R workflow fun

· 487 words · 3 minute read
R · resources · roundups

I was excited to see Declutter your R workflow with tidy tools1, a preprint by Zev Ross, Hadley Wickham, and David Robinson, among the many excellent papers in the Practical Data Science for Stats PeerJ collection.2 Workflows, whether data-analysis-related or not, are never a one-size-fits-all matter of course. Among other things, the workflows here differ in scope (heck, one of them doesn’t even use R).

That said, there’s lots to be learned from the processes, mistakes, and hard-wrought wisdom 🦉 of others. So, here’s a roundup of some of the workflow posts that have piqued my interest over the past year or so.3 My only note of caution ⚠️ is to take the word new with a grain of salt— what was once 🆕 is now old hat.

Implementation of a basic reproducible data analysis workflow by Joris Muller

New tools and workflows for data analysis by Jenny Bryan

A new data processing workflow for R by Zev Ross

Efficient R programming: Efficient workflow by Colin Gillespie & Robin Lovelace

My set of packages for (daily) data analysis by Daniel Lüdecke

Teaching the data science process by Balázs Kégl

Reproducible workflows in R by Will Landau

A Git Workflow Walkthrough Series by Jim Vallandingham

represtools: Reproducible research tools by Brian Fannin

  1. Ross Z, Wickham H, Robinson D. (2017) Declutter your R workflow with tidy tools. PeerJ Preprints 5:e3180v1 https://doi.org/10.7287/peerj.preprints.3180v1↩

  2. There are a lot of great resources in there, so I highly recommend giving them a read, or at least a skim, if you’re truly pressed for time.↩

  3. So, really, just workflow related tweets from 🐦 @dataandme put here in a somewhat less ephemeral medium.↩

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