Learning outcomes
 
By the end of this course, delegates will be able to :
  

• Transform one-off analysis scripts into reusable functions and tools
• Build custom R classes and methods to support specialized analytical workflows
• Design, implement and evaluate S3, S4 and R6 solutions appropriate to different software engineering requirements.
• Build reusable, extensible R APIs using object-oriented programming principles including encapsulation, inheritance and polymorphism.
• Create and extend generic functions and custom methods for new object classes.
• Debug and reason about code execution using knowledge of environments, lexical scoping and lazy evaluation.
• Design functions with rich results that capture, evaluate and manipulate user-supplied expressions safely and predictably.
• Implement tidy-evaluation workflows that support programmable data-analysis pipelines.
• Extend the functionality of functions for new object types
• Use rlang tools to build functions that accept and evaluate variables, expressions and dynamic column names.
• Develop maintainable R code that can be extended by other developers and integrated into larger projects.
• Debug difficult scoping, evaluation and environment related issues with confidence
• Implement R6 classes using public, private and active members and evaluate implications of reference semantics.
• Compare alternative object-oriented approaches and justify design decisions for real-world projects.

• Advanced Functions: Scoping rules (including lexical scope), The … argument, Argument matching
• Function components, parameters versus arguments, closures, environments and function exécution mechanics
• Lazy evaluation and argument evaluation timing.
• Data masking and distinction between data variables and environment variables.
• Defused expressions, expr(), enexpr(), eval(), eval_tidy().
• Quosures and environment capture.
• S3 classes: Introduction to object-oriented programming, Constructing S3 objects, generic functions, method dispatch and creation of new generics
• Creating and using S4 classes, validity checking, slots, initialisation methods and multiple inheritance
• R6 classes: Differences between {R6} and S3/S4, Mutable states, Creating methods, Shallow and deep copies, private fields, active bindings, reference semantics and cloning
• Modifying user argument in functions calls
• Quoting code with quosures
• Using quasi quotation

 
Target Audience
 
This course assumes that participants are comfortable with the fundamentals of R programming. It would be suitable for statisticians, data scientists, quantitative researchers, developers, package authors, Shiny developers and analysts who are moving from exploratory analysis towards production-quality code.

Delegate Feedback

 ““Extremely good teacher, great explanations, funny examples and very flexible in terms of content and time. I got to know a lot of things, that I did not think were possible.”
 
“Material well presented and delivered”
 
”I am not scared of R anymore. It was actually fun!”

“Really great course! Useful content that will greatly benefit me in my future R projects.”