RleArray-class {DelayedArray} | R Documentation

## RleArray objects

### Description

The RleArray class is a DelayedArray subclass for representing an in-memory Run Length Encoded array-like dataset.

All the operations available for DelayedArray objects work on RleArray objects.

### Usage

    ## Constructor function:
    RleArray(data, dim, dimnames, chunksize=NULL)

### Arguments

`data` | An Rle object, or an ordinary list of Rle objects, or an RleList object, or a DataFrame object where all the columns are Rle objects. More generally speaking, `data` can be any list-like object where all the list elements are Rle objects.
`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.
`dimnames` | The dimnames of the object to be created. Must be `NULL` or a list of length the number of dimensions. Each list element must be either `NULL` or a character vector along the corresponding dimension.
`chunksize` | Experimental. Don't use!

### Value

An RleArray object.

### See Also

- Rle and DataFrame objects in the S4Vectors package and RleList objects in the IRanges package.

- DelayedArray objects.

- DelayedArray-utils for common operations on DelayedArray objects.

- `realize` for realizing a DelayedArray object in memory or on disk.

- HDF5Array objects in the HDF5Array package.

- The RleArraySeed helper class.

### Examples

    ## ---------------------------------------------------------------------
    ## A. BASIC EXAMPLE
    ## ---------------------------------------------------------------------

    data <- Rle(sample(6L, 500000, replace=TRUE), 8)
    a <- array(data, dim=c(50, 20, 4000))  # array() expands the Rle object
                                           # internally with as.vector()

    A <- RleArray(data, 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])
    m1 <- toto(a)
    head(m1)

    M1 <- toto(A)  # very fast! (operations are delayed)
    M1

    stopifnot(identical(m1, as.array(M1)))

    cs <- colSums(m1)
    CS <- colSums(M1)
    stopifnot(identical(cs, CS))

    ## Coercing a DelayedMatrix object to DataFrame produces a DataFrame
    ## object with Rle columns:
    as(M1, "DataFrame")

    ## ---------------------------------------------------------------------
    ## B. MAKING AN RleArray OBJECT FROM A LIST-LIKE OBJECT OF Rle OBJECTS
    ## ---------------------------------------------------------------------

    ## From a DataFrame object:
    DF <- DataFrame(A=Rle(sample(3L, 100, replace=TRUE)),
                    B=Rle(sample(3L, 100, replace=TRUE)),
                    C=Rle(sample(3L, 100, replace=TRUE) - 0.5),
                    row.names=sprintf("ID%03d", 1:100))

    M2 <- RleArray(DF)
    M2

    A3 <- RleArray(DF, dim=c(25, 6, 2))
    A3

    M4 <- RleArray(DF, dim=c(25, 12), dimnames=list(LETTERS[1:25], NULL))
    M4

    ## From an ordinary list:
    ## If all the supplied Rle objects have the same length and if the 'dim'
    ## argument is not specified, then the RleArray() constructor returns an
    ## RleMatrix object with 1 column per Rle object. If the 'dimnames'
    ## argument is not specified, then the names on the list are propagated
    ## as the colnames of the returned object.
    data <- as.list(DF)
    M2b <- RleArray(data)
    A3b <- RleArray(data, dim=c(25, 6, 2))
    M4b <- RleArray(data, dim=c(25, 12), dimnames=list(LETTERS[1:25], NULL))

    data2 <- list(Rle(sample(3L, 9, replace=TRUE)) * 11L,
                  Rle(sample(3L, 15, replace=TRUE)))
    ## Not run:
      RleArray(data2)  # error! (cannot infer the dim)

    ## End(Not run)
    RleArray(data2, dim=c(4, 6))

    ## From an RleList object:
    data <- RleList(data)
    M2c <- RleArray(data)
    A3c <- RleArray(data, dim=c(25, 6, 2))
    M4c <- RleArray(data, dim=c(25, 12), dimnames=list(LETTERS[1:25], NULL))

    data2 <- RleList(data2)
    ## Not run:
      RleArray(data2)  # error! (cannot infer the dim)

    ## End(Not run)
    RleArray(data2, dim=4:2)

    ## Sanity checks:
    data0 <- as.vector(unlist(DF, use.names=FALSE))
    m2 <- matrix(data0, ncol=3, dimnames=dimnames(M2))
    stopifnot(identical(m2, as.matrix(M2)))
    rownames(m2) <- NULL
    stopifnot(identical(m2, as.matrix(M2b)))
    stopifnot(identical(m2, as.matrix(M2c)))
    a3 <- array(data0, dim=c(25, 6, 2))
    stopifnot(identical(a3, as.array(A3)))
    stopifnot(identical(a3, as.array(A3b)))
    stopifnot(identical(a3, as.array(A3c)))
    m4 <- matrix(data0, ncol=12, dimnames=dimnames(M4))
    stopifnot(identical(m4, as.matrix(M4)))
    stopifnot(identical(m4, as.matrix(M4b)))
    stopifnot(identical(m4, as.matrix(M4c)))

    ## ---------------------------------------------------------------------
    ## C. COERCING FROM RleList OR DataFrame TO RleMatrix
    ## ---------------------------------------------------------------------

    ## Coercing an RleList object to RleMatrix only works if all the list
    ## elements in the former have the same length.
    x <- RleList(A=Rle(sample(3L, 20, replace=TRUE)),
                 B=Rle(sample(3L, 20, replace=TRUE)))
    M <- as(x, "RleMatrix")
    stopifnot(identical(x, as(M, "RleList")))

    x <- DataFrame(A=x[[1]], B=x[[2]], row.names=letters[1:20])
    M <- as(x, "RleMatrix")
    stopifnot(identical(x, as(M, "DataFrame")))

    ## ---------------------------------------------------------------------
    ## D. CONSTRUCTING A LARGE RleArray OBJECT
    ## ---------------------------------------------------------------------

    ## The RleArray() constructor does not accept a "long" Rle object (i.e.
    ## an object of length > .Machine$integer.max) at the moment:
    ## Not run:
      RleArray(Rle(5, 3e9), dim=c(3, 1e9))  # error!

