If you’re reading this, you’ve either encountered this problem before, or you just got to this article out of curiousity (in which case you probably don’t know what problem I’m talking about).

A few days ago I was given code by a client for a function that, given a path to a patient’s file, generates a useful ID for the patient. I won’t post the actual function, but it was something along the lines of this:

library(stringr)
library(dplyr)

patient_name <- function(path) {
  path_list <- str_split(path, "/") %>% unlist()
  paste(path_list[length(path_list) - 1], path_list[length(path_list)], sep = "_")
}

Given a path some/path/abc/001.txt, this function will return abc_001.txt. So far, so good. (There are better ways to implement this function, but that’s not the point here).

I had a dataframe with file paths as a column, and I needed to add this ID as another column. Normally, this would be easily achieved with a simple mutate() (from dplyr):

df <- data.frame(path = c("some/path/abc/001.txt", "another/directory/xyz/002.txt"),
                 stringsAsFactors = FALSE)
df %>% mutate(patient_name = patient_name(path))

This code does run, but it’s not correct. Here’s the output:

                           path patient_name
1         some/path/abc/001.txt  xyz_002.txt
2 another/directory/xyz/002.txt  xyz_002.txt

You can see the result is incorrect, and it’s because patient_name() is not a vectorized function - it assumes that its input is just a single path, and doesn’t know how to properly work when given multiple paths.

Usually when I write my own code, I try to make my functions vectorized, so that you can call them with both a single element or with a vector. But in this case, I wasn’t allowed to modify the code to make the function vectorized. So how do we easily vectorize patient_name() without modifying its code? We can use the Vectorize() function!

patient_name_v <- Vectorize(patient_name)
df %>% mutate(patient_name = patient_name_v(path))

This actually gives us the correct result:

                           path patient_name
1         some/path/abc/001.txt  abc_001.txt
2 another/directory/xyz/002.txt  xyz_002.txt

This is only one usecase of the Vectorize() function. It can come in handy whenever you need to vectorize a non-vectorized function.

For example, it seems that the nrow() function is not vectorized, because if I try to create a list with two dataframes in it and get the number of rows, I get NULL:

dflist <- list(mtcars, iris)
nrow(dflist)
# NULL

I hypothesized that vectorizing it will do the trick, and it indeed seems to work!

(Vectorize(nrow))(dflist)
# [1]  32 150

It should be made clear that writing your function in a vectorized form to avoid this problem altogether would be the ideal solution.

UPDATE: As is usually the case with useful R functions, there are other packages that have functions to achieve similar things that may be more efficient and flexible for different situations. You should read Jim Hester’s followup on why using purrr functions is safer than this base R function. If you do want to use only base R functions and use Vectorize(), it’s a good idea to read its documentation and take note of its two parameters SIMPLIFY and USE.NAMES that both default to TRUE.