https://oppkey.github.io/r-beats-python/

A Quarto website mockup to explore how content written by Midori AI can be used in content marketing.

The concept is to create an interactive community site that highlights areas where R excels over Python in data science and statistics. Community content from webinars or events is mixed in with AI-generated, human edited, technical content.

The concept site provides detailed comparisons and analysis of specific areas where R programming language provides superior capabilities compared to Python. While both languages are excellent tools, R has distinct advantages in statistical computing, research, and specialized domains.

• Home (index.qmd) - Overview of R's strengths and advantages
• About (about.qmd) - Information about the site's purpose and approach

The site features 10 comprehensive blog posts covering different areas where R excels:

1. Statistical Modeling - R's superior statistical modeling capabilities
2. Data Visualization - ggplot2 vs matplotlib comparison
3. Reproducible Research - R Markdown/Quarto vs Jupyter notebooks
4. Academic Research - R's dominance in statistics and research
5. Data Manipulation - dplyr vs pandas comparison
6. Time Series Analysis - R's comprehensive time series tools
7. Bioinformatics - R's Bioconductor ecosystem
8. Finance and Economics - R's quantitative finance tools
9. Social Sciences - R's research tools for social sciences
10. Machine Learning - R's statistical approach to ML

10 blog posts are AI-generated from real-world R/Medicine sessions. The transcipts are AI-generated and combined with session descriptions submitted by the human speaker.

• Detailed Comparisons - Side-by-side analysis of R vs Python approaches
• Code Examples - Working code examples demonstrating R's advantages
• Performance Comparisons - Tables comparing capabilities across domains
• Real-world Applications - Practical examples from research and industry
• Best Practices - Tips for leveraging R's strengths effectively

• Quarto Site - Modern, responsive website built with Quarto
• Custom Styling - Professional CSS design with R-themed colors
• Code Highlighting - Syntax highlighting for R and Python code
• Responsive Design - Mobile-friendly layout
• Search and Navigation - Easy content discovery

• Quarto installed on your system
• R and RStudio (for development)

1. Clone this repository:

   git clone https://github.com/your-username/r-beats-python.git
   cd r-beats-python

2. Install R dependencies using renv:

   # Restore the project environment
   renv::restore()

3. Install Quarto dependencies:

   quarto install

4. Render the site:

   quarto render

5. Preview the site:

   quarto preview

To add new content or modify existing posts:

1. Create new .qmd files in the blog/ directory
2. Follow the existing front matter format
3. Use the established structure and styling
4. Test locally before committing

This project uses renv for reproducible R package management:

• Restore environment: renv::restore() - Installs all packages from renv.lock
• Add new packages: renv::install("package_name") - Installs and records new packages
• Snapshot changes: renv::snapshot() - Updates renv.lock with current environment
• Check status: renv::status() - Shows differences between lock file and current environment

The .Rprofile file automatically activates the renv environment when you open the project in RStudio.

The site uses the following key Quarto configuration:

• Theme: Cosmo with custom CSS
• Navigation: Left sidebar with table of contents
• Code Execution: Enabled with echo and eval options
• Output Formats: HTML with PDF and Word options

The site includes custom CSS (styles.css) with:

• R-themed color scheme
• Professional typography
• Responsive design
• Custom components for comparisons
• Print-friendly styles

• Objective Analysis - Present facts and practical comparisons
• Evidence-based - Support claims with examples and data
• Balanced Approach - Acknowledge both languages' strengths
• Educational Focus - Aim to inform, not advocate

• Include working R and Python code
• Provide clear explanations
• Use realistic datasets
• Show practical applications
• Include performance comparisons

Each blog post follows a consistent structure:

1. Introduction - Overview of the topic
2. R's Approach - Detailed R examples and capabilities
3. Python's Limitations - Areas where Python falls short
4. Performance Comparison - Side-by-side comparison table
5. Key Advantages - Summary of R's strengths
6. Conclusion - Wrap-up and next steps changes locally before submitting

The site can be deployed to GitHub Pages:

1. Push to GitHub repository
2. Enable GitHub Pages in repository settings
3. Set source to GitHub Actions
4. Site will build and deploy automatically

For Netlify deployment:

1. Connect repository to Netlify
2. Set build command: quarto render
3. Set publish directory: _site
4. Deploy automatically on commits

The site can be deployed to any static hosting platform:

• Vercel
• AWS S3
• Azure Static Web Apps
• Any web server

This site is created by data scientists who appreciate both R and Python but recognize R's unique strengths in statistical computing and research applications.