> x <- c("a","b","c","d")
>
> # 3 elements
> i <- 3; x[0:i]
[1] "a" "b" "c"
>
> # 0 elements
> i <- 0; x[0:i]
character(0)
>
> # with a 1 doesn't work so well:
> i <-0; x[1:i]
[1] "a"

RN: I edited this to restore what I take to be what was intended. Remember! To enter a "<" character, you need to use "&lt;".

R> a<-c(NA,3)
R> a==3
[1] NA TRUE
R> a %in% 3
[1] FALSE TRUE

However, I’m not convinced that the example you give is good behaviour. If I use a vector of TRUE/FALSE values as a subscript, I expect to get a vector whose length is equal to the number of TRUE values. If some of these TRUE/FALSE values are actually NA, the length of vector to return is uncertain (if you think of NA as being unceretainly either TRUE or FALSE). This is not at all the same as a vector of a well-defined length in which some elements are NA. In particular, it’s unlikely that the presence of the NA elements will correctly propagate through subsequent operations to produce a sensible result. So it may be best to signal an error when a logical vector containing an NA is used as a subscript.

The situation is different when the subscript is a vector of integers, some of which are NA. Then the result should be a vector the same length as the subscript, and putting in NA where the subscript is NA correctly represents the uncertainty in the value of that subscript.

It’s interesting that %in% handles NA the way you show, though I’m not sure it’s consistent with R’s general treatment of NA!

a<-c(NA,3)
a[a==3]
# the first element might well be 3, were it not missing!!!!
# but if you want just 3′s, no NA’s:
# and it’s not about subsetting, it’s about comparing with `==`

a[a %in% 3]

#or
b <- na.omit(a)
b[b==3]

# or even [very funny!!!]
a[sapply(a, identical, 3)]

I almost always use %in% instead of == in functions

The effect of 4 versus -4 does seem a bit funny, though individually the definitions of what they do seem reasonably sensible (not necessarily the best way, since many buggy references will be considered valid, but not silly).

Many properties that one might hope for aren’t going to be satisfied by R’s indexing. For instance, one might hope that saving the i’th element in another variable, then setting it to something else, then setting it back to its saved value, would leave the vector unchanged. But of course it won’t if the index is beyond the previous extent of the vector.

I’m reminded of the useful conversational elements in the English-Spanish phrase book.
Do you speak Norwegian?
…
Does anyone here speak Norwegian?
…
I don’t speak Norwegian.

Unfortunately, there seems to be no way to edit comments after they’re submitted, even for me when moderating them.