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Getting Started
This is what the ultimate R data analysis workflow looks like
Preface
Full disclaimer, I know the title is a little “clickbaity”, but after years of writing R scripts for data analysis, I believe I’ve come across a solid milestone of what a fully reproducible R workflow should aspire to be. This article is NOT intended for beginners but rather for advanced R users who write functionalized code and may already have personal workflows in managing many scripts. Also, please keep in mind that this article is entirely my opinion, as everyone may have their own notion of an “ultimate” workflow. Hopefully, this article may give you some new ideas!
Imagine you have a long and complex data analysis pipeline with many steps and thus lots of lines of code. After doing some research you realize something in the early stages of your pipeline needs to be fixed. You make the change. You run all the code. You wait because there’s a lot of code. An error message pops up. Crap, your change wasn’t exactly correct. You have to go back and try something different. You rerun the code. You wait. Another error message pops up in a different location because it was dependent on what you had just changed. Crap. You think to yourself, how long is this going to take me, as you buckle down for a long session of coding ahead.