Ah, great topic! Let me share a few lesser-known features of R that can boost productivity for advanced users: 1. Environments and scoping rules: Understanding R's scoping rules and how environments work can help you write more efficient and maintainable code. Mastering lexical scoping allows for better organization and control of your variables. 2. Function factories: R allows you to create functions that generate other functions. This powerful technique can be used in various scenarios, such as creating personalized modeling functions or generating dynamic reports. 3. 'tryCatch' function: R's 'tryCatch' function enables you to handle errors in a more controlled manner. By providing custom error handlers, you can gracefully handle exceptional situations and take appropriate actions without halting the execution of your code.

Here are a few lesser-known features of R that can enhance productivity for advanced users: 1. Function currying: This technique allows you to transform a function with multiple arguments into a sequence of nested functions, each taking one argument. By partially applying arguments, you can create new functions that are focused and concise. 2. Non-standard evaluation: R's powerful metaprogramming capabilities enable non-standard evaluation. This allows you to write functions that work on the unevaluated form of their arguments, giving you more flexibility in code generation and manipulation. 3. Parallel computing: Utilizing R's parallel computing capabilities, such as the parallel package or functions like mclapply, can greatly speed up time-consuming computations by distributing the workload across multiple cores or machines, taking advantage of modern hardware.

Great question! Here are a couple of lesser-known R features that can boost productivity for advanced users: 1. Closures: R supports closures, which are functions that remember the environment in which they were created. Closures help with encapsulation and can be used to create powerful functions with internal state. 2. Profiling: R has built-in profiling tools that allow you to analyze the performance of your code. By identifying bottlenecks, you can optimize critical sections and make your code more efficient. 3. 'setdiff' function: Often overlooked, the 'setdiff' function allows you to find the differences between two vectors. This can be incredibly useful in various scenarios, from data manipulation to debugging.