Another great feature is the ability to use the apply family of functions, such as lapply, sapply, and vapply. These functions allow you to apply a function to each element of a list or vector, making it easier to work with complex data structures and saving you from writing repetitive code.

One lesser-known feature in R that can greatly improve code efficiency is the use of vectorization. Instead of using loops, R allows you to perform operations on entire vectors or matrices at once, resulting in much faster execution times.

R also has a powerful caching mechanism called memoization. It allows you to cache the results of expensive computations, so that the next time you call the same function with the same arguments, the result is retrieved from the cache instead of being recomputed. This can drastically speed up computations in certain scenarios.

Lastly, R has a feature called lazy evaluation. This means that expressions are not evaluated until they are absolutely needed. This can be advantageous in scenarios where you have large datasets or complex calculations, as it allows you to avoid unnecessary computations until the results are actually required.