Determine Duplicate Elements
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
duplicated(x, incomparables = FALSE, ...)"duplicated"(x, incomparables = FALSE, fromLast = FALSE, nmax = NA, ...)"duplicated"(x, incomparables = FALSE, MARGIN = 1, fromLast = FALSE, ...)anyDuplicated(x, incomparables = FALSE, ...) "anyDuplicated"(x, incomparables = FALSE, fromLast = FALSE, ...) "anyDuplicated"(x, incomparables = FALSE, MARGIN = 1, fromLast = FALSE, ...)
- a vector or a data frame or an array or
- a vector of values that cannot be compared.
FALSEis 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
- 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.
- the maximum number of unique items expected (greater than one).
- arguments for particular methods.
- the array margin to be held fixed: see
apply, and note that
MARGIN = 0maybe useful.
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
anyDuplicated(x, ...) is a generalized shortcut for
any(duplicated(x, ...)), in the sense that it returns the
i of the first duplicated entry
there is one, and
0 otherwise. Their behaviours may be
different when at least one of
anyDuplicated has a relevant method.
duplicated(x, fromLast = TRUE) is equivalent to but faster than
The data frame method works by pasting together a character
representation of the rows separated by
\r, so may be imperfect
if the data frame has characters with embedded carriage returns or
columns which do not reliably map to characters.
The array method calculates for each element of the sub-array
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).
MARGIN = 0 returns an array of the same
dimensionality attributes as
Missing values are regarded as equal, but
NaN is not equal to
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.
When used on a data frame with more than one column, or an array or matrix when comparing dimensions of length greater than one, this tests for identity of character representations. This will catch people who unwisely rely on exact equality of floating-point numbers!
Character strings will be compared as byte sequences if any input is
Except for factors, logical and raw vectors the default
nmax = NA is
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
nmax is set too small there is liable to be an
nmax = 1 is silently ignored.
Long vectors are supported for the default method of
duplicated, but may only be usable if
nmax is supplied.
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
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 $O(n^2)$.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
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 anyDuplicated(x) anyDuplicated(x, fromLast = TRUE)
#take some data data <- mtcars[, c(1:3)] row.names(data) <- NULL #add three new rows equal to the first three rows of data new.rows <- data.frame(mpg=c(21.0, 21.0, 22.8), cyl=c(6, 6, 4), disp=c(160, 160, 93)) new.data <- rbind(data, new.rows) #check for duplicated rows duplicated(new.data) #select the duplicated rows new.data[duplicated(new.data),] #remove the duplicated rows new.data[!duplicated(new.data),]