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 head() and 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.