Encourage use of same datasets across implementations about lost-stats.github.io HOT 7 OPEN

I think my previous post left too much ambiguity: My strong intention is to keep everything in Rdatasets rock stable, and to only remove/modify data if the underlying package does it too, or if I am asked directly.

So far, this has only happened once or twice in many years, and not recently. So I wouldn't worry about the examples you posted. They should still work.

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I definitely agree with this. I can add something to the Contributing page (although I'm not sure how closely that gets read anyway).

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I agree, and this has generally been my approach. (Although in some cases I've used the nice sklearn built-in data generation functions like make_regression because they make it really easy to demonstrate something with a generated dataset.)

Agree about the problematic page; it could be better. When I was doing Python versions of some geospatial pages, I took one look at that and thought 'no'.

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@NickCH-K Okay, cool. I'll let you update then. Feel free to close this when you do. I think people will get the idea once we manage to set the standard across a couple pages.

@aeturrell Ya, I agree it can be tough to balance with benefits of generated datasets. On that note, I just saw in commit log that you added a Python SVM example (nice!). Do you know of a good off-the-shelf dataset that we could use here?

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FWIW, Julia uses its own mirror of Rdatasets (which is extremely out of date now). I think that John Myles White, who originally forked it years ago, was worried about stability. This was reasonable at the time because he was one of the very first people to discover Rdatasets.

But I think that overall it's been pretty stable. Since I stopped actively contributing to Python statsmodels, I think that Josef has only contacted me once about a problem, and we got it fixed pretty quickly.

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Oh, thanks for the heads-up @vincentarelbundock. I should double check that the example I used above actually works then! Do you think there's any chance of them updating it (have you contacted them?) I'm happy to if not...

EDIT: Scratch that, I've just read that they only want to bundle some example and that any more would have to be added manually. I'll see about putting in a PR.

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Do you know of a good off-the-shelf dataset that we could use here?

Good question. To replace what's there, we'd want linear and non-linear binary classification with balanced classes. A quick look at the sklearn datasets (which is also where the make_dataset generation functions are), suggests this might work for simple binary classification with almost balanced classes. I haven't checked Rdatasets though.

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