R beats Python! R beats Julia! Anyone else wanna challenge R?

Abstraction: Argument for R's superiority in statistics and data science over Python and Julia

Key points:

• R is written by statisticians for statisticians — statistical correctness and domain conventions baked in by design
• CRAN's massive head start in statistical packages is nearly impossible to replicate because statistics is not Python's or Julia's central mission
• Julia speed comparisons were unfair: they didn't exploit R's vectorization; vectorized random walk in R was 1000x faster than equivalent Julia loop for 1M steps
• Python's GIL prevents true multi-core threading; multiprocessing module is clunky; R has richer parallel computing ecosystem
• For large data manipulation, data.table and dplyr close the performance gap; Rcpp handles serial computation bottlenecks
• Author (Norman Matloff, UC Davis) acknowledges Python's elegance and Julia's promise, but does not see either displacing R for data science

Connections: R Language · Python · Julia · Statistical Computing · Data Science · Parallel Computing

Source: https://matloff.wordpress.com/2014/05/21/r-beats-python-r-beats-julia-anyone-else-wanna-challenge-r/