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Computer Science
Data Science
R Programming for Data Science
1. Introduction to R for Data Science
2. R Language Fundamentals
3. Data Import and Export
4. Data Manipulation with tidyverse
5. Data Visualization with ggplot2
6. Programming Fundamentals in R
7. Statistical Analysis in R
8. Reproducible Research and Communication
9. R Ecosystem and Best Practices
Reproducible Research and Communication
R Markdown Fundamentals
Document Structure
YAML Header Configuration
Markdown Syntax Elements
Document Sections
Code Integration
Code Chunk Options
Inline Code Execution
Code Chunk Naming
Chunk Dependencies
Output Formats
HTML Documents
PDF Documents
Word Documents
Presentation Formats
Dynamic Content
Parameter-driven Reports
Conditional Content
Cross-references
Advanced Features
Bibliography Management
Mathematical Notation
Interactive Elements
Report Customization
HTML Customization
CSS Styling
JavaScript Integration
Interactive Widgets
PDF Customization
LaTeX Integration
Custom Templates
Page Layout Control
Template Development
Custom R Markdown Templates
Package Templates
Interactive Applications with Shiny
Shiny Architecture
Client-Server Model
Reactive Programming Concepts
User Interface Design
Layout Functions
Input Controls
Text Inputs
Numeric Inputs
Selection Inputs
File Inputs
Output Elements
Text Output
Plot Output
Table Output
Server Logic
Reactive Expressions
Render Functions
Event Handling
Input Validation
Reactivity System
Reactive Sources
Reactive Conductors
Reactive Endpoints
Invalidation and Updates
Application Deployment
Local Deployment
Shiny Server
Cloud Deployment Options
Version Control Integration
Git Fundamentals
Repository Concepts
Commit Workflow
Branch Management
RStudio Git Integration
Git Pane Usage
Commit Interface
Diff Viewing
GitHub Integration
Repository Creation
Push and Pull Operations
Collaboration Workflows
Issue Tracking
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