Github user HyukjinKwon commented on the issue: https://github.com/apache/spark/pull/17161 @felixcheung, please let me leave my humble opinion. In general, I like this change. Maybe, it should not necessarily be always runnable (at least for now) but I believe it is still an improvement to make them runnable, for example, it allows users to directly copy and paste the example and easily test. To me, this is more like the case that we (at least I) prefer the JIRA having a self-contained reproducer. Personally, I just copy and paste the example and check the input/output when I first learn and try new APIs as a user. > Firstly, I see this as slightly different from Python, in that in R it is common to have built-in datasets and possibly users are used to having them and having examples using them. In case of Python examples, up to my knowledge, it is also common to document self-runnable examples (e.g., [pandas](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot.html#pandas.DataFrame.pivot) and [numpy](https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html)). Moreover, they are tested in doctests so there is no overhead to check if they are runnable everytime. Maybe, let's talk about this in the PR or JIRA (I am sorry for dragging Python one into this). > I have done a pass on the changes in this PR and I'm happy with changing from non-existing json file to mtcars. I'm slightly concerned with the few cases of artificial 3 rows data (like here) - more on that below on small dataset. Maybe, we could take out the examples that you are concerned of for now.. For the four concerns you described, I do agree in general but if it has a downside more than what it improves, I would disagree but to me it might sound the improvement might be better than the downsides about the change itself. >how much work and how much change is it to change all examples (this is only 1 .R out of 20-something files we have, in a total of 300+ methods which is on the high side for R packages) I guess the first one is about reviewing cost/backporting/conflict concern. I am willing to help verify the examples by manually running even if they are so many in terms of reviewing cost if the change is big. > how much churn will it be to keep them up-to-date when we are having changes to API (eg. sparkR.session()); especially since in order to have examples self-contained we tend to add additional calls to manipulate data and thereby increasing the number of references of API calls; which could get stale easily like the example with insertInto For the second concern, if it makes the maintenance header, it'd be a important point but I think we are being conservative on the API changes from Spark 2.x. So, I guess we would less likely need to sweep these. I think it is okay even if it would be change in the near future if the changes are not quite big. > perhaps more importantly, how practical or useful it would be to use built-in datasets or native R data.frame (mtcars, cars, Titanic, iris, or make up some; that are super small) on a scalable data platform like Spark? perhaps it is better to demonstrate, in examples, how to work with external data sources, multiple file formats etc.? > and lastly, we still have about a dozen methods that are without example that are being flagged by CRAN checks (but not enough to fail it yet) For the third and fourth concerns, I think we could improve this by adding more examples, explaining and describing the details. For example, we could leave some comments about this such as .. "this is an example but in pratice blabla..". Maybe, we could do this in another PR maybe. Otherwise, we could leave the examples as they are with some proper comments. For other thoughts, I think it would be great if they are ran in the tests at lesat.
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