This advanced R programming course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools.

Syllabus for advanced course on R programming:

Section 1: Functional Programming

• Introduction to functional programming
• Functions as first-class objects
• Higher-order functions
• Functionals from the purrr package

Section 2: Object-Oriented Programming

• Introduction to object-oriented programming
• S3 and S4 object systems
• Creating and manipulating objects
• Object-oriented design patterns

Section 3: Advanced Data Manipulation

• Working with large datasets
• Data manipulation with data.table and dplyr
• Joining datasets
• Reshaping data with tidyr

Section 4: Advanced Visualization

• Creating interactive visualizations with ggplot2 and plotly
• Visualizing geographic data with maps and leaflet
• Creating dashboards with Shiny

Section 5: Advanced Statistical Modeling

• Bayesian inference with Stan
• Generalized linear models with glm() and lme4
• Mixed-effects models with nlme and lme4
• Nonlinear regression with nls()

Section 6: Parallel Computing

• Introduction to parallel computing
• Parallel computing with base R and parallel
• Parallel computing with doParallel and foreach
• High-performance computing with Rmpi

Section 7: Advanced Programming Techniques

• Memoization
• Profiling and benchmarking
• Code optimization
• Creating and distributing packages

Section 8: Final Project

• Students work on a final project applying advanced R programming techniques to a real-world problem or dataset
• Presentations and feedback