Notes on Learning R

While I continue to focus on developing my ability to code in general by working with Python, R, the computing language, makes a lot of statistical analysis much simpler, as Jonathan Goodwin once pointed out to me. I don’t have time right now to delve too deeply into R, but I do want to begin to collect things that might be useful. This post is a bit of a laundry list, but I am also on planning on using a tag for R, [R-lang][] going forward.

* [DataCamp][] currently offers a great deal of introductory R modules for free. (Thank you, DataCamp.) Like Codecademy, and I’m sure there are others, it offers a very sleek online interface, where lessons and assignments are provided in a lefthand pane, and a place to write code and a console are stacked on the right so that you can write your code as if you were writing a script and then running that script at the command line. DataCamp enhances this particular model with some nice videos here and there — I haven’t worked enough yet to know what the balance is.
* [Revolutions][] is a blog dedicated to news and information of interest to members of the R community.

See also: [Notes on Machine Learning][]. (It’s hard to know where one arena ends and another begins, but given that R and Python are languages with which many individuals and organizations implement forms of machine learning, it seemed wise, at this stage in my own education, to have a separate category for machine learning, respecting, as it were, the one level [at least?] of abstraction.)

[R-lang]: http://johnlaudun.org/tag/R-lang
[DataCamp]: https://www.datacamp.com
[Revolutions]: http://blog.revolutionanalytics.com/
[Notes on Machine Learning]: http://johnlaudun.org/20140809-machine-learning-notes/

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