> Now I have to put my money where my mouth is. I've offered to visit a > high school and introduce R to some fairly advanced students > participating in a longitudinal 3-year science research class. > > I anticipate keeping things very simple: > --objects and the fact that there is stuff inside them. str(), head(), tail() > --how to get data into R > --dataframes, as I imagine they will mostly be using single, > "rectangular" datasets > --a lot of graphics (I can't imagine that plot(force, acceleration) > is beyond a high-schooler's capability.) > --simple descriptive statistics > --maybe t-tests, chi-square tests, and simple linear regression.
I think those are good topics to cover, but the order is wrong - start with graphics. They are immediately useful and you can start with built in datasets (although I'd recommend finding a package with more interesting/bigger datasets than the base packages). Once you've shown them how to use graphics to understand data you can talk more about how it works - what is a dataframe, how you load data in R, etc. That's the path I follow when I teach R (http://stat405.had.co.nz/, http://vita.had.co.nz/papers/assessment.html), and I find it to be successful at keeping students motivated enough to work through the initial frustrations of learning a new language. R is not too difficult for high-school students to learn, but you need to make sure you provide them with tools to do things that they're interested in - finding interesting problems that they _want_ to solve is most of the battle. Hadley -- Assistant Professor / Dobelman Family Junior Chair Department of Statistics / Rice University http://had.co.nz/ ______________________________________________ R-help@r-project.org mailing list 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.
