bigmemory-package         package:bigmemory         R Documentation

_b_i_g_m_e_m_o_r_y: _m_a_s_s_i_v_e _m_a_t_r_i_c_e_s _i_n (_p_o_s_s_i_b_l_y _s_h_a_r_e_d) _m_e_m_o_r_y.

_D_e_s_c_r_i_p_t_i_o_n:

     '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'.

_D_e_t_a_i_l_s:


       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.

_A_u_t_h_o_r(_s):

     John W. Emerson and Michael J. Kane

     Maintainer: Jay Emerson <john.emerson@yale.edu>

_R_e_f_e_r_e_n_c_e_s:

     See <URL: http://www.stat.yale.edu/~jay/bigmemory>.

_S_e_e _A_l_s_o:

     'big.matrix', 'mwhich', 'colmean', 'biglm'

_E_x_a_m_p_l_e_s:

     # 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)

