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.
After reading a dataset I will have a look at the first and last few observations (using
tail(), which by default show 6 observations). This gives you an idea of how the dataset looks like, but it doesn’t confirm the structure (for example, which variables are factors). The function
str() provides a good overview of variable types and together with
summary() one gets an idea of ranges, numbers of observations and missing values.
# Define your working directory (folder). This will make # your life easier. An example in OS X: setwd('~/Documents/apophenia') # and one for a Windows machine setwd('c:/Documents/apophenia') # Read the data apo = read.csv('apophenia-example.csv', header = TRUE) # Have a look at the first few and last few observations head(apo) tail(apo) # Check the structure of the data (which variables are numeric, # which ones are factors, etc) str(apo) # Obtain a summary for each of the variables in the dataset summary(apo)
This code should help you avoid the ‘fitting factors as covariates’ pitfall; anyway, always check the degrees of freedom of the ANOVA table just in case.