woodpeckR

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:

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:

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.

Problem

Especially in a project with many different scripts, it can be challenging to keep track of all the packages you need to load. It’s also easy to lose track of whether or not you’ve incorporated package loading into the script itself until you switch to a new computer or restart R and all of a sudden, your packages need to be re-loaded.

Context

When I was first starting out in R, I learned quickly to load packages all together at the top of a script, not along the way as I needed them. But it took a while, until I started using R Projects, before I decided to centralize package loading above the script level. I was sick of having to deal with loading the right packages at the right times, so I decided to just streamline the whole thing.

Solution

Make a separate R script, called “libraries.R” or “packages.R” or something. Keep it consistent. Mine is always called “libraries,” and I keep it in my project folder.

It looks something like this (individual libraries may vary, of course):

Then, at the top of each analysis script, I can simply source the libraries script, and all the libraries I need load automatically.

Outcome

I can easily load libraries in the context of a single R Project, keep track of which ones are loaded, and not have to worry about making my scripts look messy with a whole chunk of library() commands at the top of each one. It’s also straightforward to pop open the “libraries” script whenever I want to add a new library or delete one.

Problem

The ifelse() function only allows for one “if” statement, 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 on the same level, 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):

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 hate nesting functions. It’s confusing and hard to read. 

Continue reading “if ifelse() had more if’s”

Problem

How do I initialize an empty list for use in a for-loop or function?

Context

Sometimes I’m writing a for-loop (I know, I know, I shouldn’t use for-loops in R, but sometimes it’s just easier. I’m a little less comfortable with apply functions than I’d like to be) and I know I’ll need to store the output in a list. Once in a while, the new list will be similar in form to an existing one, but more often, I just need to start from scratch, knowing only the number of elements I want to include.

This isn’t a totally alien thing to need to do––it’s pretty familiar if you’re used to initializing empty vectors before for-loops. There’s a whole other debate to be had about whether or not it’s acceptable to start with a truly empty vector and append to it on every iteration of the loop or whether you should always know the length beforehand, but I’ll just focus on the latter case for now.

Anyway, initializing a vector of a given length is easy enough; I usually do it like this:

> desired_length <- 10 # or whatever length you want
> empty_vec <- rep(NA, desired_length)

I couldn’t immediately figure out how to replicate this for a list, though. The solution turns out to be relatively simple, but it’s just different enough that I can never seem to remember the syntax. This post is more for my records than anything, then.

Continue reading “Initializing an empty list”