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

TL;DR

This paper demonstrates how integrating R and Python using the reticulate package enhances data science, machine learning, and reinforcement learning workflows by combining the strengths of both languages with practical code examples.

Contribution

It provides a comprehensive guide and practical examples for combining R and Python to improve data analysis and machine learning tasks.

Findings

Seamless integration of R and Python enhances analytical capabilities.

Using reticulate simplifies calling Python libraries from R.

Practical examples show improved productivity in ML and RL 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 However, certain libraries have become outdated, limiting their functionality and performance.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.