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

In recent years, data science and machine learning fields have experienced a rise in the use of Python and R [1, 2]. Python is often regarded as a tool with the greatest amount of libraries and tools designed for machine learning, artificial intelligence, and data engineering. Conversely, R remains a go-to language for statistical analysis and advanced visualization, thanks to packages along the lines of stats [3], caret [4], ggplot2 [5] or shiny [6]. In the evolving landscape of data science, combining multiple programming languages has become a popular strategy to take advantage of the strengths of each. For example, research has explored integrating Julia and Python for scientific computing to use Julia's computational efficiency alongside Python [7]. Similarly, the integration of Stata and Python has been examined to enhance machine learning applications, as shown in [8], which details how Stata's recent integration with Python allows for optimal tuning of machine learning models using Python's scikit-learn library.