Why use a dependency manager?

[github-learning-lab[bot]]

We're asking everyone to invest in the concepts of reproducibility and efficiency of reproducibility, both of which are enabled via dependency management systems such as remake, scipiper, drake, and targets.

Background

We hope that the case for reproducibility is clear - we work for a science agency, and science that can't be reproduced does little to advance knowledge or trust.

But, the investment in efficiency of reproducibility is harder to boil down into a zingy one-liner. Many of us have embraced this need because we have been bitten by issues in our real-world collaborations, and found that data science practices and a reproducibility culture offer great solutions. Karl Broman is an advocate for reproducibility in science and is faculty at UW Madison. He has given many talks on the subject and we're going to ask you to watch part of one of them so you can be exposed to some of Karl's science challenges and solutions. Karl will be talking about GNU make, which is the inspiration for almost every modern dependency tool that we can think of. Click on the image to kick off the video.

:computer: Activity: Watch the above video on make and reproducible workflows up until the 11 minute mark (you are welcome to watch more)

Use a GitHub comment on this issue to let us know what you thought was interesting about these pipeline concepts using no more than 300 words.

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I'll respond once I spot your comment (refresh if you don't hear from me right away).

[cnell-usgs]

The distinction between reproducible vs replicable is interesting because I think these things often are blurred together, but both aspects matter for fully reproducible research

[github-learning-lab[bot]]

Great comments @collnell! :sparkles:

You could consider GNU make to be a great grandparent of the packages we referred to early in this lesson (remake, scipiper, drake, and targets). Will Landau, the lead developer of targets, has added a lot of useful features to dependency management systems in R, and has a great way of summarizing why we put energy into using these tools: "Skip the work you don't need"

We'd like you to next check out a short part of Will's video on targets

:tv: Activity: watch video on targets from at least 7:20 to 11:05 (you are welcome to watch the full main talk from 7:20 to 7:40)

Use a github comment on this issue to let us know what contrasts you identified between solutions in make and what is offered in R-specific tools, like targets. Please use less than 300 words. Then assign your onboarding cohort team member this issue to read what you wrote and respond with any questions or comments.

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When you are satisfied with the discussion, you can close this issue and I'll direct you to the next one.

[cnell-usgs]

R-specific tools have the benefit of using R for everything and keeping it in the same house. scipiper, targets, and other R-based tools also track code changes in the pipeline to trigger rebuilds form the point of change and streamline the process.

[lindsayplatt]

Yes, I love this idea of keeping everything as an R script. Feels so much more familiar and easier to introduce to others!

[github-learning-lab[bot]]

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When you are done poking around, check out the next issue.