similar to: New version of package ff

Really old subject?, so, all my apologizes for digging up but, since I also ran into this? maybe this hack can be useful to someone I propose monkey patching here: library(ff) my.as.ffdf.data.frame <- function (x, vmode = NULL, col_args = list(), ...) { rnam <- attr(x, "row.names") if (is.integer(rnam)) { if (all(rnam == seq_along(rnam))) rnam <- NULL else

Hi, I am running the examples in page 70 of the ff package document, but it failed with the following error > cat("let's create some ff objects\n") let's create some ff objects > n <- 8e3 > a <- ff(sample(n, n, TRUE), vmode="integer", length=n, filename="d:/tmp/a.ff") > b <- ff(sample(255, n, TRUE), vmode="ubyte", length=n,

I've spent some time trying to wrap my head around reading in large csv files with the ff-package. I think I know how to do it, but am bumping into some problems. I've tried to recreate the issues as best as I can with a smaller example and maybe someone can help explain the problems. The following code just creates a csv file with an integer column, character column and logical column.

Hello everyone, I need to create and work with some big matrices that actually have somewhat over 2 million columns and 117 rows. To do some calculations on such big matrices R just needs too much memory for my PC (4GB installed). So I need a solution to work with large datasets. I'm trying to use the ff-package but I don't think I really understand the whole functionality of the

Hi, I am using version 2.1-1 of the ff package. I have a data set with 80 million rows and I need to create a new ffdf object, subseting by values in one of the original ffdf's columns. Here is my code: bigData <- read.table.ffdf(file="/data/demodata/data/smallData.txt", next.rows=1e5, head=TRUE, sep="|") dim(bigData) N <- nrow(bigData);N select <- ff(

Dear list, I am trying to understand and use the ff package. As I had some problems saving some ff objects, and as I did not fully manage to understand the whole concept of *.ff, *.ffData and *.RData with the help of the documentation, I tried to reproduce the examples from the help of ffsave. When I ran, however : (copied from the help) message("let's create some ff objects")

Hi, The read.csv.ffdf function in package ff will create the ff object physical file in the default directories, I am trying to let the files created in the paths users specify, I think the point is to make use of the asffdf_args parameter, I have a test CSV file named D:\rtemp\fftest.csv, the content of the file is as following: col1,col2,col3 1,"amber",2.4 2,"linda",4.5

I'm having trouble getting ffdfindexget to work right in Windows. Even the most trivial of examples gives me problems. > myVec = ff(1:5) > another = ff(10:14) > littleFrame = ffdf(myVec, another) > posVec = ff(c(2, 4), vmode = 'integer') > ffdfindexget(littleFrame, posVec) Error in if (any(B < 1)) stop("B too small") : missing value where TRUE/FALSE

Dear R community, The next release of package ff is available on CRAN. With kind help of Brian Ripley it now supports the Win64 and Sun versions of R. It has three major functional enhancements: a) new fast in-memory sorting and ordering functions (single-threaded) b) ff now supports on-disk sorting and ordering of ff vectors and ffdf dataframes c) ff integer vectors now can be used as

Dear R community, The next release of package ff is available on CRAN. With kind help of Brian Ripley it now supports the Win64 and Sun versions of R. It has three major functional enhancements: a) new fast in-memory sorting and ordering functions (single-threaded) b) ff now supports on-disk sorting and ordering of ff vectors and ffdf dataframes c) ff integer vectors now can be used as

Dear R friends. After having some troubles learning how to create a ffdf object, now I find myself having problems saving it. this is the data i´d like to save: str(DATA) List of 3 $ virtual: 'data.frame': 6 obs. of 7 variables: .. $ VirtualVmode : chr "double" "short" "integer" "integer" ... .. $ AsIs : logi FALSE FALSE FALSE

Hi, I have two questions on using the ff package and wonder if anyone who used ff can share some thoughts. I need to save a matrix as a memory-mapped file and load it back later. To save the matrix, I use mat = matrix(1:20, 4, 5) matFF = ff(mat, dim=dim(mat), filename="~/a.mat", overwrite=TRUE, dimnames = dimnames(mat)) To load it back, I use matFF2 = ff(vmode = "double",

Hi everyone, my apologies if I'm overlooking something obvious in the documentation. I'm relatively inexperienced with the (awesome) ff package. My goal is to use the read.table.ffdf() function to call the read.fwf() function and pass through the colClasses and comment.char arguments. The code below shows exactly what doesn't work for me. If the colClasses and comment.char

Hello to everyone, I'm a newbie with ff package and I´m starting to use it. I´ve been reading the ff.pdf guide and another documents and questions , but I´m really confused about some procedures I can´t see how to do. I´d want to know if it´s possible (and how) to "save" a ffdf file(s) and open it in another session, via saving it in an permanet location. Let´s supose we´re

I've noticed that ff uses the unzip utility available on it's host OS to load datasets via ffload. It seems to work fine in linux, but when I try to use the package in Windows (hence dling Windows unzip utils) I get 2 errors, one telling me that the options aren't being passed to unzip correctly and another indicating that my filename is incorrect. First, does anyone know of an unzip

*Dear R users, Ive just started using the ff package. There is a csv file (~4Gb) with 7 columns and 6e+7 rows. I want to read only column from the file, skipping the first 100 rows. Below Ive provided different outcomes, which will clarify my problem * > sessionInfo() R version 2.14.2 (2012-02-29) Platform: x86_64-pc-mingw32/x64 (64-bit) locale: ... attached base packages: [1] tools

Hello. I'm using as.ffdf(mydataframe) to create ffdf objects inside an lapply loop and returning that. I then use crbind to combine the lapply results into allData. So...simplified flow looks like this. res <- lapply(1:nchunks, function(n) { blah blah with nth chunk mydataframe <- data.frame(blah blah) dat <-

Dear R community, Package bit version 1.1-3 and ff version 2.1.2 is available on CRAN and should be useful to handle large datasets. It adds convenient utilities for managing ff objects and files (see ?ffsave) and removes some performance bottlenecks. In case you experience unexpected performance problems with ff, here is a couple of recommendations based on FAQs: 1) Compare the size of data

Dear R community, Package bit version 1.1-3 and ff version 2.1.2 is available on CRAN and should be useful to handle large datasets. It adds convenient utilities for managing ff objects and files (see ?ffsave) and removes some performance bottlenecks. In case you experience unexpected performance problems with ff, here is a couple of recommendations based on FAQs: 1) Compare the size of data

Dear R Helpers, I would like to report on an error in the ff package here. The ff package is an R package which enables us to store large data on disk systematically and have fast access to the database. I used the package in Linux as a guest OS of VirtualBox, and executed the following commands. library(ff)