I'm not a python or R dev (Scala dev actually whose day job consists substantially of re-factoring python and R code written by data scientists and data analyst

I'm not a python or R dev (Scala dev actually whose day job consists substantially of re-factoring python and R code written by data scientists and data analysts to run on the JVM on prod servers). Sure python is easier to grok for up-stream ETL/data processing, but that's commodity work (or it should be anyway) and not a solid basis to compare R vs python. R has far more packages than the "scientific" python portion of pypi and for certain domains the quality (and quantity) of the packages in R makes the better choice; examples: signal processing (or any more-than-routine time-series analysis; seismic interpretation, finance, experimental design, chemo-metrics, etc. And with strict use of the datatables package--coercing dataframes as datatables and using that package's syntax to manipulate your data, R is very fast. Ignore the smack and leave those folks to their "serious" work