Figure One Lab Course Update: Posit Cloud + Kajabi, Bare Minimum R

Here is another update on my progress in building Figure One Lab online courses designed to help biologists pick up computational skills less painfully.

Posit Cloud + Kajabi

Thanks to feedback from several people last week, and especially to Veronika Romero’s suggestion, I have decided to offer all of the coding elements of Figure One Lab courses via Posit Cloud, which is essentially RStudio in a browser. This resolves all the pain around R version conflicts, package incompatibility issues, operating system differences, etc. I tried Posit Cloud for the first time last week, and it is intuitive and pleasant to use!'

In Posit Cloud, I can start an R project for each course, convert each lesson for that course into a Quarto document, invite students to join the R project, and be assured that every student will be able to work off of the same exact set of lessons. All necessary R versions and packages are pre-set in the R project by me, and students who join my R project will automatically have the same exact R set up as I do. I can even monitor the progress of each student in real time as they code directly in the Quarto documents.

Another reason why I love this setup in Posit Cloud is that it actually resembles the interface that working computational biologists use daily. It feels more authentic of an experience than any of my previous setups.

All the other elements that make online courses fun and effective - discussion forums, live sessions, tracking student progress, comments, video/audio recordings, digital downloads, etc. - will be offered through Kajabi, a platform designed specifically for hosting online courses. Most of these elements are not possible to offer via Posit Cloud as part of a cohesive experience.

So, for now, Posit Cloud + Kajabi seem to be a workable combination for delivering Figure One Lab course content.

Bare Minimum R

I also made some progress in developing the first course in the Figure One Lab collection of courses. But before I describe the course, I want to start with some motivation.

Have you every come across a document called something like "Introduction to R" that ended up being 100+ pages long? I'm sure it was an excellent introduction, but we'll never know because most people who got their hands on it probably didn't read it. It's just too much information all at once. Therefore, very little learning actually happened, even though someone had put in a lot of work to produce that content. I want to avoid that mistake.

So I have decided to call the first Figure One Lab course "Bare Minimum R". In writing the lessons for this course, I adhered to one central tenet: If a piece of content is not absolutely necessary, skip it. Avoid burdening the student with too much information.

The goal of the course is to get a biologist with little to no experience coding in R to independently clean up an actual, messy biological dataset (not the "titanic" or "iris" datasets that hold little interest for biologists) and create some informative, exploratory plots with it. In crafting the lessons for this course, I have painstakingly removed anything that a biologist does not absolutely have to learn in order to reach the goal.

In this way, I hope to get a biologist "addicted" to using R to plot their own data. I hope to replace the use of Excel and GraphPad in their current data analysis practice. I hope to help them achieve greater satisfaction with their current work and build confidence in their ability to learn more advanced computational analysis skills in R.

For now, here are the main sections of the course:

• R basics: logical vectors, subsetting matrices, dataframes, etc.
• dplyr
• ggplot2
• Exploratory data analysis of CCLE, a large cancer cell line bulk RNA-seq dataset

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That's all I have for now! More updates to come in the weeks ahead. I aim to have "Bare Minimum R" available by January 1, 2025.

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