Figure One Lab Course Update: Easy Code Environment Setup

Last week I made a commitment to build a course to help biologists pick up computational skills less painfully. So I thought I would give periodic updates on how that's coming along.

One of the main obstacles for biologists who are completely new to data analysis in R/Python is actually setting up the coding environment. It's quite common to run into package incompatibility issues, R/Python version conflicts, etc., and before you know it, you've already spent a week troubleshooting these issues. You haven't even touched any data yet.

I am now working on a downloadable ZIP file that contains everything students need to setup their R code environment. Students can just:

1. Download and unzip the ZIP file.
2. Double-click on the R project file.
3. Open up a specified R Markdown file.
4. In the R Markdown file, run renv::restore().

These steps should allow students to get the exact R setup that I have in a matter of minutes. This should circumvent many of the usual hiccups that new learners run into when figuring out their coding environment.

I will also provide visual aids/screenshots along the way so that students can double-check that they are following these directions correctly.

Critics may say that setting up the coding environment is an important part of being a fully proficient computational biologist. I completely agree. But I also think that this should not be the first thing people learn. Too many eager learners would get stuck at this step otherwise. Learners have a finite amount of willpower to pick up new computational skills, and I want to be strategic in how I spend that finite resource. Let's learn the actual computational biology analyses first, experience the joy in learning them, then we can always cover good coding practices at a later time.

That's all I have for now! More updates to come.

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