PHphillc73·Feb 24, 2017
I guess I never found the <- syntax odd, because that's all I know. Not using = for assignment kind of makes sense to me, as equals has mathematical connotations.
I always thought there were plenty of options for parallel and high(er) performance computing with R.[1]
Data sets which don't entirely fit in memory can be stored pretty much anywhere and queried. Simplest would be an SQLite database. Formats like Feather[2] and fst[3] are also really useful. There are multiple R interfaces for Redis.[4]
R has a library to interact with the Tensorflow API[5] and many other machine learning services.
"Multiple legacy object semantics" doesn't really mean a lot to me. Knowing nothing else, that's just the way R is, plus I don't think there are that many really strange ways of doing things. Mostly there's a discernable pattern across functions.
So
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unding like a fanboy sure, but as I said I just don't know anything else and while Julia sounds nice (speed, way of the future etc) it doesn't have the breadth of library support, stability or community that R does yet and I don't see a compelling reason for me personally to spend time learning Python.
Not saying R is best to the exclusion of all else, but for data analysis, generalised scripting and some web based reports/reactive apps, I can't find a compelling reason to switch, and I guess don't really understand the criticism of the language.
[1] https://cran.r-project.org/web/views/HighPerformanceComputin...
[2] https://github.com/wesm/feather
[3] https://github.com/fstpackage/fst
[4] https://github.com/antirez/redis-doc/pull/798/files
[5] https://github.com/rstudio/tensorflow/blob/master/README.md