`match`

returns a vector of the positions of (first) matches of
its first argument in its second.

`%in%`

is a more intuitive interface as a binary operator,
which returns a logical vector indicating if there is a match or not
for its left operand.

`match(x, table, nomatch = NA_integer_, incomparables = NULL)`x %in% table

x

vector or `NULL`

: the values to be matched.
Long vectors are supported.

table

vector or `NULL`

: the values to be matched against.
Long vectors are not supported.

nomatch

the value to be returned in the case when no match is
found. Note that it is coerced to `integer`

.

incomparables

a vector of values that cannot be matched. Any
value in `x`

matching a value in this vector is assigned the
`nomatch`

value. For historical reasons, `FALSE`

is
equivalent to `NULL`

.

A vector of the same length as `x`

.

`match`

: An integer vector giving the position in `table`

of
the first match if there is a match, otherwise `nomatch`

.

If `x[i]`

is found to equal `table[j]`

then the value
returned in the `i`

-th position of the return value is `j`

,
for the smallest possible `j`

. If no match is found, the value
is `nomatch`

.

`%in%`

: A logical vector, indicating if a match was located for
each element of `x`

: thus the values are `TRUE`

or
`FALSE`

and never `NA`

.

`%in%`

is currently defined as
`"%in%" <- function(x, table) match(x, table, nomatch = 0) > 0`

Factors, raw vectors and lists are converted to character vectors, and
then `x`

and `table`

are coerced to a common type (the later
of the two types in R's ordering, logical < integer < numeric <
complex < character) before matching. If `incomparables`

has
positive length it is coerced to the common type.

Matching for lists is potentially very slow and best avoided except in simple cases.

Exactly what matches what is to some extent a matter of definition.
For all types, `NA`

matches `NA`

and no other value.
For real and complex values, `NaN`

values are regarded
as matching any other `NaN`

value, but not matching `NA`

,
where for complex `x`

, real and imaginary parts must match both
(unless containing at least one `NA`

).

Character strings will be compared as byte sequences if any input is
marked as `"bytes"`

, and otherwise are regarded as equal if they are
in different encodings but would agree when translated to UTF-8 (see
`Encoding`

).

That `%in%`

never returns `NA`

makes it particularly
useful in `if`

conditions.

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
*The New S Language*.
Wadsworth & Brooks/Cole.

`pmatch`

and `charmatch`

for (*partial*)
string matching, `match.arg`

, etc for function argument
matching.
`findInterval`

similarly returns a vector of positions, but
finds numbers within intervals, rather than exact matches.

`is.element`

for an S-compatible equivalent of `%in%`

.

`unique`

(and `duplicated`

) are using the same
definitions of “match” or “equality” as `match()`

,
and these are less strict than `==`

, e.g., for
`NA`

and `NaN`

in numeric or complex vectors,
or for strings with different encodings, see also above.

# NOT RUN { ## The intersection of two sets can be defined via match(): ## Simple version: ## intersect <- function(x, y) y[match(x, y, nomatch = 0)] intersect # the R function in base is slightly more careful intersect(1:10, 7:20) 1:10 %in% c(1,3,5,9) sstr <- c("c","ab","B","bba","c",NA,"@","bla","a","Ba","%") sstr[sstr %in% c(letters, LETTERS)] "%w/o%" <- function(x, y) x[!x %in% y] #-- x without y (1:10) %w/o% c(3,7,12) ## Note that setdiff() is very similar and typically makes more sense: c(1:6,7:2) %w/o% c(3,7,12) # -> keeps duplicates setdiff(c(1:6,7:2), c(3,7,12)) # -> unique values ## Illuminating example about NA matching r <- c(1, NA, NaN) zN <- c(complex(real = NA , imaginary = r ), complex(real = r , imaginary = NA ), complex(real = r , imaginary = NaN), complex(real = NaN, imaginary = r )) zM <- cbind(Re=Re(zN), Im=Im(zN), match = match(zN, zN)) rownames(zM) <- format(zN) zM ##--> many "NA's" (= 1) and the four non-NA's (3 different ones, at 7,9,10) length(zN) # 12 unique(zN) # the "NA" and the 3 different non-NA NaN's stopifnot(identical(unique(zN), zN[c(1, 7,9,10)])) ## very strict equality would have 4 duplicates (of 12): symnum(outer(zN, zN, Vectorize(identical,c("x","y")), FALSE,FALSE,FALSE,FALSE)) ## removing "(very strictly) duplicates", i <- c(5,8,11,12) # we get 8 pairwise non-identicals : Ixy <- outer(zN[-i], zN[-i], Vectorize(identical,c("x","y")), FALSE,FALSE,FALSE,FALSE) stopifnot(identical(Ixy, diag(8) == 1)) # }