\HeaderA{duplicated}{Determine Duplicate Elements}{duplicated}
\methaliasA{duplicated.array}{duplicated}{duplicated.array}
\methaliasA{duplicated.data.frame}{duplicated}{duplicated.data.frame}
\methaliasA{duplicated.default}{duplicated}{duplicated.default}
\methaliasA{duplicated.matrix}{duplicated}{duplicated.matrix}
\keyword{logic}{duplicated}
\keyword{manip}{duplicated}
\begin{Description}\relax
Determines which elements of a vector of data frame are duplicates
of elements with smaller subscripts, and returns a logical vector
indicating which elements (rows) are duplicates.
\end{Description}
\begin{Usage}
\begin{verbatim}
duplicated(x, incomparables = FALSE, ...)

## S3 method for class 'array':
duplicated(x, incomparables = FALSE, MARGIN = 1, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] a vector or a data frame or an array or \code{NULL}.
\item[\code{incomparables}] a vector of values that cannot be compared.
Currently, \code{FALSE} is the only possible value, meaning that all
values can be compared.
\item[\code{...}] arguments for particular methods.
\item[\code{MARGIN}] the array margin to be held fixed: see
\code{\LinkA{apply}{apply}}.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
This is a generic function with methods for vectors (including lists),
data frames and arrays (including matrices).

The data frame method works by pasting together a character
representation of the rows separated by \code{\bsl{}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
specified by \code{MARGIN} if the remaining dimensions are identical
to those for an earlier element (in row-major order).  This would most
commonly be used to find duplicated rows (the default) or columns
(with \code{MARGIN = 2}).
\end{Details}
\begin{Section}{Warning}
Using this for lists is potentially slow, especially if the elements
are not atomic vectors (see \code{\LinkA{vector}{vector}}) or differ only
in their attributes.  In the worst case it is \eqn{O(n^2)}{}.
\end{Section}
\begin{References}\relax
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
\emph{The New S Language}.
Wadsworth \& Brooks/Cole.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{unique}{unique}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
x <- c(9:20, 1:5, 3:7, 0:8)
## extract unique elements
(xu <- x[!duplicated(x)])
## xu == unique(x) but unique(x) is more efficient

duplicated(iris)[140:143]

duplicated(iris3, MARGIN = c(1, 3))
\end{ExampleCode}
\end{Examples}

