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The Split-Apply-Combine Technique for Machine Learning with R

Introduction Much discussion in the R community has revolved around the proper way to implement the “split-apply-combine”. In particular, I love the exploration of this topic in this blog post. It seems that the “preferred” approach is dplyr::group_by() + tidyr::nest() for splitting, dplyr::mutate() + purrr::map() for applying, and tidyr::unnest() for combining. Additionally, many in the community have shown implementations of the “many models” approach in {tidyverse}-style pipelines, often also using the {broom} package.