Detect Relationships With Linear Regression (10 Must-Know Tidyverse Functions #4)

Written by Matt Dancho

This article is part of a R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks.

Group Split and Map are SECRET TOOLS in my data science arsenal. Combining them will help us scale up to 15 linear regression summaries to assess relationship strength & combine in a GT table. Here are the links to get set up 👇

(Click image to play tutorial)

My secret weapon

In fact, I use group_split() almost every day. I use to convert data frames to iterable lists:

  • Shiny Apps (making iterable cards)
  • Modeling (Regression by Sub-Groups)
  • Doing complex group-wise calculations - things you can’t do with group_by()

Let’s check group_split() out. With 3 lines of code, we turn an ordinary data frame into an iterable.

Boring old data frame.

Now we have a list of data frames (i.e. an iterable)

Modeling with Broom

So what can we do with this “iterable”?

How about detect relationships with a Linear Regression Model using Broom’s Glance Function!

And with a little extra work (thanks to Thomas Mock @rstudio & the gt R package), we can create this INSANE TABLE! 💥💥💥

That was ridiculously easy.


But you’re NOT a Wizard yet!

Here’s how to master R programming & save the world Harry Potter Style. 👇

Tidyverse wizard

…And the look on your boss’ face after seeing your first Shiny App. 👇


This is career acceleration.


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