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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.14695 |
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| _version_ | 1866913439175147520 |
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| author | Navarro, Alejandro L. García Koneva, Nataliia Sánchez-Macián, Alfonso Hernández, José Alberto |
| author_facet | Navarro, Alejandro L. García Koneva, Nataliia Sánchez-Macián, Alfonso Hernández, José Alberto |
| contents | 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 However, certain libraries have become outdated, limiting their functionality and performance.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14695 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning Navarro, Alejandro L. García Koneva, Nataliia Sánchez-Macián, Alfonso Hernández, José Alberto Machine Learning Programming Languages 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 However, certain libraries have become outdated, limiting their functionality and performance.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. |
| title | A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning |
| topic | Machine Learning Programming Languages |
| url | https://arxiv.org/abs/2407.14695 |