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David Robinson

Chief Data Scientist at DataCamp, works in R and Python.

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This weekend I gave a presentation on my broom package for tidying model objects (see my introduction here) at the UP-STAT 2015 conference at SUNY Geneseo. I’m sharing the slides here, along with some highlights below.

I first explained how broom fits with other tidy tools such as dplyr, tidyr and ggplot2 as part of an exploratory data analysis workflow:

Exploratory data analysis

I explored the many small ways that a linear regression object is “messy”, which typify the obstacles of using tidy tools with model objects:

how linear models are "messy"

And I showed how broom made the process easy:

broom's tidy method

I gave an overview of the many kinds of objects broom can tidy:

broom objects table

I also touched on one of the more powerful analyses that broom can enable: namely, combining many models so that they can be compared and contrasted.

combining models

Enjoy the slides!