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Yeah but in the end it’s just code. And even better, just R.

The business value comes from the stats guy.

When the R/stats guy quits and you have to figure out which of his 7 notebooks to run in which order and which local files need to be in which local directories to run correctly and which versions of each package are now broken and which code you need to rewrite to fix it you start to realize the value he produced was clicking a lot of buttons in the right order and that overall this doesn't scale at all.

That sounds more like a jupyter notebook/python problem than an R problem.

but otherwise, yes, I see the problem.

The hours I have spent debugging package problems in R would disagree.

I know that pain. That’s why I’m saying avoid it if you can do so.

Yeah, but I meant that because the business value is in the stats, and there is such low quality of stats in the field to begin with, it’s borked no matter what.

There’s no point in fixing it. You can just pretend like you did. But if the stat work is quality, then it’s worth the effort to optimize.

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