conversion table**]

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> R </th>
   <th style="text-align:left;"> Python </th>
   <th style="text-align:left;"> Examples </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Single-element vector </td>
   <td style="text-align:left;"> Scalar </td>
   <td style="text-align:left;"> 1   , 1L  , TRUE, foo </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Multi-element vector </td>
   <td style="text-align:left;"> List </td>
   <td style="text-align:left;"> c(1.0, 2.0, 3.0), c(1L, 2L, 3L) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> List of multiple types </td>
   <td style="text-align:left;"> Tuple </td>
   <td style="text-align:left;"> list(1L, TRUE, &quot;foo&quot;) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Named list </td>
   <td style="text-align:left;"> Dict </td>
   <td style="text-align:left;"> list(a = 1L, b = 2.0), dict(x = x_data) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Matrix/Array </td>
   <td style="text-align:left;"> NumPy ndarray </td>
   <td style="text-align:left;"> matrix(c(1,2,3,4), nrow=2, ncol=2) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Data Frame </td>
   <td style="text-align:left;"> Pandas DataFrame </td>
   <td style="text-align:left;"> data.frame(x = c(1,2,3), y = c(&quot;a&quot;,&quot;b&quot;,&quot;c&quot;)) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Function </td>
   <td style="text-align:left;"> Python function </td>
   <td style="text-align:left;"> function(x) x +1 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Raw </td>
   <td style="text-align:left;"> Python bytearray </td>
   <td style="text-align:left;"> as.raw(c(1:10)) </td>
  </tr>
  <tr>
   <td style="text-align:left;"> NULL, TRUE, FALSE </td>
   <td style="text-align:left;"> None, True, False </td>
   <td style="text-align:left;"> NULL, TRUE, FALSE </td>
  </tr>
</tbody>
</table>
---
## Type conversions

`python_functions.py`:

```python
def check_python_type(x):
  print(type(x))
  return x
```

```r
source_python("python_functions.py")

r_var <- matrix(c(1,2,3,4),nrow=2, ncol=2)
class(r_var)
py_capture_output(check_python_type(r_var))
r_var2 <- check_python_type(r_var)
class(r_var2)
```

```
## [1] "matrix" "array" 
## [1] "<class 'numpy.ndarray'>\n\n"
## [1] "matrix" "array"
```

```r
source_python("python_functions.py", convert=FALSE)

r_var <- matrix(c(1,2,3,4),nrow=2, ncol=2)
class(r_var)
py_capture_output(check_python_type(r_var))
r_var2 <- check_python_type(r_var)
class(r_var2)
r_var3 <- py_to_r(r_var2)
class(r_var3)
```

```
## [1] "matrix" "array" 
## [1] "<class 'numpy.ndarray'>\n\n"
## [1] "numpy.ndarray"         "python.builtin.object"
## [1] "matrix" "array"
```
---
## Type conversions

- `42` in R is a floating point number. In Python it is an integer

```r
str(42)
check_python_type(42)
py_capture_output(check_python_type(42))
```

```
##  num 42
## 42.0
## [1] "<class 'float'>\n\n"
```
--

```r
str(42L)
check_python_type(42L)
py_capture_output(check_python_type(42L))
```

```
##  int 42
## 42
## [1] "<class 'int'>\n\n"
```

---
## Type conversions

- List conversions of single element vectors, automatically translated to Python scalar

```r
str(c(24))
check_python_type(c(24))
py_capture_output(check_python_type(c(24)))
```

```
##  num 24
## 24.0
## [1] "<class 'float'>\n\n"
```
--

```r
str(list(24))
check_python_type(list(24))
py_capture_output(check_python_type(list(24)))
```

```
## List of 1
##  $ : num 24
## [24.0]
## [1] "<class 'list'>\n\n"
```

---
name: examples

## Examples

---
name: end-slide
class: end-slide, middle
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# Thank you. Questions?

<p>R version 4.1.0 (2021-05-18)<br><p>Platform: x86_64-w64-mingw32/x64 (64-bit)</p><p>OS: Windows 10 x64 (build 19042)</p><br>

Built on : <i class='fa fa-calendar' aria-hidden='true'></i> 14-Jun-2021 at <i class='fa fa-clock-o' aria-hidden='true'></i> 08:53:32

<b>2021</b> • [SciLifeLab](https://www.scilifelab.se/) • [NBIS](https://nbis.se/) • [RaukR](https://nbisweden.github.io/workshop-RaukR-2106/)