bigmemory (version 4.4.6)

bigmemory-package: Manage massive matrices with shared memory and memory-mapped files.

Description

Create, store, access, and manipulate massive matrices. Matrices are, by default, allocated to shared memory and may use memory-mapped files. Packages biganalytics, synchronicity, bigalgebra, and bigtabulate provide advanced functionality. Access to and manipulation of a big.matrix object is exposed in Rby an S4 class whose interface is simlar to that of an Rmatrix. Use of these packages in parallel environments can provide substantial speed and memory efficiencies. bigmemory also provides a C++ framework for the development of new tools that can work both with big.matrix and native Rmatrix objects.

Details

ll{ Package: bigmemory Type: Package Version: 4.4.6 Date: 2013-11-18 License: LGPL-3 Copyright: (C) 2013 Michael J. Kane and John W. Emerson URL: http://www.bigmemory.org LazyLoad: yes }

Index of functions/methods (grouped in a friendly way): big.matrix, filebacked.big.matrix, as.big.matrix

is.big.matrix, is.separated, is.filebacked

describe, attach.big.matrix, attach.resource

sub.big.matrix, is.sub.big.matrix

dim, dimnames, nrow, ncol, print, head, tail, typeof, length

read.big.matrix, write.big.matrix

mwhich

morder, mpermute

deepcopy

flush

Multi-gigabyte data sets challenge and frustrate Rusers, even on well-equipped hardware. Use of C/C++ can provide efficiencies, but is cumbersome for interactive data analysis and lacks the flexibility and power of R's rich statistical programming environment. The package bigmemory and sister packages biganalytics, synchronicity, bigtabulate, and bigalgebra bridge this gap, implementing massive matrices and supporting their manipulation and exploration. The data structures may be allocated to shared memory, allowing separate processes on the same computer to share access to a single copy of the data set. The data structures may also be file-backed, allowing users to easily manage and analyze data sets larger than available RAM and share them across nodes of a cluster. These features of the Bigmemory Project open the door for powerful and memory-efficient parallel analyses and data mining of massive data sets.

This project (bigmemory and its sister packages) is still actively developed, although the design and current features can be viewed as "stable." Please feel free to email us with any questions: [e-mail].

References

The Bigmemory Project: http://www.bigmemory.org/.

See Also

For example, big.matrix, mwhich, read.big.matrix

Examples

# Our examples are all trivial in size, rather than burning huge amounts
# of memory.

x <- big.matrix(5, 2, type="integer", init=0,
                dimnames=list(NULL, c("alpha", "beta")))
x
x[1:2,]
x[,1] <- 1:5
x[,"alpha"]
colnames(x)
options(bigmemory.allow.dimnames=TRUE)
colnames(x) <- NULL
x[,]