Why is `[` better than `subset`?

Question

When I need to filter a data.frame, i.e., extract rows that meet certain conditions, I prefer to use the `subset` function: subset(airquality, Month == 8 & Temp > 90)Rather than the `[` function: airquality[airquality$Month == 8 & airquality$Temp > 90, ]There are two main reasons for my preference: 1. I find the code reads better, from left to right. Even people who know nothing about R could tell what the `subset` statement above is doing. 2. Because columns can be referred to as variables in the `select` expression, I can save a few keystrokes. In my example above, I only had to type `airquality` once with `subset`, but three times with `[`.So I was living happy, using `subset` everywhere because it is shorter and reads better, even advocating its beauty to my fellow R coders. But yesterday my world broke apart. While reading the `subset` documentation, I notice this section:>Warning>This is a convenience function intended for use interactively. For programming it is better to use the standard subsetting functions like [, and in particular the non-standard evaluation of argument subset can have unanticipated consequences.Could someone help clarify what the authors mean?First, what do they mean by "*for use interactively*"? I know what an interactive session is, as opposed to a script run in BATCH mode but I don't see what difference it should make.Then, could you please explain "*the non-standard evaluation of argument subset*" and why it is dangerous, maybe provide an example?

Answer 1

I am not a super expert, but I have come across the same warnings. Subset generally will create a separate index or data.frame. It may do it recursively (i.e. back into the original data.frame) but it creates separate files and if necessary, merges them. The use of the bracket indexing [ ]. is that the filtering is relational. It creates a temporary relationship which treats the original data.frame as a part of itself. Thus you do what you need to do without creating multiple copies. Much faster. That is probably why the warnings. As long as you have data.frames with less than 100,000 entries (width x nrows), I wouldn't worry.