This is a silly problem that bit me again recently. It’s an elementary mistake that I’ve somehow repeatedly failed to learn to avoid in eight years of R coding. Here’s an example to demonstrate.
Suppose we create a data frame with a categorical column, in this case the heights of ten adults along with their gender.
(heights <- data.frame( height_cm = c(153, 181, 150, 172, 165, 149, 174, 169, 198, 163), gender = c("female", "male", "female", "male", "male", "female", "female", "male", "male", "female") ))
Using a factory fresh copy of R, the gender column will be assigned a factor with two levels: “female” and then “male”. This is all well and good, though the column can be kept as characters by setting
stringsAsFactors = FALSE.
Now suppose that we want to assign a body weight to these people, based upon a gender average.
avg_body_weight_kg <- c(male = 78, female = 63)
Pop quiz: what does this next line of code give us?
Well, the first value of
heights$gender is “female”, so the first value should be 63, and the second value of
heights$gender is “male”, so the second value should be 78, and so on. Let’s try it.
avg_body_weight_kg[heights$gender] # male female male female female male male female female male # 78 63 78 63 63 78 78 63 63 78
Uh-oh, the values are reversed. So what really happened? When you use a factor as an index, R silently converts it to an integer vector. That means that the first index of “female” is converted to 1, giving a value of 78, and so on.
The fundamental problem is that there are two natural interpretations of a factor index – character indexing or integer indexing. Since these can give conflicting results, ideally R would provide a warning when you use a factor index. Until such a change gets implemented, I suggest that best practice is to always explicitly convert factors to integer or to character before you use them in an index.