tidyverse

A Newbie's Guide to Making A Pull Request (for an R package)

I had the wonderful opportunity to participate in the {tidyverse} Developer Day the day after rstudio::conf2019 officially wrapped up. 1 One of the objectives of the event was to encourage open-source contributor newbies (like me 😄) to gain some experience, namely through submitting pull requests to address issues with {tidyverse} packages. Having only ever worked with my own packages/repos before, I found this was to be perfect opportunity to “get my feet wet”!

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.