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 👇
My secret weapon
Group split is SERIOUSLY POWERFUL.
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()
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. 👇
…And the look on your boss’ face after seeing your first Shiny App. 👇
This is career acceleration.
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