
Careless comparison bites back (again)
When running stats labs I like to allocate a slightly different subset of data to each student, which acts as an incentive for people to do their own work (rather than copying the same results from a fellow student). We also need to be able to replicate the results when marking, so we need a…

R pitfall #3: friggin’ factors
I received an email from one of my students expressing deep frustration with a seemingly simple problem. He had a factor containing names of potato lines and wanted to set some levels to NA. Using simple letters as example names he was baffled by the result of the following code: lines

R pitfall #1: check data structure
A common problem when running a simple (or not so simple) analysis is forgetting that the levels of a factor has been coded using integers. R doesn’t know that this variable is supposed to be a factor and when fitting, for example, something as simple as a oneway anova (using lm()) the variable will be…