beginR

A modular web-based training platform for transitioning from SAS to R programming in data analysis and clinical research

beginR is a comprehensive Flask-based training portal that provides structured, progressive learning modules for data professionals transitioning from SAS to R. The platform emphasizes practical skills development through hands-on exercises, GitHub Copilot integration, and real-world data manipulation scenarios including SDTM programming and quality control procedures.

• Modular: 7 progressive learning modules from RStudio setup to advanced QC and reporting
• Interactive: Progress tracking, downloadable resources, and hands-on exercises
• AI-Enhanced: GitHub Copilot integration throughout the curriculum
• Practical: Real-world data manipulation, SDTM creation, and professional reporting workflows

Project Status

• Version: 1.2.0 (stable)
• Status: ✅ Production-ready for training programs and self-study
• Scope: Complete SAS-to-R transition curriculum with 7 structured modules
• Latest Updates: Enhanced UI, improved server stability, automatic logging, CI/CD integration

Features

• Metadata-driven learning: Module configuration in Python dictionaries with flexible content management
• Progressive curriculum: RStudio setup → data manipulation → joins → dates/text → functions → SDTM → QC/reporting
• Resource management: Downloadable exercises, solutions, templates, and reference materials with organized bonus content
• Progress persistence: localStorage-based tracking across sessions with visual progress indicators and completion animations
• Theme support: Light/dark mode with CSS variables and user preference persistence
• Certificate generation: Automatic PDF certificate generation with completion logging to file-based storage
• Contact system: Secure contact form with file-based message storage (no email dependencies)
• Enhanced UI: Improved alignment, bigger interactive elements, and better visual feedback
• CI/CD Integration: GitHub Actions workflows with automated testing, formatting, and trusted contributor auto-merge
• Responsive design: Mobile-first Bootstrap 5 implementation with cross-device compatibility

Installation

git clone https://github.com/chiara-cattani/beginR.git
cd beginR
pip install -r requirements.txt

Requirements: Python ≥ 3.8. Dependencies include Flask, Jinja2, Werkzeug. Optional: python-dotenv for environment variable management.

For R exercises: R ≥ 4.0, RStudio recommended. Required R packages: dplyr, lubridate, stringr, haven, gt, sdtm.oak.

install.packages(c("dplyr", "lubridate", "stringr", "haven", "gt", "remotes"))
remotes::install_github("pharmaverse/sdtm.oak")

Quick Start

The portal provides multiple launch options:

# Launch the application
python app.py
# or
python run.py

# Robust server startup (recommended)
python start_server.py              # Cross-platform robust startup
start_server_robust.bat            # Windows with auto-restart
start_app.bat                      # Windows simple startup
./start_app.sh                     # Unix/Linux startup

Access the training portal at http://localhost:5000

Navigate the curriculum

1. Start with Module 1: RStudio setup and environment configuration
2. Progress sequentially: Each module builds on previous concepts
3. Use progress tracking: Check off learning objectives as you complete them
4. Download resources: Access exercises, solutions, and reference materials
5. Practice with examples: Work through hands-on coding exercises in RStudio

Core Modules

Module	Topic	Key Skills
1	RStudio & Environment Setup	Installation, envconfig, GitHub Copilot basics
2	Data Manipulation Basics	dplyr fundamentals, tibbles, data types, SAS comparison
3	Joins & Summaries	left_join, group_by, summarise, frequency tables
4	Date & Text Handling	lubridate functions, stringr operations, study day calculations
5	Functions & Macro Translation	Custom R functions, SAS macro conversion, purrr iteration
6	SDTM Programming	sdtm.oak domains, metadata reading, XPT export
7	QC & Reporting	ISO8601 formatting, double-programming QC, gt tables

� Bonus Resources

Supplementary Resources

All bonus materials are organized in the bonus_resources/ folder:

Resource	Description	Module Coverage
sas_to_r_cheatsheet.pdf	SAS→R syntax mapping with practical examples	All modules
copilot_prompt_library.pdf	GitHub Copilot prompt templates and best practices	All modules
report_template.Rmd	Professional RMarkdown template for analysis reports	Module 7
data_manipulation_examples.R	dplyr operations and data wrangling techniques	Modules 2-3
date_text_functions.R	lubridate and stringr practical implementations	Module 4
custom_functions_library.R	Reusable R functions and SAS macro translations	Module 5
sdtm_programming_guide.R	SDTM domain creation with sdtm.oak package	Module 6
qc_validation_toolkit.R	Quality control procedures and validation methods	Module 7

Repository Layout

• app.py — Flask application, module configuration, and routing logic • templates/ — Jinja2 HTML templates with inheritance and component structure • static/css/styles.css — CSS variables, responsive design, and theme management • static/js/main.js — Progress tracking, theme toggle, and interactive features • training_material/ — Structured learning content organized by module • bonus_resources/ — Downloadable materials, templates, and reference files • requirements.txt — Python dependencies and version specifications

Contributing

1. Fork the repository
2. Create a feature branch for your changes
3. Follow coding standards and submit a pull request with clear description
4. For detailed development setup, see CI-CD.md

Roadmap

• Enhanced progress analytics — detailed learning analytics and completion reports
• Interactive coding exercises — in-browser R code execution and validation
• Video integration — embedded tutorial videos for complex concepts
• Community features — discussion forums and peer collaboration tools
• Assessment modules — quizzes and practical evaluations for each module
• Certification pathway — formal completion certificates and skills verification

References

• R for Data Science — Comprehensive R programming resource
• dplyr documentation — Data manipulation reference
• CDISC SDTM Implementation Guide — Clinical data standards
• GitHub Copilot Documentation — AI programming assistance

Citation

Cattani, C. (2025). beginR: Modular Web-Based Training Platform for SAS-to-R Transition. Web application version 1.0.0. https://github.com/chiara-cattani/beginR

License

This project is licensed under the MIT License — see the LICENSE file for details.