Question: when would I use this?

[andrewcstewart]

Just curious, but why would I use this package instead of the following...

df %>%
    lm(y ~ x, data = .)

I'm reading https://drsimonj.svbtle.com/a-tidy-model-pipeline-with-twidlr-and-broom and trying to figure out what twidlr is doing differently.

[drsimonj]

Piping is a convenience of twidlr, but here are some of the more important reasons:

• It provides a consistent data-frame format for all models (where many require different formats like data frames, matrices, vectors, etc)
• It allows formula method even in cases that it's not natively enabled
• It requires predict to include a data set, avoiding some nasty traps like these
• It lets you use predict for models that don't natively support it (like kmeans)

Hope these are a little more convincing!

[andrewcstewart]

Aha, I see. I saw reference to this lib in the context of a tinymodels workflow and was trying to figure out the relationship. I’ll def play around with this. Thanks!

On December 15, 2018 at 3:11:30 AM, Dr Simon Jackson ( [email protected]) wrote:

Piping is a convenience of twidlr, but here are some of the more important reasons:

• It provides a consistent data-frame format for all models (where many require different formats like data frames, matrices, vectors, etc)
• It allows formula method even in cases that it's not natively enabled
• It requires predict to include a data set, avoiding some nasty traps like these https://gist.github.com/drsimonj/5b2cfc428fce350676db5dc77c059052
• It lets you use predict for models that don't natively support it (like kmeans)

Hope these are a little more convincing!

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