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I completely agree - while I love using the command-line for most tasks, R is ideally designed for this... and rightly so, since that's the whole point of the l
by chimeracoder 14y ago
I completely agree - while I love using the command-line for most tasks, R is ideally designed for this... and rightly so, since that's the whole point of the language!
R also supports the use of atomic operations on vectors (it automatically maps the operation over the vector), and other idioms that would be liabilities in other programming languages, but hugely beneficial in this one area.
I still haven't found a language that treats reading (and then processing) a CSV file more easily than R.
Conveniently, R should be really easy to pick up for someone familiar with working with a POSIX shell or Bash. For example, to see all variables defined in the current namespace, just type ls().
R is basically the POSIX mentality applied specifically to data processing, instead of general-purpose work.
- myg204 14y agoOne exception though. Piping unix command this way allow handling of _very_ large file (stream model), whereas R requires loading the whole content in memory.
- dredmorbius 14y agoThat depends on the statistical moments you're calculating. For sums and means, yes. For percentiles, standard deviation, and other moments, you'll need to operate on the set as a whole, which either means re-reading the dataset or processing it in memory. That said, I've got my own univariate statistics generator written in awk that can handle sets with millions of values readily on desktop/laptop hardware.
- dredmorbius 14y agoI'd disagree that R is trivial to pick up. I find a lot of impedance crossing tool boundaries, and much of R is written by (and for) advanced stats use, making trivial application somewhat daunting. Which isn't to say that it's not a phenomenally powerful and capable tool. I'm just referring to its accessibility.