For anyone interest in R without a background in statistics: I would highly recommend learning the two in parallel (if not statistics first).
R is first and foremost a language for statistical computing. You really aren't going get much out of it without working on some interesting data/stats problems. Plus for most hacker types I think being able to play with the statistics you're learning about with R can be a great learning aid.
However not only is it beneficial to learn stats with R, it is imho dangerous to learn R without some stats. There's already too much research being published with were 'p-value' means "the thing that t.test() output that I was told needs to be in the paper".
Because R lets you play so freely with stats I find it a great tool to gain greater intuition about certain mathematical principles, but there is a temptation to let the tool do the work and the thinking for you.
R is first and foremost a language for statistical computing. You really aren't going get much out of it without working on some interesting data/stats problems. Plus for most hacker types I think being able to play with the statistics you're learning about with R can be a great learning aid.
However not only is it beneficial to learn stats with R, it is imho dangerous to learn R without some stats. There's already too much research being published with were 'p-value' means "the thing that t.test() output that I was told needs to be in the paper".
Because R lets you play so freely with stats I find it a great tool to gain greater intuition about certain mathematical principles, but there is a temptation to let the tool do the work and the thinking for you.