If ifelse() had more if’s

In which I discover dplyr’s case_when() function, a vectorized version of ifelse().

Figure 1: Source: Interesting Engineering (https://interestingengineering.com/video/make-your-very-own-arduino-based-color-candy-sorting-machine)

This post has been modified from its original form on woodpeckR.

Problem

The ifelse() function only allows for one “if” statement, and therefore two cases. You could add nested “if” statements, but that’s just a pain, especially if the 3+ conditions you want to use are all equivalent, conceptually. Is there a way to specify multiple conditions at the same time?

Context

I was recently given some survey data to clean up. It looked something like this (but obviously much larger):

dat <- data.frame(name = c("Kaija", "Ella", "Andis"),
                  test1 = c(FALSE, TRUE, TRUE),
                  test2 = c(FALSE, FALSE, TRUE))
dat

   name test1 test2
1 Kaija FALSE FALSE
2  Ella  TRUE FALSE
3 Andis  TRUE  TRUE

I needed to classify people in this data set based on whether they had passed or failed certain tests.

I wanted to separate the people into three groups:

People who passed both tests: Group A

People who passed one test: Group B

People who passed neither test: Group C

I thought about using a nested ifelse() statement, and I certainly could have done that. But that approach didn’t make sense to me. The tests are equivalent and not given in any order; I simply want to sort the people into three equal groups. Any nesting of “if” statements would seem to imply a hierarchy that doesn’t really exist in the data. Not to mention that I don’t usually like nesting functions–I find it confusing and hard to read.

Solution

Once again, dplyr to the rescue! I’m becoming more and more of a tidyverse fan with each passing day.

Turns out, dplyr has a function for exactly this purpose: case_when(). It’s also known as “a general vectorised if,” but I like to think of it as “if ifelse() had more if’s.”

Here’s the syntax:

library(dplyr) # load dplyr

dat <- dat %>% # using the dplyr/magrittr pipe
  mutate(grp = case_when(test1 & test2 ~ "A", # both tests: group A
                         xor(test1, test2) ~ "B", # one test: group B
                         !test1 & !test2 ~ "C" # neither test: group C
  ))

# Show the result:
dat

   name test1 test2 grp
1 Kaija FALSE FALSE   C
2  Ella  TRUE FALSE   B
3 Andis  TRUE  TRUE   A

Let me translate the above into English:

After loading the package, I reassign dat, my data frame, to a modified version of the old dat. Then (%>%), I use the dplyr::mutate function to add a new column called grp. The contents of the column will be defined by the case_when() function.

case_when(), in this example, took three conditions. The condition is on the left side of the ~, and the resulting group assignment (in my case, A, B, or C) is on the right.

I used logical operators for my conditions. The newest one to me was the xor() function, which is an exclusive or: only one of the conditions in the parentheses can be TRUE, not both.

Outcome

Easily make conditional assignments within a data frame. This function is a little less succinct than ifelse(), so I’m probably not going to use it for applications with only two cases, where ifelse() would work fine. But for three or more cases, it can’t be beat. Notice that I could have added any number of conditions to my case_when() statement, with no other caveats.

I love this function, and I think we should all be using it.

NOTE: case_when() has some tricky behavior when it comes to cases you don’t explicitly assign to an outcome. That is to say, the else part of this vectorized ifelse function is a little confusing. I discuss the else more in the next installment of this post.