Reflections on useR! 2022

conference ggtrace

Notes from attending and speaking at my first R conference

June Choe (University of Pennsylvania Linguistics)


I’ve had my eyes on useR! 2022 ever since they announced it - I had my package {ggtrace} that I’d been developing for about half a year by the time calls for abstracts were announced, and thought that it’d make a good submission for my “debut” into the “academic” R world. I’ve built up a bit of a resistance against going to online conferences over the covid years but I’ve never been to an R conference before and I’ve heard good things about the last virtual useR! 2021, so I was actually very excited about the prospect of attending.

In short I’m very very glad I did and it was very educational! It was a great first R conference for me.

Also if you’re wondering why I’m writing this over a month after the conference ended, it’s because I had to immediately switch gears to rstudioconf prep (sorry!).

Submission process

The submission process was incredibly simple,1 and that low barrier to entry was part of the reason that convinced me to apply. The submission form consisted of a 250-word abstract that I typed up into a textbox input field, along with relevant links about my project.2

I also applied for the diversity scholarship, which was a separate form sent to me after I got accepted for a talk. I’ve never applied for these kind of things before, but when it came to R I felt like I could really use this to advocate for myself and others who share my background (students in traditionally “humanities” and “soft science” departments) to convince their programs about the benefits of students being involved in the R community. I’m fortunate enough to be in a program that’s recently been making a lot of effort to incorporate R/programming into doing science,3 but me, being selfish, wanted even more R in my life. The conference generously offered me the diversity scholarship and I am incredibly honored! It covered my registration fee and allowed me to attend two workshops, which I’ll talk about later.

Here’s the timeline of how things went for me:

Walkshops and talks I attended

Here are my notes from the workshops and some of the talks I attended, in no particular order. Keep in mind that I was attending from home in Korea, so I missed a few live talks and didn’t have great notes for others (I’ll be catching up through the recordings).

Recordings are available on the conference youtube channel



R in Teaching

Building the R Community 1

Interfaces with C, C++, Rust, and V

Expanding Tidyverse

Learning ggplot2

  1. Nothing like the 1-2 page length, properly formatted abstracts like in academic conferences.↩︎

  2. I linked to the Github repository and package website.↩︎

  3. For example, our department offered its first data science course for undergrads last year, which I was the head TA for.↩︎

  4. Actually I still haven’t figures out the proper(?) term between dimension/dimension-al/dimension-ality reduction.↩︎

  5. Which makes sense - turns out that fPCA is kinda domain-specific and designed for the analysis of acoustic data specifically.↩︎

  6. A move that the team also pre-empted with semi-customized feedback.↩︎

  7. Big mood - me too.↩︎

  8. It was used for the last few seconds of the video to create the bell sound effect to “show” what the finish times would “sound” like, to emphasize the point that all sprinters are similarly fast in the grand scheme of things.↩︎

  9. When I TA-ed for an undergrad data science course last year, we used Google Colab, and I wondered the pros and cons of that vs. RStudio Cloud.↩︎

  10. When I was a junior, my alma mater Northwestern University started a data science program with a handful of teaching professor hires to focus on educating undergrads.↩︎

  11. Like, do you know what all the possible arguments to geom_histogram() is? You probably don’t and they’re actually pretty hard to find, but you wouldn’t have that problem if you had one function with a bunch of arguments that you can find on just the function’s help page with no redirects.↩︎

  12. And it has a lot of “data lessons” built-in, like you can use it to demonstrate the Simpson’s paradox and k-means clustering.↩︎

  13. In fact, one of the really insightful parts of the talk was Alison talking on the slide “Why has it been so popular?”.↩︎

  14. Which I embarassingly never visited before this talk.↩︎

  15. e.g., Adelie is always capitalized because it’s named after a person, but optional for chinstraps and gentoos. The data itself capitalizes all of them.↩︎

  16. I didn’t even know Twitter Space was a thing before this, despite spending a lot of time on Twitter.↩︎

  17. You can chain + theme()’s.↩︎