There seems to be a difference between levels and labels of a factor in R.
Up to now, I always thought that levels were the 'real' name of factor levels, and labels were the names used for output (such as tables and plots). Obviously, this is not the case, as the following example shows:
df <- data.frame(v=c(1,2,3),f=c('a','b','c'))
str(df)
'data.frame': 3 obs. of 2 variables:
$ v: num 1 2 3
$ f: Factor w/ 3 levels "a","b","c": 1 2 3
df$f <- factor(df$f, levels=c('a','b','c'),
labels=c('Treatment A: XYZ','Treatment B: YZX','Treatment C: ZYX'))
levels(df$f)
[1] "Treatment A: XYZ" "Treatment B: YZX" "Treatment C: ZYX"
I thought that the levels ('a','b','c') could somehow still be accessed when scripting, but this doesn't work:
> df$f=='a'
[1] FALSE FALSE FALSE
But this does:
> df$f=='Treatment A: XYZ'
[1] TRUE FALSE FALSE
So, my question consists of two parts:
-
What's the difference between levels and labels?
-
Is it possible to have different names for factor levels for scripting and output?
Background: For longer scripts, scripting with short factor levels seems to be much easier. However, for reports and plots, this short factor levels may not be adequate and should be replaced with preciser names.
Best Answer
Very short : levels are the input, labels are the output in the
factor()
function. A factor has only alevel
attribute, which is set by thelabels
argument in thefactor()
function. This is different from the concept of labels in statistical packages like SPSS, and can be confusing in the beginning.What you do in this line of code
is telling to R that there is a vector
df$f
The factor function will look for the values a, b and c, convert them to numerical factor classes, and add the label values to the
level
attribute of the factor. This attribute is used to convert the internal numerical values to the correct labels. But as you see, there is nolabel
attribute.