Reticulate

RaukR 2023 • Advanced R for Bioinformatics

Nina Norgren

27-Jun-2023

Learning outcomes

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

Introduction

R versus Python The ultimate fight!

Not anymore!

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

library(reticulate)

Importing Python modules

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

todays_r_date
class(todays_r_date)

[1] "2023-08-04 11:00:22 UTC"
[1] "POSIXct" "POSIXt" 

Objects are automatically converted to R types, unless otherwise specified

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

todays_py_date
class(todays_py_date)

datetime.datetime(2023, 8, 4, 11, 0, 22, 351864)
[1] "datetime.datetime"     "datetime.date"         "python.builtin.object"

Importing built-in Python functions

Access Python’s built-in functions directly in 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.

builtins$len(r_vec); builtins$max(r_vec)

[1] 8
[1] 5

Python built-in functions still working on R objects

max(r_vec)

[1] 5

Normal R way

Sourcing scripts

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

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

R code:

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?

Sourcing scripts

But what happens in between?

File python_functions.py:

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

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

Execute Python code

Run python string:

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

Execute Python code

Run python script my_python_script.py:

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()

py_run_file("my_python_script.py")
py$final

[1] 39

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:

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:

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

originalTitle	startYear	runtimeMinutes	genres	averageRating
Kate & Leopold	2001	118	Comedy,Fantasy,Romance	6.4
The Brain That Wouldn't Die	1962	82	Horror,Sci-Fi	4.4
The Fugitive Kind	1960	119	Drama,Romance	7.1
Les yeux sans visage	1960	90	Drama,Horror	7.7
À bout de souffle	1960	90	Crime,Drama	7.8
13 Ghosts	1960	85	Horror,Mystery	6.1
The Alamo	1960	162	Adventure,Drama,History	6.8

Python in R Markdown

Continue working with the now converted R object in 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

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

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.

Conversion table

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
Raw	Python bytearray	as.raw(c(1:10))
NULL, TRUE, FALSE	None, True, False	NULL, TRUE, FALSE

Type conversions

python_functions.py:

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

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" 

Type conversions

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

str(42)
check_python_type(42)
py_capture_output(check_python_type(42))

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

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

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"

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"

Thank you! Questions?

2023 • SciLifeLab • NBIS • RaukR