if ifelse() had more if’s, AND an else

November 22, 2019
By

[This article was first published on woodpeckR, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Problem

The case_when() function in dplyr is great for dealing with multiple complex conditions (if’s). But how do you specify an “else” condition in case_when()?

Context

Last month, I was super excited to discover the case_when() function in dplyr. But when I showed my blog post to a friend, he pointed out a problem: there seemed to be no way to specify a “background” case, like the “else” in ifelse(). In the previous post, I gave an example with three outcomes based on test results. The implication was that there would be roughly equal numbers of people in each group. But what if the vast majority of people failed both tests, and we really just wanted to filter out the ones who didn’t?

Today, I came across exactly this problem in my research. I’m analyzing morphometric data for about 500 tadpoles, and I made a PCA score plot that looked like this:

Screen Shot 2019-11-22 at 12.48.01 PM

Before continuing my analysis, I wanted to take a closer look at those outlier points, to make sure they represent real measurements and not mistakes in the data. Specifically, I wanted to take a look at these ones:

Screen Shot 2019-11-22 at 12.48.01 PM copy.png

To figure out which tadpoles to investigate, I’d have to pull out their names based on their scores on the PC1 and PC2 axes.

Solution

I decided to add a column called investigate to the PCA scores data frame, set to “investigate” or “ok” depending on whether the observation in question needed to be looked at.

scores <- scores %>% 
          mutate(investigate = case_when(PC1 > 0.2 ~ "investigate",
                                         PC2 > 0.15 ~ "investigate",
                                         PC1 < -0.1 & PC2 > 0.1 ~ "investigate,
                                         TRUE ~ "ok"))

What’s up with that weird TRUE ~ "ok" line at the end of the ​​case_when() statement? Basically, that’s the equivalent of else.  It translates, roughly, to “assign anything that’s left to “ok.”

I’m really not sure why the equivalent of else here is TRUE, and the ​case_when documentation doesn’t really explain it. The only way I figured out that this worked was by reading through the examples in the documentation and noticing that they all seemed to end with this TRUE ~ statement, so I tried it, and voilà. If anyone has an understanding of why this works, under the hood, I’d love to know!

One thing to note is that the order of arguments matters here. If we had started off with the TRUE ~ "ok" statement and then specified the other conditions, it wouldn’t have worked: everything would just get assigned to “ok.”

I’m really glad I figured out how to add an else to case_when()! Before I started using dplyr, I would have attempted this problem like this:

scores$investigate <- "ok" # Create a whole column filled with "ok"
scores$investigate[scores$PC1 > 0.2] <- "investigate"
scores$investigate[scores$PC2 > 0.15] <- "investigate"
scores$investigate[scores$PC1 < -0.1 & scores$PC2 > 0.1] <- "investigate"

Or maybe I would have used some really long and complex boolean statement to get all those conditions in one line of code. Or nested ifelse‘s. But that’s annoying and hard to read. This is so much neater, and saves typing!

Outcome

It turns out that if you read the documentation closely, case_when()is a fully-functioning version of ifelse that allows for multiple if statements AND a background condition (else). The more I learn about the tidyverse, the more I love it.

 

To leave a comment for the author, please follow the link and comment on their blog: woodpeckR.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)