A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning
On the map: Deep reinforcement learning methods → Deep RL algorithms and representations
Abstract (arXiv)
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 However, certain libraries have become outdated, limiting their functionality and performance.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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 17 references that are in the map. First posted on arXiv 19 Jul 2024.
Builds on
3 of 17 references.
References · 3
- 2020Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligencecited by 81
- 2021Machine Learning using Stata/Pythoncited by 2
- 2024Bridging Worlds: Achieving Language Interoperability between Julia and Python in Scientific Computingcited by 2
Built on by
Not cited yet.