[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120316-en":3,"doc-seo-120316-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120316,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",6,"Technology","A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning","Python’s popularity in machine learning, AI, and data engineering stems from its mature ecosystem and extensive libraries, while R remains central for statistical analysis and visualization. Some Python and R libraries can become outdated, limiting performance and capabilities for specific workflows. This paper explains how to use R’s reticulate package to call Python from an R environment, enabling a unified workflow that pairs Python’s advanced ML/AI capabilities with R’s statistical tools. Practical examples demonstrate running scikit-learn, PyTorch, and OpenAI Gym for ML, deep learning, and reinforcement learning projects.","arXiv :2407 . 14695v 1 [ cs .LG] 19 Jul 2024  \nA COMPREHENSIVE GUIDE TO COMBINING R AND PYTHON CODE FOR DATA SCIENCE, MACHINE LEARNING AND REINFORCEMENT LEARNING  \nAlejandro L. García Navarro, Nataliia Koneva, Alfonso Sánchez-Macián, José Alberto Hernández  \nDepartamento de Ingeniería Telemática  \nUniversidad Carlos III de Madrid, Spain  \n{agnavarr,[nkoneva}@pa.uc3m.es](nkoneva}@pa.uc3m.es) , {alfonsan,[jahgutie}@it.uc3m.es](jahgutie}@it.uc3m.es)  \nABSTRACT  \nPython 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 However, certain libraries have become outdated, limiting their functionality and performance.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.  \nKeywords Python · R · reticulate · Machine-Learning · Reinforcement Learning · Scikit-learn · Pytorch · OpenAI Gym  \n1 Introduction  \nIn 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] .  \nIn 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.  \nIn the AI/ML field, it often happens that many libraries and open-source code examples appear in Python, and it takes some time until libraries are ported to R. Therefore, R programmers have it difficult to start using new AI/ML libraries developed in Python. On the other hand, R is well-known to provide thousands of libraries dedicated to statistical analysis and tests, many of them not yet written in Python.  \nThis paper explores the reticulate package [9], which acts as a bridge between R and Python, allowing programmers to use both languages within a single workflow. This makes it easier for programmers to combine Python’s cutting-edge machine-learning capabilities with R’s statistical tools, hence creating a more versatile environment. By showing practical examples with code snippets, we aim to demonstrate how to combine the best of both worlds. Such hello-world code snippets show clear examples for using classical Machine Learning, Deep Learning and Reinforcement Learning libraries like scikit-learn, pytorch and OpenAI Gym.  \nA Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning  \nThe structure of the paper is as follows: Section 2 provides an introduction to the package, highlighting its key features and walking through the installation process. Section 3 presents some examples, including code snippets; and finally, Section 4 concludes the pape","cbCaicHI1sNZH9X9","https://ap.wps.com/l/cbCaicHI1sNZH9X9","pdf",278766,1,12,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 The reticulate Package\n## 2.1 Introduction to reticulate\n## 2.2 Key features and functionalities\n## 2.3 Installation and setup\n# 3 Practical Implementation\n## 3.1 Basic Usage\n## 3.1.1 Importing Python Modules","[{\"question\":\"What is the main purpose of using reticulate in this paper?\",\"answer\":\"Reticulate is used as a bridge to call Python code from an R environment, allowing both languages to be used within a single workflow.\"},{\"question\":\"Which benefits are highlighted when combining R and Python?\",\"answer\":\"The paper emphasizes using R for statistical analysis and visualization while leveraging Python’s machine-learning libraries and capabilities, along with easier data transfer between the ecosystems.\"},{\"question\":\"What libraries does the paper show running through the R-to-Python integration?\",\"answer\":\"The examples mention scikit-learn, PyTorch, and OpenAI Gym, covering machine learning, deep learning, and reinforcement learning use cases.\"}]","A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning | PDF",1785729417,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-comprehensive-guide-to-combining-r-and-python-code-for-data-science-machine-learning-and-reinforcement-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-comprehensive-guide-to-combining-r-and-python-code-for-data-science-machine-learning-and-reinforcement-learning/120316/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main purpose of using reticulate in this paper?","Question",{"text":76,"@type":77},"Reticulate is used as a bridge to call Python code from an R environment, allowing both languages to be used within a single workflow.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which benefits are highlighted when combining R and Python?",{"text":81,"@type":77},"The paper emphasizes using R for statistical analysis and visualization while leveraging Python’s machine-learning libraries and capabilities, along with easier data transfer between the ecosystems.",{"name":83,"@type":74,"acceptedAnswer":84},"What libraries does the paper show running through the R-to-Python integration?",{"text":85,"@type":77},"The examples mention scikit-learn, PyTorch, and OpenAI Gym, covering machine learning, deep learning, and reinforcement learning use cases.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,114,119,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":29,"slug":122},8,"Research & Report","research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]