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).

[AndyMcAliley]

Using a make-like tool to automate a process is interesting, and I want to know more about that. The standardized directory structure appealed to me. I've used a similar structure at the outset of projects but had difficulty maintaining it as complexity accumulated, so I want to hear more about strategies for maintaining order over time.

[github-learning-lab[bot]]

Great comments @AndyMcAliley! :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 talk if you'd like)

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.

[AndyMcAliley]

It sounds like targets is designed to integrate with R: it can be called from an R session, it uses the same function-oriented style that R lends itself to, and files are abstracted as R objects. It also lets the dependency graph be visualized, and it manages storage and loading of target files. I don't think make can do any of that as well, if at all.

[hcorson-dosch-usgs]

Yes, you're spot on that those are all big benefits of targets. One of the coolest capabilities of targets as a tool designed for R workflows is how it tracks functions. With make, you specify the file (e.g., .R files or a data file) that a target depends on, and make tracks the file. If the timestamp of a .R file has changed, make considers downstream targets that depend on that file to be out of date, even if the function steps defined within that file haven't changed. targets (like other R-specific pipelining tools) goes one step further, and actually tracks the operations of each function defined within a given .R file. It only marks targets that use a given function as 'out of date' if the actual operations of that function have changed. It ignores any edits to comments, formatting, or whitespace. So that means if you go back into a script to add comments or parameter definitions, it won't trigger a rebuild! 🎉

[AndyMcAliley]

Whoa! Code-aware checking; that is really neat.

[github-learning-lab[bot]]

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