Confusion between factor levels and factor labels

Solution 1:

Very short : levels are the input, labels are the output in the factor() function. A factor has only a level attribute, which is set by the labels argument in the factor() 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

df$f <- factor(df$f, levels=c('a','b','c'),
  labels=c('Treatment A: XYZ','Treatment B: YZX','Treatment C: ZYX'))

is telling to R that there is a vector df$f

• which you want to transform into a factor,
• in which the different levels are coded as a, b, and c
• and for which you want the levels to be labeled as Treatment A etc.

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 no label attribute.

> df <- data.frame(v=c(1,2,3),f=c('a','b','c'))    
> attributes(df$f)
$levels
[1] "a" "b" "c"

$class
[1] "factor"

> df$f <- factor(df$f, levels=c('a','b','c'),
+   labels=c('Treatment A: XYZ','Treatment B: YZX','Treatment C: ZYX'))    
> attributes(df$f)
$levels
[1] "Treatment A: XYZ" "Treatment B: YZX" "Treatment C: ZYX"

$class
[1] "factor"

Solution 2:

I wrote a package "lfactors" that allows you to refer to either levels or labels.

# packages
install.packages("lfactors")
require(lfactors)

flips <- lfactor(c(0,1,1,0,0,1), levels=0:1, labels=c("Tails", "Heads"))
# Tails can now be referred to as, "Tails" or 0
# These two lines return the same result
flips == "Tails"
#[1]  TRUE FALSE FALSE  TRUE  TRUE FALSE
flips == 0 
#[1]  TRUE FALSE FALSE  TRUE  TRUE FALSE

Note that an lfactor requires that the levels be numeric so that they cannot be confused with the labels.