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\name{SparseArray}
\docType{class}
\alias{class:SparseArray}
\alias{SparseArray-class}
\alias{SparseArray}
\alias{class:SparseMatrix}
\alias{SparseMatrix-class}
\alias{SparseMatrix}
\alias{dim,SparseArray-method}
\alias{dimnames,SparseArray-method}
\alias{dimnames<-,SparseArray,ANY-method}
\alias{is_sparse,SparseArray-method}
\alias{show,SparseArray-method}
\title{SparseArray objects}
\description{
The \pkg{SparseArray} package defines the SparseArray virtual class
whose purpose is to be extended by other S4 classes that aim
at representing in-memory multidimensional sparse arrays.
It has currently two concrete subclasses, \link{COO_SparseArray}
and \link{SVT_SparseArray}, both also defined in this package.
Each subclass uses its own internal representation for the nonzero
multidimensional data, the \emph{COO layout} for \link{COO_SparseArray},
and the \emph{SVT layout} for \link{SVT_SparseArray}. The two layouts
are described in the \link{COO_SparseArray} and \link{SVT_SparseArray}
man pages, respectively.
Finally, the package also defines the SparseMatrix virtual class, as
a subclass of the SparseArray class, for the specific 2D case.
}
\usage{
## Constructor function:
SparseArray(x, type=NA)
}
\arguments{
\item{x}{
An ordinary matrix or array, or a dg[C|R]Matrix object, or an
lg[C|R]Matrix object, or any matrix-like or array-like object that
supports coercion to \link{SVT_SparseArray}.
}
\item{type}{
A single string specifying the requested type of the object.
By default, the SparseArray object returned by the constructor
function has the same \code{type()} as \code{x}. However the
user can use the \code{type} argument to request a different type.
Note that doing:
\preformatted{ sa <- SparseArray(x, type=type)}
is equivalent to doing:
\preformatted{ sa <- SparseArray(x)
type(sa) <- type}
but the former is more convenient and will generally be more efficient.
Supported types are all R atomic types plus \code{"list"}.
}
}
\details{
The SparseArray class extends the \link[S4Arrays]{Array} virtual class
defined in the \pkg{S4Arrays} package. Here is the full SparseArray
sub-hierarchy as defined in the \pkg{SparseArray} package (virtual
classes are marked with an asterisk):
\preformatted{
: Array class : Array*
: hierarchy : ^
|
- - - - - - - - - - - - - - - - - | - - - - - - - - - - - - - - -
: SparseArray : SparseArray*
: sub-hierarchy : ^ ^ ^
| | |
COO_SparseArray | SVT_SparseArray
^ | ^
- - - - - - - - - - - - | - - - - | - - - - | - - - - - - - - - -
: SparseMatrix : | SparseMatrix* |
: sub-sub-hierarchy : | ^ ^ |
| | | |
COO_SparseMatrix SVT_SparseMatrix}
Any object that belongs to a class that extends SparseArray e.g. (a
\link{SVT_SparseArray} or \link{SVT_SparseMatrix} object) is called
a \emph{SparseArray derivative}.
Most of the \emph{standard matrix and array API} defined in base R should
work on SparseArray derivatives, including \code{dim()}, \code{length()},
\code{dimnames()}, \code{`dimnames<-`()}, \code{[}, \code{drop()},
\code{`[<-`} (subassignment), \code{t()}, \code{rbind()}, \code{cbind()},
etc...
SparseArray derivatives also support \code{type()}, \code{`type<-`()},
\code{is_sparse()}, \code{nzcount()}, \code{nzwhich()}, \code{nzvals()},
\code{`nzvals<-`()}, \code{sparsity()}, \code{arbind()}, and \code{acbind()}.
}
\value{
A \emph{SparseArray derivative}, that is a \link{SVT_SparseArray},
\link{COO_SparseArray}, \link{SVT_SparseMatrix}, or
\link{COO_SparseMatrix} object.
The \code{type()} of the input object is preserved, except if a
different one was requested via the \code{type} argument.
What is considered a zero depends on the \code{type()}:
\itemize{
\item \code{"logical"} zero is \code{FALSE};
\item \code{"integer"} zero is \code{0L};
\item \code{"double"} zero is \code{0};
\item \code{"complex"} zero is \code{0+0i};
\item \code{"raw"} zero is \code{raw(1)};
\item \code{"character"} zero is \code{""} (empty string);
\item \code{"list"} zero is \code{NULL}.
}
}
\seealso{
\itemize{
\item The \link{COO_SparseArray} and \link{SVT_SparseArray} classes.
