R for Programmers

Please could you forward these comments to the author? Considering he is offering the pdf for free, he may take errata as well. Thanks!

I took Prof. Matloff's class about a year ago. I recall him giving out extra credit for mistakes found in his materials. Please send in any errors/comments/suggestions you've found, he appreciates feedback and interest. He's more than willing to explain any curiosities in why he chose to do something, a rare trait that makes a good teacher.

What kind of functionalities is she using? I'm working on some articles and an app to help people learn R, especially people who are scared of programming or don't like it. I'm interested in learning what problems R beginners in different fields face. Feel free to drop me a line (see email in profile).

It's an introduction for those already familiar with some of the concepts that have to be extensivly explained in introductions for, say, social scientists.

I have to add, though, that I (as a non-programmer and aspiring social scientist) liked this tutorial much better than many other R tutorials out there. In know a little bit about programming but that was enough to pretty much understand the whole tutorial. (I have the sneaking suspicion that I'm an outlier so don't expect many other social scientists to know about basic CS concepts.)

(And boy am I happy that I now can feel confident ditching SPSS in favor of R. That gruesome SPSS!)

1) Well, machine learning is basically computational statistics, and machine learning is key to a lot of web apps.

2) The graphics in R are pretty good. I've used matlab and matplotlib and gnuplot. I'd say comparable functionality to matplotlib in 2D, with better 3D graphics via rgl (though I guess in that case you'd use a python OpenGL library).

See:

http://addictedtor.free.fr/graphiques/

I don't know about Perl, but if you want to get up and running with R for some basic data analysis, I'd say:

1 week to get used to the syntax and do basic data manipulation such as contructing, editing data frames, reading and writing files etc. You can also get going with the simple graphing functions quite quickly.

1 week to learn the statistical stuff - scatter plotting, correlations, linear regression modelling. Of course, I'm talking about just learning the basic syntax. Doing regression properly could take a long time to learn.

The R Book by Crawley is quite a good introduction, especially if you want to brush up on some basic statistics. It's not very in-depth though, so you might have to move on to more advanced texts after a month or so.

I've used R since 1.4 ~ (2001). Compared to other dynamic languages, I think it's harder than most. Why? Because there's a lot more semantics. There might be R one order of magnitude more keywords for R than for Perl, Ruby or Python (PHP doesn't count :) ). There are two object systems, none of them very intuitive. You need to learn functional programming. 1600 packages and counting means more functions and deep concepts to understand. And last, you need to understand the math of whatvever it is you are using. Forget doing point-and-click, black-box analyses on SPSS.

Downsides include poor programming tools/debugger and pass-by-value. Still, it's one of my favorite languages.