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Software dependencies can often be a double edged sword. On one hand they
let you take advantage of others' work, giving your software marvelous new
features and reducing bugs. On the other hand they can change, causing your
software to break unexpectedly and increasing your maintenance burden. These
problems occur everywhere, in R scripts, R packages, Shiny applications and
deployed ML pipelines.
So when should you take a dependency and when should you avoid them?
Well, it depends!
This talk will show ways to weigh the pros and cons of a given dependency and
provide tools for calculating the weights for your project. It will also
provide strategies for dealing with dependency changes, and if
needed, removing them. We will demonstrate these techniques with some real life
cases from packages in the tidyverse and r-lib.