Kevin Feasel



Matt Dancho shows how to use multidplyr to perform parallel processing on data cleansing activities:

There’s nothing more frustrating than waiting for long-running R scripts to iteratively run. I’ve recently come across a new-ish package for parallel processing that plays nicely with the tidyverse: multidplyr. The package has saved me countless hours when applied to long-running, iterative scripts. In this post, I’ll discuss the workflow to parallelize your code, and I’ll go through a real world example of collecting stock prices where it improves speed by over 5X for a process that normally takes 2 minutes or so. Once you grasp the workflow, the parallelization can be applied to almost any iterative scripts regardless of application.

This is a longer article, but if you’re using dplyr with R today, it’s worth a read.

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