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Tuesday, June 28 • 5:21pm - 5:39pm
broom: Converting statistical models to tidy data frames

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The concept of "tidy data" offers a powerful and intuitive framework for structuring data to ease manipulation, modeling and visualization, and has guided the development of R tools such as ggplot2, dplyr, and tidyr. However, most functions for statistical modeling, both built-in and in third-party packages, produce output that is not tidy, and that is therefore difficult to reshape, recombine, and otherwise manipulate. I introduce the package “broom," which turns the output of model objects into tidy data frames that are suited to further analysis and visualization with input-tidy tools. The package defines the tidy, augment, and glance methods, which arrange a model into three levels of tidy output respectively: the component level, the observation level, and the model level. These three levels can be used to describe many kinds of statistical models, and offer a framework for combining and reshaping analyses using standardized methods. Along with the implementations in the broom package, this offers a grammar for describing the output of statistical models that can be applied across many statistical programming environments, including databases and distributed applications.

avatar for Jacqueline Meulman

Jacqueline Meulman

Visiting Professor, Stanford University

avatar for David Garrett Robinson

David Garrett Robinson

Heap Analytics
David Robinson is Director of Data Science at Heap Analytics, where he's helping to build the next generation of product analytics technology. He's the co-author with Julia Silge of the tidytext package and the O’Reilly book Text Mining with R. He also created the broom, fuzzyjoin... Read More →

Tuesday June 28, 2016 5:21pm - 5:39pm PDT
McCaw Hall