In every activity, knowing something about it allows you to avoid repeating the mistakes of the past. There are non-statistical uses of programming languages, so you could use it for domains you are familiar with. Or you could see some intriguing statistical analysis and study in that area to understand it so you can apply it. The difficulty in such ad-hoc approaches to learning is that it can be inefficient and leave big holes in your knowledge. Of course, you may have limited options at this point, so inefficient may be better than not at all. To minimize the risk of missing a significant point, you should try to be thorough in your self-study and use expert consultation if you are unsure. (This list is not a good venue for purely theoretical questions, but such venues like stats.stackexchange.com or your local university do exist.)
However, please don't apply R like a magic answers box, because you can mislead others and cause harm. -- Sent from my phone. Please excuse my brevity. On May 31, 2016 12:22:59 AM PDT, Prasad Kale <prasad.prasad.k...@gmail.com> wrote: >Hi, > >I am very new to R and just started learning R. But i am not from >statistical background so can i learn R or to learn R statistical >background is must. > >Please guide. > >Thanks in Advance >Prasad > > [[alternative HTML version deleted]] > >______________________________________________ >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
