`duplicated()`

determines which elements of a vector or data
frame are duplicates
of elements with smaller subscripts, and returns a logical vector
indicating which elements (rows) are duplicates.

`anyDuplicated(.)`

is a “generalized” more efficient
shortcut for `any(duplicated(.))`

.

`duplicated(x, incomparables = FALSE, …)`# S3 method for default
duplicated(x, incomparables = FALSE,
fromLast = FALSE, nmax = NA, …)

# S3 method for array
duplicated(x, incomparables = FALSE, MARGIN = 1,
fromLast = FALSE, …)

anyDuplicated(x, incomparables = FALSE, …)
# S3 method for default
anyDuplicated(x, incomparables = FALSE,
fromLast = FALSE, …)
# S3 method for array
anyDuplicated(x, incomparables = FALSE,
MARGIN = 1, fromLast = FALSE, …)

x

a vector or a data frame or an array or `NULL`

.

incomparables

a vector of values that cannot be compared.
`FALSE`

is a special value, meaning that all values can be
compared, and may be the only value accepted for methods other than
the default. It will be coerced internally to the same type as
`x`

.

fromLast

logical indicating if duplication should be considered
from the reverse side, i.e., the last (or rightmost) of identical
elements would correspond to `duplicated = FALSE`

.

nmax

the maximum number of unique items expected (greater than one).

…

arguments for particular methods.

MARGIN

the array margin to be held fixed: see
`apply`

, and note that `MARGIN = 0`

maybe useful.

`duplicated()`

:
For a vector input, a logical vector of the same length as
`x`

. For a data frame, a logical vector with one element for
each row. For a matrix or array, and when `MARGIN = 0`

, a
logical array with the same dimensions and dimnames.

`anyDuplicated()`

: an integer or real vector of length one with
value the 1-based index of the first duplicate if any, otherwise
`0`

.

Using this for lists is potentially slow, especially if the elements
are not atomic vectors (see `vector`

) or differ only
in their attributes. In the worst case it is \(O(n^2)\).

These are generic functions with methods for vectors (including lists), data frames and arrays (including matrices).

For the default methods, and whenever there are equivalent method
definitions for `duplicated`

and `anyDuplicated`

,
`anyDuplicated(x, ...)`

is a “generalized” shortcut for
`any(duplicated(x, ...))`

, in the sense that it returns the
*index* `i`

of the first duplicated entry `x[i]`

if
there is one, and `0`

otherwise. Their behaviours may be
different when at least one of `duplicated`

and
`anyDuplicated`

has a relevant method.

`duplicated(x, fromLast = TRUE)`

is equivalent to but faster than
`rev(duplicated(rev(x)))`

.

The array method calculates for each element of the sub-array
specified by `MARGIN`

if the remaining dimensions are identical
to those for an earlier (or later, when `fromLast = TRUE`

) element
(in row-major order). This would most commonly be used to find
duplicated rows (the default) or columns (with `MARGIN = 2`

).
Note that `MARGIN = 0`

returns an array of the same
dimensionality attributes as `x`

.

Missing values (`"NA"`

) are regarded as equal, numeric and
complex ones differing from `NaN`

; character strings will be compared in a
“common encoding”; for details, see `match`

(and
`unique`

) which use the same concept.

Values in `incomparables`

will never be marked as duplicated.
This is intended to be used for a fairly small set of values and will
not be efficient for a very large set.

Except for factors, logical and raw vectors the default `nmax = NA`

is
equivalent to `nmax = length(x)`

. Since a hash table of size
`8*nmax`

bytes is allocated, setting `nmax`

suitably can
save large amounts of memory. For factors it is automatically set to
the smaller of `length(x)`

and the number of levels plus one (for
`NA`

). If `nmax`

is set too small there is liable to be an
error: `nmax = 1`

is silently ignored.

Long vectors are supported for the default method of
`duplicated`

, but may only be usable if `nmax`

is supplied.

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

```
# NOT RUN {
x <- c(9:20, 1:5, 3:7, 0:8)
## extract unique elements
(xu <- x[!duplicated(x)])
## similar, same elements but different order:
(xu2 <- x[!duplicated(x, fromLast = TRUE)])
## xu == unique(x) but unique(x) is more efficient
stopifnot(identical(xu, unique(x)),
identical(xu2, unique(x, fromLast = TRUE)))
duplicated(iris)[140:143]
duplicated(iris3, MARGIN = c(1, 3))
anyDuplicated(iris) ## 143
# }
# NOT RUN {
anyDuplicated(x)
anyDuplicated(x, fromLast = TRUE)
# }
```

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