Yes, that is right. But even though they ultimately boil down to an O(n) time complexity, it can still have a significant performance impact if your arrays are massive, as you said. In a way, I'm not really misleading beginners here because it's the truth of the matter.
But for most cases, one does not need to worry about these performance impacts. It really is just an unnecessary optimization.
I don't believe that you make any comments about .map using more space in your article do you? You also don't comment and say this is only worth doing in one step if your arrays are massive. That's why I suggest that you are misleading beginners. Your example is contrived. For this article to be really useful, you should use a really big array as an example and demonstrate the performance benefits of moving all your operations into the reducer. At the same time, you should speak about the code using significantly less space and that's why it's faster.
Ah, I see now. There's a reason why I didn't add the #beginners tag for this article. It is exactly for the reason you stated. I wasn't targeting beginners for this article. I was assuming that those who read it will most likely know how to optimize their algorithms themselves with what they learned from this article as a guide on how to do so. I suppose that was wrong of me to assume.
Also, I was actually thinking about discussing the space inefficiency of long chains of array methods, but I felt that doing so would have caused me to stray away from the actual message I was trying to convey throughout the whole article: longer chains mean more iterations.
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