How about standing in a seat he actually has a good chance of winning, so the leader of the party can be in parliment?

I use R for the vast majority of my data science like tasks, often for the reasons you outline. But when u do switch to python, it is most often because of memory issues.

That said, I also see that arguement for pass by ref. While copy on write might seem like the obvious solution, it does make memory usage less easy to predict. And I think with modern biological datasets with millions of rows, memory usage is still a consideration.

Pass by reference is an interesting point. I absolutely get what you are saying, and side effects on arguments are indeed a big source of difficult to diagnose bugs, and my lecture on mutable vs immutable types is really the one thing my python students struggle with.

Non-standard evaluation is powerful and useful, but good god it can be confusing. All that quoting, deparse,!! Just to get the name of a column in a title. TBH I always struggle to get my head around and have to look it up everytime. Newbies don't stand a chance.

I always think about this when I think of EDI or Data management sections of grants. People are now using LLMs to write these as a clear win. But surely the point of these sections was to be costly.

So if you mean only publish papers where the experiments are correctly concieved, designed, carried out and interpreted, irrepsective of the outcome, then sure.

But I don't think only publishing mammoth, "impactful" pieces, then I think that would make things worse, not better.

Even then, only publishing when you beieve you are ready to change the world, or at least the field, can lead to people sitting on data for years when other people might have the key that makes it make sense, but two and two are never put together.

Perhaps when we move to a different system of judge scientists, then we could think about the size of the minimal publishible unit. Thats what I meant by "unless you are careful".

Being in the right place at the right time, being put on the right project, having the right collaborators, all these things are at least partially out of an early career scientists hands. If only 1 in 20 scientists get to be on one of these better papers in 3.5 years of PhD, what of the other 19?