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\name{RleArray-class}
\docType{class}
\alias{class:RleArraySeed}
\alias{RleArraySeed-class}
\alias{class:SolidRleArraySeed}
\alias{SolidRleArraySeed-class}
\alias{class:RleRealizationSink}
\alias{RleRealizationSink-class}
\alias{type,RleRealizationSink-method}
\alias{class:ChunkedRleArraySeed}
\alias{ChunkedRleArraySeed-class}
\alias{dim,RleArraySeed-method}
\alias{dimnames,RleArraySeed-method}
\alias{coerce,SolidRleArraySeed,Rle-method}
\alias{coerce,RleRealizationSink,Rle-method}
\alias{extract_array,SolidRleArraySeed-method}
\alias{extract_array,ChunkedRleArraySeed-method}
\alias{coerce,RleRealizationSink,ChunkedRleArraySeed-method}
\alias{coerce,ChunkedRleArraySeed,SolidRleArraySeed-method}
\alias{class:RleArray}
\alias{RleArray-class}
\alias{class:RleMatrix}
\alias{RleMatrix-class}
\alias{RleMatrix}
\alias{coerce,RleArray,RleMatrix-method}
\alias{matrixClass,RleArray-method}
\alias{coerce,ANY,RleMatrix-method}
\alias{DelayedArray,RleArraySeed-method}
\alias{RleArray}
\alias{coerce,RleArray,Rle-method}
\alias{write_block,RleRealizationSink-method}
\alias{coerce,RleRealizationSink,RleArray-method}
\alias{coerce,RleRealizationSink,DelayedArray-method}
\alias{coerce,ANY,RleArray-method}
\alias{coerce,DelayedArray,RleArray-method}
\alias{coerce,DelayedMatrix,RleMatrix-method}
\alias{coerce,DataFrame,RleArray-method}
\alias{coerce,RleMatrix,DataFrame-method}
\alias{coerce,DelayedMatrix,DataFrame-method}
\alias{coerce,RleList,RleArray-method}
\alias{coerce,RleMatrix,RleList-method}
\title{RleArray objects}
\description{
The RleArray class is an array-like container where the values are stored
in a run-length encoding format. RleArray objects support delayed operations
and block processing.
}
\usage{
RleArray(rle, dim, dimnames=NULL, chunksize=NULL) # constructor function
}
\arguments{
\item{rle}{
An \link[S4Vectors]{Rle} object.
}
\item{dim}{
The dimensions of the object to be created, that is, an integer vector
of length one or more giving the maximal indices in each dimension.
}
\item{dimnames}{
Either \code{NULL} or the names for the dimensions. This must a list of
length the number of dimensions. Each list element must be either
\code{NULL} or a character vector along the corresponding dimension.
}
\item{chunksize}{
Experimental. Don't use!
}
}
\details{
RleArray extends \link{DelayedArray}. All the operations available on
\link{DelayedArray} objects work on RleArray objects.
}
\seealso{
\itemize{
\item \link[S4Vectors]{Rle} objects in the \pkg{S4Vectors} package.
\item \link{DelayedArray} objects.
\item \link{DelayedArray-utils} for common operations on
\link{DelayedArray} objects.
\item \code{\link{realize}} for realizing a DelayedArray object in memory
or on disk.
\item \link[HDF5Array]{HDF5Array} objects in the \pkg{HDF5Array} package.
\item \link[S4Vectors]{DataFrame} objects in the \pkg{S4Vectors} package.
\item \link[base]{array} objects in base R.
}
}
\examples{
rle <- Rle(sample(6L, 500000, replace=TRUE), 8)
a <- array(rle, dim=c(50, 20, 4000)) # array() expands the Rle object
# internally with as.vector()
A <- RleArray(rle, dim=c(50, 20, 4000)) # Rle object is NOT expanded
A
object.size(a)
object.size(A)
stopifnot(identical(a, as.array(A)))
as(A, "Rle") # deconstruction
toto <- function(x) (5 * x[ , , 1] ^ 3 + 1L) * log(x[, , 2])
b <- toto(a)
head(b)
B <- toto(A) # very fast! (operations are delayed)
B
stopifnot(identical(b, as.array(B)))
cs <- colSums(b)
CS <- colSums(B)
stopifnot(identical(cs, CS))
## Coercion of a DelayedMatrix object to DataFrame produces a DataFrame
## object with Rle columns:
as(B, "DataFrame")
## Coercion of an RleList object to RleArray only works if all the list
## elements in the RleList have the same length. Column names are taken
## from the names of the list elements.
rle_list <- RleList(A=Rle(sample(3L, 100, replace=TRUE)),
B=Rle(sample(3L, 100, replace=TRUE)))
C <- as(rle_list, "RleArray")
C
stopifnot(identical(as(C, "RleList"), rle_list))
}
\keyword{classes}
\keyword{methods}
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