> It is important to note that the benchmark codes are not written for absolute maximal performance (the fastest code to compute recursion_fibonacci(20) is the

> It is important to note that the benchmark codes are not written for absolute maximal performance (the fastest code to compute recursion_fibonacci(20) is the constant literal 6765). Instead, the benchmarks are written to test the performance of identical algorithms and code patterns implemented in each language. This is quite interesting. So potentially the code for other languages are not the best. It would complete the argument then that the "code patterns" is more often than not the most "natural" e.g. it translates well from pseudo-code. When I was trying Julia I keep trying to vectorize code like I do in R and it took me a while to get comfortable with writing loops again. Vectorized code in R can be quite fast, but there is big caveat - you need a lot more memory to do the same task (until ALTEREP makes into R Base) . E.g. say I wanted to find the maximum of `abs(a^2)` in R where `a` is a vector, so I would do `max(abs(a^2)` this creates a temporary vector. In Julia I can do `maximum(x->abs(x^2), a)` and achieve similar (if not better performance) using much less RAM. This is qutie important if you deal with large datasets (e.g. Fannie Mae data at 1.8 billion rows).