    ## End(Not run)

    ## The workaround is to supply a list of Rle objects instead:

    toy_Rle <- function() {
      run_lens <- c(sample(4), sample(rep(c(1:19, 40) * 3, 6e4)), sample(4))
      run_vals <- sample(700, length(run_lens), replace=TRUE) / 5
      Rle(run_vals, run_lens)
    }
    rle_list <- lapply(1:80, function(j) toy_Rle())  # takes about 20 sec.

    ## Cumulative length of all the Rle objects is > .Machine$integer.max:
    sum(lengths(rle_list))  # 3.31e+09

    ## Feed 'rle_list' to the RleArray() constructor:
    dim <- c(14395, 320, 719)
    A <- RleArray(rle_list, dim)
    A

    ## Because all the Rle objects in 'rle_list' have the same length, we
    ## can call RleArray() on it without specifying the 'dim' argument. This
    ## returns an RleMatrix object where each column corresponds to an Rle
    ## object in 'rle_list':
    M <- RleArray(rle_list)
    M
    stopifnot(identical(as(rle_list, "RleList"), as(M, "RleList")))

    ## ---------------------------------------------------------------------
    ## E. CHANGING THE TYPE OF AN RleArray OBJECT FROM "double" TO "integer"
    ## ---------------------------------------------------------------------

    ## An RleArray object is an in-memory object so it can be useful to
    ## reduce its memory footprint. For an object of type "double" this can
    ## be done by changing its type to "integer" (integers are half the size
    ## of doubles in memory). Of course this only makes sense if this results
    ## in a loss of precision that is acceptable.
    ## On an ordinary array (or matrix) 'a', this is simply a matter of
    ## doing 'storage.mode(a) <- "integer"'. However, with a DelayedArray
    ## object, things are a little bit different. Let's do this on a subset
    ## of the RleMatrix object 'M' created in the previous section.

    M1 <- as(M[1:6e5, ], "RleMatrix")
    rm(M)

    ## First of all, it's important to be aware that object.size() (from
    ## package utils) is NOT reliable on RleArray objects! This is because
    ## the data in an RleArray object is stored in an environment and
    ## object.size() stubbornly refuses to take the content of an environment
    ## into account when computing its size:
    object.size(list2env(list(aa=1:10)))   # 56 bytes
    object.size(list2env(list(aa=1:1e6)))  # always 56 bytes!

    ## So we'll use object_size() instead (from package pryr):
    library(pryr)
    object_size(list2env(list(aa=1:10)))   # 264 B
    object_size(list2env(list(aa=1:1e6)))  # 4 MB
    object_size(list2env(list(aa=as.double(1:1e6))))  # 8 MB

    object_size(M1)  # 16.7 MB

    type(M1) <- "integer"  # Delayed!
    M1                     # Note the class: it's no longer RleMatrix!
                           # (That's because the object now carries delayed
                           # operations.)

    ## Because changing the type is a delayed operation, the memory footprint
    ## of the object has not changed yet (remember that the original data in
    ## a DelayedArray object is stored in its "seed" and its seed is never
    ## modified **in-place**, that is, no operation on the object will ever
    ## modify its seed):
    object_size(M1)  # Still the same (well, a very tiny more, because the
                     # object is now carrying one more delayed operation,
                     # the `type<-` operation)

    ## To effectively reduce the memory footprint of the object, a new object
    ## needs to be created. This is achieved simply by **realizing** M1 as a
    ## (new) RleArray object. Note that this realization will use block
    ## processing:

    DelayedArray:::set_verbose_block_processing(TRUE)  # See block processing
                                                       # in action.
    getAutoBlockSize()      # Automatic block size (100 Mb by default).
    setAutoBlockSize(20e6)  # Set automatic block size to 20 Mb.

    M2 <- as(M1, "RleArray")
    DelayedArray:::set_verbose_block_processing(FALSE)
    setAutoBlockSize()      # Reset automatic block size to factory settings.

    M2
    object_size(M2)  # 6.91 MB (Less than half the original size! This is
                     # because RleArray objects use some internal tricks to
                     # reduce memory footprint even more when the data in
                     # their seed is of type "integer".)

    ## Finally note that the 2-step approach described here (i.e.
    ## type(A) <- "integer" followed by realization) is generic and works
    ## on any kind of DelayedArray object or derivative. In particular,
    ## after doing 'type(A) <- "integer"', 'A' can be realized as anything
    ## as long as the realization backend is supported (e.g. could be
    ## 'as(A, "HDF5Array")' or 'as(A, "TENxMatrix")') and realization will
    ## always use block processing so the array data will never be fully
    ## loaded in memory.

[Package DelayedArray version 0.14.1 Index]