notes in a shoebox

Quantum Forest

Category: pitfalls

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 record of exactly which observations were dropped to create a particular data set. I have done this in a variety of ways, but this time I opted for code that looked like:

R pitfall #3: friggin’ factors

I received an email from one of my students expressing deep frustation 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:

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 one-way anova (using lm()) the variable will be used as a covariate rather than as a factor.

There is a series of steps that I follow to make sure that I am using the right variables (and types) when running a series of analyses. I always define the working directory (using setwd()), so I know where the files that I am reading from and writing to are.

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