I was recently asked to give a talk to our graduate school annual conference. I offered several titles and the one they picked was Using R in research. I'm not sure if this was a good idea or not. The graduate school covers PhD students across three areas of the university: social sciences (including psychology), law and business. In addition the students tend to specialize in either qualitative or quantitive research methods, so I was talking to an audience who might know nothing about statistics or a great deal (e.g., several students have completed MSc courses in psychological research methods here or elsewhere).
My solution was try and explain the advantages of R relative to alternatives such as SPSS (probably the most common statistic package in the University). I also focussed a lot on graphical methods and simulation. It seemed to go quite well, but I worry that quite a few members of the audience were overwhelmed by large chunks of it.
I promised to put my slides on my blog - though I am not sure how useful they are to anybody who wasn't there. Without my commentary some (possibly most) of the slides won't make much sense. I spent a good deal of the time talking through exploratory plots of one data set (from Hayden, 2005). I use this example a lot in teaching and it involves a bit of class participating (guessing the origin of the data) - so I won't go into to detail here (lest I spoil it for future students), but you can google the original article if you are curious. I also spent some time on how R works (e.g., object types, assignment, basic modeling, plotting functions). My reasoning was that many of the audience have no familiarity with non-GUI interfaces in software and without explaining the basics of the interface they will not have the faintest clue how R works. For those with some familiarity (e.g., SPSS syntax) the examples were selected to show how powerful R can be for things like exploratory graphics.
Several students ask about resources for learning R. I mentioned some in earlier blog posts, but for psychologists Li and Baron's web resources are a good place to start. The other major resource is probably Quick R, but there are hundreds of other places to look online (depending on what stuff you need most).
