The community and ecosystem around R is rapidly changing and adapting. R has a long and storied history as a niche language for statistics and analysis. Much like those disciplines have entered the mainstream of modern technology enabled businesses, so follows the R ecosystem. Previously laborious tasks are being revamped with new elegant APIs such as Rvest does for scraping (and dplyr for manipulation, and lubridate for date manipulation, etc...)
Performance is also historically an R bugaboo, but with changes to R's copy on write semantics and other optimizations in the base language, current benchmarks show it behaving on par with Python and other dynamic languages (if not even slightly better with tools such as dplyr and data.table.)
The maggritr package's implementation of a "pipe semantic" (often considered the only truly successful implementation of a 'component architecture') and the adoption of the model for tools such as Rvset are really allowing for the functional, vectorized nature of R to shine through. These are really darned exciting times to be a part of this community!
Performance is also historically an R bugaboo, but with changes to R's copy on write semantics and other optimizations in the base language, current benchmarks show it behaving on par with Python and other dynamic languages (if not even slightly better with tools such as dplyr and data.table.)
The maggritr package's implementation of a "pipe semantic" (often considered the only truly successful implementation of a 'component architecture') and the adoption of the model for tools such as Rvset are really allowing for the functional, vectorized nature of R to shine through. These are really darned exciting times to be a part of this community!