I am trying to understand the difference between match and intersect in R. Both return the same output in a different format. Are there any functional differences between both?
match(names(set1), names(set2))
# [1] NA 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 11
intersect(names(set1), names(set2))
# [1] "Year" "ID"
match(a, b) returns an integer vector of length(a), with the i-th element giving the position j such that a[i] == b[j]. NA is produced by default for no_match (although you can customize it).
If you want to get the same result as intersect(a, b), use either of the following:
b[na.omit(match(a, b))]
a[na.omit(match(b, a))]
Example
a <- 1:5
b <- 2:6
b[na.omit(match(a, b))]
# [1] 2 3 4 5
a[na.omit(match(b, a))]
# [1] 2 3 4 5
I just wanted to know if there any other differences between the both. I was able to understand the results myself.
Then we read source code
intersect
#function (x, y)
#{
# y <- as.vector(y)
# unique(y[match(as.vector(x), y, 0L)])
#}
It turns out that intersect is written in terms of match!
Haha, looks like I forgot the unique in the outside. Em, by setting nomatch = 0L we can also get rid of na.omit. Well, R core is more efficient than my guess.
Follow-up
We could also use
a[a %in% b] ## need a `unique`, too
b[b %in% a] ## need a `unique`, too
However, have a read on ?match. In "Details" we can see how "%in%" is defined:
"%in%" <- function(x, table) match(x, table, nomatch = 0) > 0
So, yes, everything is written using match.