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mlotti / ds-pipelines-1

https://lab.github.com/USGS-R/intro-to-pipelines
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Why use a dependency manager? #5

Closed github-learning-lab[bot] closed 3 years ago

github-learning-lab[bot] commented 3 years ago

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, and drake.

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 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.

reproducible workflows with make

: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.

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

mlotti commented 3 years ago

+1

github-learning-lab[bot] commented 3 years ago

Great comments @mlotti! :sparkles:

You could consider GNU make to be a great grandparent of the packages we asked you to install early in this lesson (drake, remake, and scipiper). The lead of drake 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 drake

reproducible workflows with R drake

:tv: Activity: watch video on drake from at least 3:33 to 7:42 (you are welcome to watch the whole thing)

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 drake. Please use less than 300 words. Then assign your onboarding cohort team member this issue to read what you wrote. When they are done, welcome them to comment with any questions or comments.

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

github-learning-lab[bot] commented 3 years ago

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