๐ฏ Introduction to R Programming
Why R? - Remember "FORCE"
Free โข Open Source โข Robust โข Community-driven โข Extensible
R is a programming language and software environment specifically designed for statistical computing and graphics. Developed by Ross Ihaka and Robert Gentleman in the 1990s, R has become the gold standard for data analysis.
๐ฌ Statistical Computing
R excels at statistical analysis, from basic descriptive statistics to advanced machine learning algorithms. It's built by statisticians, for statisticians.
๐ Data Visualization
Create publication-quality graphics with unparalleled customization. From simple scatter plots to interactive dashboards.
๐ Ecosystem Integration
Seamlessly connects with databases, web APIs, Python, Java, C++, and other tools in your data science workflow.
๐ฏ R's Competitive Advantages
Statistical Powerhouse: Over 19,000 packages covering every statistical method imaginable.
Publication-Quality Graphics: ggplot2 creates stunning visualizations that are publication-ready out of the box.
Reproducible Research: R Markdown integrates code, results, and narrative for transparent, shareable analysis.
Industry Adoption: Used by Google, Facebook, Microsoft, pharmaceutical companies, banks, and academic institutions worldwide.
๐ฅ Complete Installation Guide
๐ Your First R Commands
๐ง R Basics & Fundamentals
๐งฎ Arithmetic & Assignment Operators
๐ Understanding Data Types
๐ Working with Variables
๐ Getting Help in R
๐ Data Types & Structures
VLFMD - R Data Structures
Vectors โข Lists โข Factors โข Matrices โข Data Frames
๐ Working with Vectors
๐ฆ Working with Lists
๐ฒ Working with Matrices
๐ Working with Data Frames
๐ท๏ธ Working with Factors
๐ฆ R Packages & Libraries
๐ฅ Package Installation & Loading
๐ Data Manipulation
dplyr: Grammar of data manipulation
tidyr: Data reshaping and tidying
data.table: High-performance alternative
๐ Visualization
ggplot2: Grammar of graphics
plotly: Interactive plots
lattice: Trellis graphics
๐ Reporting
rmarkdown: Dynamic documents
knitr: Dynamic report generation
shiny: Interactive web apps
๐งน Tidyverse - Modern Data Science
Core dplyr Verbs
Select โข Filter โข Mutate โข Arrange โข Summarise โข Group_by
๐ Tidyverse Data Pipeline
โ๏ธ Functions & Programming
๐ง Creating and Using Functions
๐ Data Visualization with R
๐ฏ Creating Your First Plots
๐ Statistical Analysis in R
Statistical Analysis Workflow - "SPHIT"
Sequencing โข Probability โข Hypothesis โข Inference โข Testing
๐ Descriptive Statistics
๐ฏ Probability Concepts
๐ Statistical Distributions
๐งช Hypothesis Testing Framework
๐ฌ Common Statistical Tests
๐ฏ Choosing the Right Test
Consider: data type (continuous/categorical), sample size, distribution assumptions, and research question.
๐ P-values vs Effect Sizes
P-values indicate statistical significance, but effect sizes show practical significance. Report both!
๐ง Assumptions Matter
Check assumptions before testing: normality, homoscedasticity, independence. Use non-parametric alternatives when violated.
๐ Statistical Analysis Best Practices
- Explore before testing: Use plots and descriptive statistics first
- Check assumptions: Verify test requirements are met
- Report effect sizes: Don't rely solely on p-values
- Correct for multiple testing: Adjust p-values when doing many tests
- Understand your data: Context matters more than statistical significance
๐ Data Import & Export
Common Data Sources - "CSTDE"
CSV โข Spreadsheets โข Text files โข Databases โข Excel
๐ Reading Text Files & CSVs
๐ Working with Excel Files
๐๏ธ Database Connections
๐พ Exporting Data
๐ Data I/O Best Practices
- Always check your data: Use dim(), str(), summary() after importing
- Handle missing values: Specify na.strings parameter appropriately
- Use readr for large files: Faster than base R functions
- Close database connections: Always disconnect when done
- Version control your data: Keep track of data source and modifications
๐งน Data Preprocessing & Cleaning
๐ฏ Data Subsetting Techniques
โ Handling Missing Values
๐ Data Reshaping
๐ Merging & Joining Data
๐ฏ Data Quality Checks
Always validate data after preprocessing: Check dimensions, data types, ranges, and distributions to ensure quality.
๐ Reproducible Workflows
Document all preprocessing steps in scripts. Use functions and pipelines for consistent, repeatable processes.
๐ Tidy Data Principles
Each variable forms a column, each observation forms a row, each type of observational unit forms a table.
๐ Dynamic Reporting with R Markdown
R Markdown Workflow - "KAMP"
Knit โข Analyze โข Markdown โข Pandoc
๐ What is R Markdown?
R Markdown combines the simplicity of Markdown with the power of R to create dynamic, reproducible documents.
Key Components:
- ๐น Markdown: Simple text formatting syntax
- ๐น R Code: Embedded analysis and visualization
- ๐น YAML Header: Document metadata and options
- ๐น Pandoc: Universal document converter
๐ Getting Started with R Markdown
โ๏ธ Markdown Syntax Guide
๐ง R Code Chunks
๐ Output Formats & Customization
๐ Reproducible Research
R Markdown ensures your analysis is reproducible by combining code, results, and narrative in a single document.
๐ Interactive Reports
Create dashboards with flexdashboard, interactive plots with plotly, and dynamic content with parameters.
๐จ Professional Output
Generate publication-ready documents in multiple formats: HTML, PDF, Word, presentations, and websites.
๐ R Markdown Best Practices
- Structure your document: Use clear headers and logical flow
- Name your chunks: Makes debugging and navigation easier
- Set global options: Use setup chunk for consistent formatting
- Cache long computations: Speed up rendering for complex analyses
- Version control: Track both .Rmd source and rendered outputs
๐ Advanced R Programming
โก Performance Optimization
Vectorization, efficient data structures, parallel computing, and memory management for faster code execution.
๐ฆ Package Development
Creating your own R packages with proper documentation, testing, and version control.
๐ Web Integration
APIs, web scraping, Shiny applications, and connecting R to databases and web services.
๐ฌ Advanced Analytics
Machine learning, time series analysis, Bayesian statistics, and big data processing.
๐ Advanced Programming Concepts
๐ Advanced R Development Path
- Performance: Profile first, optimize second - use vectorization and efficient data structures
- Packages: Learn to create and maintain R packages for code reusability
- Integration: Connect R with databases, APIs, and other programming languages
- Best Practices: Version control, testing, documentation, and reproducible research
- Specialization: Focus on specific domains like bioinformatics, finance, or machine learning