Description

Introduction

As data science ecosystems evolve, combining the strengths of R and Python unlocks powerful, flexible solutions. This course, “R Shiny with Python Integration: A Cross-Language Workflow,” teaches you how to embed Python code and models within Shiny applications using packages like reticulate. Learn to bridge both languages seamlessly for data manipulation, machine learning, and interactive visualizations in a unified interface.

Prerequisites

• Intermediate knowledge of R and Shiny

• Working experience with Python programming

• Familiarity with data science workflows

• Basic understanding of R packages and environments

Table of Contents

1. Introduction to Cross-Language Workflows
1.1 Why Integrate R and Python?
1.2 Use Cases for Mixed-Language Apps
1.3 Overview of the reticulate Package
2. Setting Up the Environment
2.1 Installing Python for R Users
2.2 Managing Virtual Environments
2.3 Troubleshooting Compatibility Issues
2.4 Best Practices for Environment Setup
3. Using Python in R Scripts and Shiny
3.1 Calling Python Code from R
3.2 Passing Data Between R and Python
3.3 Embedding Python in Shiny Server Functions
3.4 Handling Python Errors in Shiny
4. Data Processing and Analysis Across Languages
4.1 Preprocessing in Python, Visualizing in R
4.2 Using pandas and dplyr Together
4.3 Loading Machine Learning Models in Python
4.4 Integrating NumPy, SciPy, and scikit-learn with Shiny
5. Building Interactive Apps with R and Python
5.1 Shiny UI + Python Logic: Design Patterns
5.2 Creating Dashboards with Dual-Language Backends
5.3 Reactive Outputs from Python Functions
5.4 Displaying Python Plots in R Shiny
6. Case Studies and Real Applications
6.1 Forecasting App Using Prophet (Python) in Shiny
6.2 NLP Sentiment Analysis with spaCy or Transformers
6.3 Financial Modeling with Python + R Visualization
6.4 AI-Powered Recommendation System in Shiny
7. Performance Optimization and Debugging
7.1 Managing Memory Between Languages
7.2 Optimizing Computation with Caching
7.3 Debugging Hybrid Workflows
7.4 Profiling R and Python Components
8. Deployment and Maintenance
8.1 Packaging Cross-Language Apps
8.2 Deployment Considerations with Shiny Server and Docker
8.3 Dependency Management for Production
8.4 CI/CD for Hybrid Apps
9. Future-Proofing and Best Practices
9.1 Keeping Python and R Environments in Sync
9.2 Testing and Validation Strategies
9.3 Structuring Code for Maintainability
9.4 Community Resources and Tooling

This course equips you with the practical skills to integrate R and Python within Shiny apps, combining the best of both ecosystems. By mastering this cross-language workflow, you’ll unlock new capabilities for advanced analytics and interactive, intelligent applications.