Factors in R: more than an annoyance?

Question

One of the basic data types in R is factors. In my experience factors are basically a pain in the ass and I never use them. I always convert to characters. I feel oddly like I'm missing something. Are there a lot of functions that use factors as grouping variables? When should I be using factors? Do you use them?

Answer 1

What a snarky title!

I believe many estimation functions allow you to use factors to easily define dummy variables... but I don't use them for that.

I use them when I have very large character vectors with few unique observations. This can cut down on memory consumption, especially if the strings in the character vector are longer-ish.

PS - I'm joking about the title. I saw your tweet. ;-)

Answer 2

You should use factors. Yes they can be a pain, but my theory is that 90% of why they're a pain is because in read.table and read.csv, stringsAsFactors() is true (and most users miss this subtly). I say they are useful because model fitting packages like lme4 use factors and ordered factors to differentially fit models and determine the type of contrasts to use. And graphing packages also use them to group by. ggplot and most model fitting functions coerce character vectors to factors, so the result is the same. However, you end up with warnings in your code:

> lm(Petal.Length ~ -1 + Species, data=iris)

Call:
lm(formula = Petal.Length ~ -1 + Species, data = iris)

Coefficients:
    Speciessetosa  Speciesversicolor   Speciesvirginica  
            1.462              4.260              5.552  

> iris.alt <- iris
> iris.alt$Species <- as.character(iris.alt$Species)
> lm(Petal.Length ~ -1 + Species, data=iris.alt)

Call:
lm(formula = Petal.Length ~ -1 + Species, data = iris.alt)

Coefficients:
    Speciessetosa  Speciesversicolor   Speciesvirginica  
            1.462              4.260              5.552  

Warning message:
In model.matrix.default(mt, mf, contrasts) :
  variable 'Species' converted to a factor
> 

One tricky thing is the whole drop=TRUE bit. In vectors this works well to remove levels of factors that aren't in the data. For example:

> s <- iris$Species
> s[s == 'setosa', drop=TRUE]
 [1] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[11] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[21] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[31] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[41] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
Levels: setosa
> s[s == 'setosa', drop=FALSE]
 [1] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[11] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[21] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[31] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[41] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
Levels: setosa versicolor virginica
> 

However, with dataframes, the behavior of [.data.frame() is different: see this email or ?[.data.frame (in backticks, which StackOverflow won't let me escape). Using drop=TRUE on dataframes does not work as you'd imagine:

> x <- subset(iris, Species == 'setosa', drop=TRUE)  # susbetting with [ behaves the same way
> x$Species
 [1] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[11] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[21] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[31] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
[41] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
Levels: setosa versicolor virginica
> 

Luckily you can drop factors easily with:

> x <- subset(iris, Species == 'setosa', drop=TRUE)
> levels(x$Species)
[1] "setosa"     "versicolor" "virginica" 
> x$Species <- factor(x$Species)
> levels(x$Species)
[1] "setosa"

or:

> x$Species <- x$Species[drop=TRUE]
> levels(x$Species)
[1] "setosa"

This is how to keep levels you've selected out from getting in ggplot legends.

Internally, factors are integers with an attribute level character vector (see attributes(iris$Species) and class(attributes(iris$Species)$levels)), which is clean. If you had to change a level name (and you were using character strings), this would be a much less efficient operation. And I change level names a lot, especially for ggplot legends. If you fake factors with character vectors, there's the risk that you'll change just one element, and accidentally create a separate new level.

Answer 3

Factors are fantastic when one is doing statistical analysis and actually exploring the data. However, prior to that when one is reading, cleaning, troubleshooting, merging and generally manipulating the data, factors are a total pain. More recently, as in the past few years a lot of the functions have improved to handle the factors better. For instance, rbind plays nicely with them. I still find it a total nuisance to have left over empty levels after a subset function.

#drop a whole bunch of unused levels from a whole bunch of columns that are factors using gdata
require(gdata)
drop.levels(dataframe)

I know that it is straightforward to recode levels of a factor and to rejig the labels and there are also wonderful ways to reorder the levels. My brain just cannot remember them and I have to relearn it every time I use it. Recoding should just be a lot easier than it is.

R's string functions are quite easy and logical to use. So when manipulating I generally prefer characters over factors.

Answer 4

ordered factors are awesome, if I happen to love oranges and hate apples but don't mind grapes I don't need to manage some weird index to say so:

d <- data.frame(x = rnorm(20), f = sample(c("apples", "oranges", "grapes"), 20, replace = TRUE, prob = c(0.5, 0.25, 0.25)))
d$f <- ordered(d$f, c("apples", "grapes", "oranges"))
d[d$f >= "grapes", ]

Answer 5

Here's a graphic I made for class to explain the differences:

But 99% of the time, character vectors will serve you just as well as factors.