Open andrewcstewart opened 5 years ago
Piping is a convenience of twidlr, but here are some of the more important reasons:
predict
to include a data set, avoiding some nasty traps like thesepredict
for models that don't natively support it (like kmeans
)Hope these are a little more convincing!
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 ( notifications@github.com) 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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Just curious, but why would I use this package instead of the following...
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.