Basics

Introduction to R

• What is R?
• Setting up R environment
• Basic syntax and structure
• Basic data types and structures

Data Manipulation

• Vectors and lists
• Data frames and matrices
• Factors
• Subsetting data

Basic Functions

• Creating and using functions
• Control structures (if, else, for, while)
• Apply family of functions

Data Visualization

• Base graphics
• ggplot2 basics
• Creating plots

Intermediate

Data Cleaning

• Handling missing values
• Data transformation with dplyr
• String manipulation with stringr

Data Import and Export

• Reading data from files
• Writing data to files
• Working with databases

Statistical Analysis

• Descriptive statistics
• Hypothesis testing
• Linear regression
• ANOVA

Working with Dates and Times

• Date and time classes
• Formatting dates and times
• Calculating differences

Advanced

Advanced Data Visualization

• Advanced ggplot2 techniques
• Interactive plots with plotly
• Creating dashboards with Shiny

Machine Learning

• Introduction to machine learning in R
• Supervised learning
• Unsupervised learning
• Model evaluation

Parallel Computing

• Introduction to parallel computing
• Using parallel package
• Optimizing code for parallel execution

Package Development

• Creating your own package
• Documenting your package
• Submitting to CRAN