Reticulate

RaukR 2025 • R Beyond the Basics

Nina Norgren

09-Jun-2025

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] "2025-06-09 13:58:08 UTC"
[1] "POSIXct" "POSIXt" 

Objects are automatically converted to R types, unless otherwise specified

Importing Python modules

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

todays_py_date
class(todays_py_date)

datetime.datetime(2025, 6, 9, 13, 58, 8, 725467)
[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.

Importing built-in Python functions

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

Sourcing scripts

Import your own python functions for use in R.

R code:

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

[1] "numeric"
[1] 9
[1] "numeric"

Sourcing scripts

Import your own python functions for use in R.

R code:

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

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)
str(res2)

4.0 is of the python type  <class 'float'>
 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

Type conversions

source_python("python_functions.py")

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

[1] "matrix" "array" 
<class 'numpy.ndarray'>
[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)
r_var2 <- check_python_type(r_var)
class(r_var2)
r_var3 <- py_to_r(r_var2)
class(r_var3)

[1] "matrix" "array" 
<class 'numpy.ndarray'>
[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)

 num 42
<class 'float'>
42.0

str(42L)
check_python_type(42L)

 int 42
<class 'int'>
42

Examples in bioinformatics

Random forest classifier

py_require(c("scikit-learn"))

# Import scikit-learn's random forest classifier
sklearn <- import("sklearn.ensemble")
RandomForestClassifier <- sklearn$RandomForestClassifier

# Create a random forest classifier
clf <- RandomForestClassifier(n_estimators=100L)

# Training data (example)
X_train <- matrix(runif(1000), ncol=10)
y_train <- sample(c(0, 1), 100, replace=TRUE)

# Train the model
clf$fit(X_train, y_train)

# Predict on new data
X_test <- matrix(runif(200), ncol=10)
predictions <- clf$predict(X_test)
predictions

RandomForestClassifier()
 [1] 1 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 1

Examples in bioinformatics

ENSEMBL API

py_require(c("ensembl-rest"))

# Load the ensembl_rest library
ensembl_rest <- import("ensembl_rest")

# Fetch gene information for a given gene ID
gene_info <- ensembl_rest$symbol_lookup(species='homo sapiens', symbol='BRCA2')

# Print gene information
gene_info$description

[1] "BRCA2 DNA repair associated [Source:HGNC Symbol;Acc:HGNC:1101]"

Examples in bioinformatics

Biopython sequence analysis

py_require(c("biopython"))

# Import Biopython's SeqIO module
SeqIO <- import("Bio.SeqIO")

# Parse a FASTA file
records <- SeqIO$parse("example.fasta", "fasta")

# Translate each sequence to a protein
translated_proteins <- list()
for (record in reticulate::iterate(records)) {
  translated_proteins[[record$id]] <- record$seq$translate()
}

translated_proteins

$GeneA
Seq('MAIVMGR*KGAR*')

$GeneB
Seq('MRMT*LTSIVAS*')

Thank you!

Questions?

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