\item \link{is_nonzero} for \code{is_nonzero()} and \code{nz*()} functions
\code{nzcount()}, \code{nzwhich()}, etc...
\item \link{SparseArray_aperm} for permuting the dimensions of a
SparseArray object (e.g. transposition).
\item \link{SparseArray_subsetting} for subsetting a SparseArray object.
\item \link{SparseArray_subassignment} for SparseArray subassignment.
\item \link{SparseArray_abind} for combining 2D or multidimensional
SparseArray objects.
\item \link{SparseArray_summarization} for SparseArray summarization
methods.
\item \link{SparseArray_Arith}, \link{SparseArray_Compare}, and
\link{SparseArray_Logic}, for operations from the \code{Arith},
\code{Compare}, and \code{Logic} groups on SparseArray objects.
\item \link{SparseArray_Math} for operations from the \code{Math} and
\code{Math2} groups on SparseArray objects.
\item \link{SparseArray_Complex} for operations from the \code{Complex}
group on SparseArray objects.
\item \link{SparseArray_misc} for miscellaneous operations on a
SparseArray object.
\item \link{SparseArray_matrixStats} for col/row summarization methods
for SparseArray objects.
\item \link{rowsum_methods} for \code{rowsum()} methods for sparse
matrices.
\item \link{SparseMatrix_mult} for SparseMatrix multiplication and
cross-product.
\item \code{\link{randomSparseArray}} to generate a random SparseArray
object.
\item \code{\link{readSparseCSV}} to read/write a sparse matrix
from/to a CSV (comma-separated values) file.
\item S4 classes \linkS4class{dgCMatrix}, \linkS4class{dgRMatrix},
and \linkS4class{lgCMatrix} defined in the \pkg{Matrix} package,
for the de facto standard for sparse matrix representations
in the R ecosystem.
\item \code{\link[S4Arrays]{is_sparse}} in the \pkg{S4Arrays} package.
\item The \link[S4Arrays]{Array} class defined in the \pkg{S4Arrays}
package.
\item Ordinary \link[base]{array} objects in base R.
\item \code{base::\link[base]{which}} in base R.
}
}
\examples{
## ---------------------------------------------------------------------
## Display details of class definition & known subclasses
## ---------------------------------------------------------------------
showClass("SparseArray")
## ---------------------------------------------------------------------
## The SparseArray() constructor
## ---------------------------------------------------------------------
a <- array(rpois(9e6, lambda=0.3), dim=c(500, 3000, 6))
SparseArray(a) # an SVT_SparseArray object
m <- matrix(rpois(9e6, lambda=0.3), ncol=500)
SparseArray(m) # an SVT_SparseMatrix object
dgc <- sparseMatrix(i=c(4:1, 2:4, 9:12, 11:9), j=c(1:7, 1:7),
x=runif(14), dims=c(12, 7))
class(dgc)
SparseArray(dgc) # an SVT_SparseMatrix object
dgr <- as(dgc, "RsparseMatrix")
class(dgr)
SparseArray(dgr) # a COO_SparseMatrix object
## ---------------------------------------------------------------------
## nzcount(), nzwhich(), nzvals(), `nzvals<-`()
## ---------------------------------------------------------------------
x <- SparseArray(a)
## Get the number of nonzero array elements in 'x':
nzcount(x)
## nzwhich() returns the indices of the nonzero array elements in 'x'.
## Either as an integer (or numeric) vector of length 'nzcount(x)'
## containing "linear indices":
nzidx <- nzwhich(x)
length(nzidx)
head(nzidx)
## Or as an integer matrix with 'nzcount(x)' rows and one column per
## dimension where the rows represent "array indices" (a.k.a. "array
## coordinates"):
Mnzidx <- nzwhich(x, arr.ind=TRUE)
dim(Mnzidx)
## Each row in the matrix is an n-tuple representing the "array
## coordinates" of a nonzero element in 'x':
head(Mnzidx)
tail(Mnzidx)
## Extract the values of the nonzero array elements in 'x' and return
## them in a vector "parallel" to 'nzwhich(x)':
x_nzvals <- nzvals(x) # equivalent to 'x[nzwhich(x)]'
length(x_nzvals)
head(x_nzvals)
nzvals(x) <- log1p(nzvals(x))
x
## Sanity checks:
stopifnot(identical(nzidx, which(a != 0)))
stopifnot(identical(Mnzidx, which(a != 0, arr.ind=TRUE, useNames=FALSE)))
stopifnot(identical(x_nzvals, a[nzidx]))
stopifnot(identical(x_nzvals, a[Mnzidx]))
stopifnot(identical(`nzvals<-`(x, nzvals(x)), x))
}
\keyword{methods}
\keyword{classes}
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