To extract the (unique) levels of a factor, use levels:
levels(factor(c(1, 2, 3, 2, 3, 2, 3)))
## [1] "1" "2" "3"
Note: the levels of a factor are always character:
class(levels(factor(c(1, 2, 3, 2, 3, 2, 3))))
## [1] "character"
To obtain just the number of levels, use nlevels:
nlevels(factor(c(1, 2, 3, 2, 3, 2, 3)))
## [1] 3
If the factor contains only integers, we can use unclass to convert it (back) to an integer class vector:
unclass(factor(c(1, 2, 3, 2, 3, 2, 3)))
## [1] 1 2 3 2 3 2 3
## attr(,"levels")
## [1] "1" "2" "3"
Note: The “levels” attribute is still being reported but the new object is not a factor.
But if the factor contains other numeric values, we can get unexpected results:
unclass(factor(c(1, 2, 1.5)))
## [1] 1 3 2
## attr(,"levels")
## [1] "1" "1.5" "2"
We might have expected this to produce a numeric vector of the form c(1,2,1.5)
Instead, we have obtained an integer class vector of the form c(1,3,2)
This is because the factors levels reflect the ordering vector values, not their actual values
We can see this at work if we unclass a factor that was created from a character vector:
unclass(factor(c("a", "b", "a")))
## [1] 1 2 1
## attr(,"levels")
## [1] "a" "b"
The result is an integer vector: c(1,2,1)
This can be especially confusing if we create a factor from a combination of numeric and character elements:
unclass(factor(c("a", "b", 1, 2)))
## [1] 3 4 1 2
## attr(,"levels")
## [1] "1" "2" "a" "b"
The result is an integer vector, c(3,4,1,2), which we can see in several steps:
(1) the numeric values are coerced to character
c("a", "b", 1, 2)
## [1] "a" "b" "1" "2"
(2) the levels of the factor are sorted numerically then alphabetically
factor(c("a", "b", 1, 2))
## [1] a b 1 2
## Levels: 1 2 a b
(3) the result is thus a numeric vector, numbered according to the order of factor levels
unclass(factor(c("a", "b", 1, 2)))
## [1] 3 4 1 2
## attr(,"levels")
## [1] "1" "2" "a" "b"
Changing factors is similar to changing other types of data, but has some unique challenges We can see this if we compare a numeric vector to a factor version of the same data:
a <- 1:4
b <- factor(a)
a
## [1] 1 2 3 4
b
## [1] 1 2 3 4
## Levels: 1 2 3 4
We can see in the way that the two variables are printed that the numeric and factor look different This is also true if we use indexing to see a subset of the vector:
a[1]
## [1] 1
b[1]
## [1] 1
## Levels: 1 2 3 4
If we try to change the value of an item in the numeric vector using positional indexing, there's no problem:
a[1] <- 5
a
## [1] 5 2 3 4
If we try to do the same thing with the factor, we get a warning:
b[1] <- 5
## Warning: invalid factor level, NA generated
b
## [1] <NA> 2 3 4
## Levels: 1 2 3 4
And the result isn't what we wanted. We get a missing value.
This is because 5 wasn't a valid level of our factor.
Let's restore our b variable:
b <- factor(1:4)
Then we can add 5 to the levels by simply replacing the current levels with a vector of the current levels and 5:
levels(b) <- c(levels(b), 5)
Our variable hasn't changed, but its available levels have:
b
## [1] 1 2 3 4
## Levels: 1 2 3 4 5
Now we can change the value using positional indexing, just like before:
b[1] <- 5
b
## [1] 5 2 3 4
## Levels: 1 2 3 4 5
And we get the intended result
This can be quite useful if we want to change the label for all values at a given level To see this, we need a vector containing repeated values:
c <- factor(c(1:4, 1:3, 1:2, 1))
c
## [1] 1 2 3 4 1 2 3 1 2 1
## Levels: 1 2 3 4
There are four levels to c:
levels(c)
## [1] "1" "2" "3" "4"
If we want to change c so that every 2 is now a 5, we can just change the appropriate level
This is easy because 2 is the second level, but we'll see a different example below:
levels(c)[2]
## [1] "2"
levels(c)[2] <- 5
levels(c)[2]
## [1] "5"
c
## [1] 1 5 3 4 1 5 3 1 5 1
## Levels: 1 5 3 4
Now c is contains 5's in place of all of the 2's
But our replacement involved positional indexing
The second factor level isn't always equal to the number 2, it just depends on what data we have
So we can also replace factor levels using logicals (e.g., to change 5 to 9):
levels(c) == "5"
## [1] FALSE TRUE FALSE FALSE
levels(c)[levels(c) == "5"]
## [1] "5"
levels(c)[levels(c) == "5"] <- 9
levels(c)
## [1] "1" "9" "3" "4"
c
## [1] 1 9 3 4 1 9 3 1 9 1
## Levels: 1 9 3 4
As you can see, factors are a potentially useful way for storing different kinds of data and R uses them alot!