bigmemory-package {bigmemory} | R Documentation |
bigmemory implements massive matricies in C++
(optionally, in shared memory)
and supports their basic manipulation and exploration.
Access to and manipulation
of a big.matrix
object is exposed in R by an S4
class whose interface is simlar to an R matrix
.
Package: | bigmemory |
Type: | Package |
Version: | 2.3 |
Date: | 2008-07-26 |
License: | LGPL-3 |
Multi-gigabyte data sets challenge and frustrate R
users even on well-equipped hardware.
C/C++ or Fortran programming can be helpful, but
is cumbersome for interactive data analysis and
lacks the flexibility and power of R's rich statistical
programming environment. The new package bigmemory
bridges this gap, implementing massive matrices in memory
(managed in R but implemented in C++) and
supporting their basic
manipulation and exploration. It is ideal for problems
involving the analysis in R of manageable subsets
of the data,or when an analysis is conducted mostly in C++. In a Unix environment,
the data structure may be allocated to shared memory with
transparent read and write locking, allowing separate R
processes on the same computer to share access to a single copy of the
data set. This opens the door for more powerful parallel
analyses and data mining of massive data sets.
John W. Emerson and Michael J. Kane
Maintainer: Jay Emerson <john.emerson@yale.edu>
See http://www.stat.yale.edu/~jay/bigmemory.
big.matrix
, mwhich
, colmean
, biglm
# Our examples are all trivial in size, rather than burning huge amounts # of memory simply to demonstrate the package functionality. x <- big.matrix(5, 2, type="integer", init=0) colnames(x)=c("alpha", "beta") x x[,] x[,1] <- 1:5 x[,] mean(x) colmean(x) summary(x)