5.6 Factors

Factors are used to represent categorical data. Factors can be ordered or unordered and are an important class for statistical analysis and for plotting.

Factors are stored as integers, and have labels associated with these unique integers. While factors look (and often behave) like character vectors, they are actually integers under the hood, and you need to be careful when treating them like strings.

In the data frame we just imported, let’s create a factor.

# a new function, factor()
# it takes a vector and converts it into a factor
cats$coat <- factor(cats$coat)

str(cats)

We can see the names of the multiple columns. And, we see that coat is a Factor w/ 5 levels

Once created, factors can only contain a pre-defined set values, known as levels. By default, R always sorts levels in alphabetical order.

You can check this by using the function levels(), and check the number of levels using nlevels():

levels(cats$coat)
nlevels(cats$coat)

Sometimes, the order of the factors does not matter, other times you might want to specify the order because it is meaningful (e.g., “low”, “medium”, “high”) or it is required by particular type of analysis. Additionally, specifying the order of the levels allows to compare levels:

satisfaction <- factor(c("low", "high", "medium", "high", "low", "medium", "high"))
levels(satisfaction)
satisfaction <- factor(satisfaction, levels = c("low", "medium", "high"))
levels(satisfaction)
min(satisfaction) ## doesn't work

## Error in Summary.factor(structure(c(1L, 3L, 2L, 3L, 1L, 2L, 3L), .Label = c("low", : 'min' not meaningful for factors

satisfaction <- factor(satisfaction, levels = c("low", "medium", "high"), ordered = TRUE)
levels(satisfaction)
min(satisfaction) ## works!

In R’s memory, these factors are represented by numbers (1, 2, 3). They are better than using simple integer labels because factors are self describing: "low", "medium", and "high"" is more descriptive than 1, 2, 3. Which is low? You wouldn’t be able to tell with just integer data. Factors have this information built in. It is particularly helpful when there are many levels (like the species in our example data set).

5.6.1 Converting factors

If you need to convert a factor to a character vector, simply use as.character(x).

Converting a factor to a numeric vector is however a little trickier, and you have to go via a character vector. Compare:

f <- factor(c(1, 5, 10, 2))
as.numeric(f)               ## wrong! and there is no warning...
as.numeric(as.character(f)) ## works...
as.numeric(levels(f))[f]    ## The recommended way.