user2016

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.