Use Python Chunks in R Markdown

1. Introduction

R Markdown allows you to combine R, Python, text, tables, and visualizations in a single reproducible document. By using the reticulate package, you can execute Python code directly inside an R Markdown (.Rmd) file.

This approach is useful when combining:

R for statistical analysis and visualization
Python for machine learning, AI, and data processing

For example, a data scientist can use R for EDA and visualization and Python for machine learning models within the same report.

2. Prerequisites

Before running Python code in R Markdown, ensure:

Install Required Packages in R

#install.packages("reticulate")
#install.packages("rmarkdown")

Load the package:

library(reticulate)
library(rmarkdown)

3. Verify Python Installation

R Markdown uses Python installed on your machine.

Check Python configuration:

library(reticulate)

#py_config()

If Python is not detected, specify the path manually:

Windows Example

use_python("C:/Users/Admin/Documents/.virtualenvs/r-reticulate/Scripts/python.exe")

4. Structure of an R Markdown Document

An R Markdown document contains:

YAML header
Text/Markdown
Code chunks (R or Python)

6. Working with Sample Data

You can create datasets directly in Python chunks.

Example: Generate Sample Data

import pandas as pd
import numpy as np

np.random.seed(123)

data = pd.DataFrame({
    'Age': np.random.randint(20, 60, 10),
    'Income': np.random.randint(30000, 100000, 10),
    'Malaria_Status': np.random.choice([0,1], 10)
})

print(data.head())

   Age  Income  Malaria_Status
0   22   63710               1
1   48   95647               0
2   54   47747               1
3   58   65662               1
4   37   98861               1

7. Data Visualization in Python Chunks

Python visualization libraries work directly inside R Markdown.

Example: Histogram

import matplotlib.pyplot as plt

plt.hist(data['Age'])
plt.title("Age Distribution")
plt.xlabel("Age")
plt.ylabel("Frequency")
plt.show()

8. Using Seaborn for Visualization

Example using seaborn:

import seaborn as sns
import matplotlib.pyplot as plt

sns.countplot(data=data, x='Malaria_Status')
plt.title("Malaria Status Distribution")
plt.show()

9. Using Built-in Datasets

Example with the diamonds dataset:

import seaborn as sns
import matplotlib.pyplot as plt

diamonds = sns.load_dataset("diamonds")
diamonds.head()

   carat      cut color clarity  depth  table  price     x     y     z
0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43
1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31
2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31
3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63
4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75

sns.countplot(data=diamonds, x="cut")
plt.xticks(rotation=45)

([0, 1, 2, 3, 4], [Text(0, 0, 'Ideal'), Text(1, 0, 'Premium'), Text(2, 0, 'Very Good'), Text(3, 0, 'Good'), Text(4, 0, 'Fair')])

plt.show()

Equivalent R code:

library(ggplot2)
ggplot(diamonds, aes(x = cut)) +
  geom_bar()

10. Passing Data Between R and Python

One major advantage of R Markdown is sharing data between R and Python.

R to Python

R chunk:

x <- c(1,2,3,4,5)
x

[1] 1 2 3 4 5

Python chunk:

print(r.x)

[1.0, 2.0, 3.0, 4.0, 5.0]

Python to R

Python chunk:

y = [10,20,30]
y

[10, 20, 30]

R chunk:

py$y

[1] 10 20 30

11. Chunk Options

Chunk options control execution and output.

Example:

print("Show code and output")

Show code and output

Common options:

Option	Meaning
`echo=TRUE`	Show code
`echo=FALSE`	Hide code
`warning=FALSE`	Hide warnings
`message=FALSE`	Hide messages
`eval=FALSE`	Show code but do not run

Example:

import warnings
warnings.filterwarnings("ignore")

12. Advantages of Python Chunks in R Markdown

Combine R and Python in one document
Reproducible analysis
Easy reporting
Supports machine learning workflows
Ideal for research and academic reports
Integrates statistical modeling and AI

13. Summary

Python chunks in R Markdown allow users to execute Python code directly within .Rmd documents. Through the reticulate package, users can integrate statistical analysis in R with machine learning and AI tools in Python, making it a powerful environment for reproducible research, analytics, and reporting.

Documentation:

R Markdown documentation

reticulate documentation