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A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning

Alejandro L. García Navarro, Nataliia Koneva, Alfonso Sánchez-Macián, José Alberto Hernández

TL;DR

This paper addresses the challenge that many advanced ML and AI libraries are Python-centric while R remains strong in statistics and visualization. It presents reticulate as a practical bridge to run Python code from R, enabling users to import modules, execute Python scripts, and exchange data between languages. The authors provide concrete, code-backed examples spanning NumPy, pandas, Matplotlib, Seaborn, Scikit-Learn, PyTorch, TensorFlow, OpenAI Gym, and BeautifulSoup to illustrate ML, DL, RL, and web-scraping tasks within R workflows. The key contribution is a didactic, end-to-end guide that broadens the data scientist’s toolbox by combining the respective strengths of R and Python, improving productivity and analytical reach in real projects.

Abstract

Python has gained widespread popularity in the fields of machine learning, artificial intelligence, and data engineering due to its effectiveness and extensive libraries. R, on its side, remains a dominant language for statistical analysis and visualization. However, certain libraries have become outdated, limiting their functionality and performance. Users can use Python's advanced machine learning and AI capabilities alongside R's robust statistical packages by combining these two programming languages. This paper explores using R's reticulate package to call Python from R, providing practical examples and highlighting scenarios where this integration enhances productivity and analytical capabilities. With a few hello-world code snippets, we demonstrate how to run Python's scikit-learn, pytorch and OpenAI gym libraries for building Machine Learning, Deep Learning, and Reinforcement Learning projects easily.

A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning

TL;DR

This paper addresses the challenge that many advanced ML and AI libraries are Python-centric while R remains strong in statistics and visualization. It presents reticulate as a practical bridge to run Python code from R, enabling users to import modules, execute Python scripts, and exchange data between languages. The authors provide concrete, code-backed examples spanning NumPy, pandas, Matplotlib, Seaborn, Scikit-Learn, PyTorch, TensorFlow, OpenAI Gym, and BeautifulSoup to illustrate ML, DL, RL, and web-scraping tasks within R workflows. The key contribution is a didactic, end-to-end guide that broadens the data scientist’s toolbox by combining the respective strengths of R and Python, improving productivity and analytical reach in real projects.

Abstract

Python has gained widespread popularity in the fields of machine learning, artificial intelligence, and data engineering due to its effectiveness and extensive libraries. R, on its side, remains a dominant language for statistical analysis and visualization. However, certain libraries have become outdated, limiting their functionality and performance. Users can use Python's advanced machine learning and AI capabilities alongside R's robust statistical packages by combining these two programming languages. This paper explores using R's reticulate package to call Python from R, providing practical examples and highlighting scenarios where this integration enhances productivity and analytical capabilities. With a few hello-world code snippets, we demonstrate how to run Python's scikit-learn, pytorch and OpenAI gym libraries for building Machine Learning, Deep Learning, and Reinforcement Learning projects easily.
Paper Structure (25 sections, 14 figures)

This paper contains 25 sections, 14 figures.

Table of Contents

  1. Introduction
  2. The reticulate Package
  3. Introduction to reticulate
  4. Key features and functionalities
  5. Installation and setup
  6. Practical Implementation
  7. Basic Usage
  8. Importing Python Modules
  9. Running Python Code in R
  10. Accessing Python Objects in R
  11. Data Manipulation
  12. Using Python Libraries Like NumPy and pandas
  13. Converting Data Types Between R and Python
  14. Visualization
  15. Using Python Visualization Libraries
  16. ...and 10 more sections

Figures (14)

  • Figure 1: Importing the NumPy library in R using reticulate.
  • Figure 2: Creating a NumPy array in R using reticulate.
  • Figure 3: Executing Python code directly within an R script using py_run_string.
  • Figure 4: Executing a Python script file from R using py_run_file.
  • Figure 5: Accessing and manipulating a Python list in R.
  • ...and 9 more figures