similar to: bigmemory

I am using the library(bigmemory) to handle large datasets, say 1 GB, and facing following problems. Any hints from anybody can be helpful. _Problem-1: _ I am using "read.big.matrix" function to create a filebacked big matrix of my data and get the following warning: > x = read.big.matrix("/home/utkarsh.s/data.csv",header=T,type="double",shared=T,backingfile

Hello All, I tried to intall the bigmemory package from a CRAN mirror site and received the following output while installing. Any idea what's going on and how to fix it? The system details are provided below. --------------------- begin error messages ----------------------- * installing *source* package 'bigmemory' ... checking for Sun Studio compiler...no checking for

Hi, I am trying to run the bigmemory example provided on the http://www.bigmemory.org/ The example runs on the "airline data" and generates summary of the csv files:- library(bigmemory) library(biganalytics) x <- read.big.matrix("2005.csv", type="integer", header=TRUE, backingfile="airline.bin", descriptorfile="airline.desc",

Hi, Is it possible for me to read data from shared memory created by a vc++ program into R using bigmemory? [[alternative HTML version deleted]]

Hi, With reference to the mail below, I have large datasets, coming from various different sources, which I can read into filebacked big.matrix using library bigmemory. I want to merge them all into one 'big.matrix' object. (Later, I want to run regression using library 'biglm'). I am unsuccessfully trying to do this from quite some time now. Can you please

Hi, I'm playing around with the 'bigmemory' package, and I have finally managed to create some really big matrices. However, only now I realize that there may not be functions made for what I want to do with the matrices... I would like to perform a cluster analysis based on a big.matrix. Googling around I have found indications that a certain kmeans.big.matrix() function should

I have pretty big data sizes, like matrices of .5 to 1.5GB so once i need to juggle several of them i am in need of disk cache. I am trying to use bigmemory package but getting problems that are hard to understand. I am getting seg faults and machine just hanging. I work by the way on Red Hat Linux, 64 bit R version 10. Simplest problem is just saving matrices. When i do something like

I recently learned about the bigmemory and foreach packages and am trying to use them to help me create a very large matrix. Without those packages, I can create the type of matrix that I want with 10 columns and 5e6 rows. I would like to be able to scale up to 5e9 rows, or more, if possible. I have created a simplified example of what I'm trying to do, below. The first part of the

Dear all, I am new to network analysis, but since I have good data I started to read about it and learned how to use the ergm and related packages. I generally get interesting results, but when I run a model including sociality and selective mixing effects for different groups, the model runs (and converges) but I get a warning as follows: mod <- ergm(network ~ edges + gwesp(0, fixed=T) +

I work with problems that have rather large data requirements -- typically a bunch of multigig arrays. Given how generous R is with using memory, the only way for me to work with R has been to use bigmatrices from bigmemory package. One thing that is missing a bit is interoperability of bigmatrices with C++ and possibly java. What i mean by that is API that would allow read and write filebacked

Hi all, I'm on a Linux server with 48Gb RAM. I did the following: x <- big.matrix(nrow=20000,ncol=500000,type='short',init=0,dimnames=list(1:20000,1:500000)) #Gets around the 2^31 issue - yeah! in Unix, when I hit the "top" command, I see R is taking up about 18Gb RAM, even though the object x is 0 bytes in R. That's fine: that's how bigmemory is supposed to

I was surprised to observe the following difference between 2.4.1 and 2.6.0 after a long overdue upgrade a few months ago of our departmental server. It wasn't a bug fix, but a subtle improvement. Here's the simplest example I could create. The size is excessive, on the order of the Netflix Competition data. The integer matrix is about 1.12 GB, and if coerced to numeric it is 2.24 GB.

At one point we might have gotten something working (older version?) on Solaris x86, but were never successful on Solaris sparc that I remember -- it isn't a platform we can test and support. We believe there are problems with BOOST library compatibilities. We'll try (again) to clear up the other warnings in the logs, though. !-) We should also revisit the possibility of a CRAN BOOST

The re-engineered bigmemory package is now available (Version 3.5 and above) on CRAN. We strongly recommend you cease using the older versions at this point. bigmemory now offers completely platform-independent support for the big.matrix class in shared memory and, optionally, as filebacked matrices for larger-than-RAM applications. We're working on updating the package vignette, and a

The long-promised revision to bigmemory has arrived, with package 4.2.3 now on CRAN. The mutexes (locks) have been extracted and will be available through package synchronicity (on R-Forge, soon to appear on CRAN). Initial versions of packages biganalytics and bigtabulate are on CRAN, and new versions which resolve the warnings and have streamlined CRAN-friendly configurations will appear

The re-engineered bigmemory package is now available (Version 3.5 and above) on CRAN. We strongly recommend you cease using the older versions at this point. bigmemory now offers completely platform-independent support for the big.matrix class in shared memory and, optionally, as filebacked matrices for larger-than-RAM applications. We're working on updating the package vignette, and a

The long-promised revision to bigmemory has arrived, with package 4.2.3 now on CRAN. The mutexes (locks) have been extracted and will be available through package synchronicity (on R-Forge, soon to appear on CRAN). Initial versions of packages biganalytics and bigtabulate are on CRAN, and new versions which resolve the warnings and have streamlined CRAN-friendly configurations will appear

Hi there! I have become a big fan of the 'foreach' package allowing me to do a lot of stuff in parallel. For example, evaluating the function f on all elements in a vector x is easily accomplished: foreach(i=1:length(x),.combine=c) %dopar% f(x[i]) Here the .combine=c option tells foreach to combine output using the c()-function. That is, to return it as a vector. Today I discovered the

Package "bigmemory" is now available on CRAN. A brief abstract follows: Multi-gigabyte data sets challenge and frustrate R users even on well-equipped hardware. C/C++ and 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,

Package "bigmemory" is now available on CRAN. A brief abstract follows: Multi-gigabyte data sets challenge and frustrate R users even on well-equipped hardware. C/C++ and 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,