similar to: ff package when reading .csv files

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

Hi! It seems that read.table() in R 3.0.1 (Linux 64-bit) does not consider quoted integers as an acceptable value for columns for which colClasses="integer". But when colClasses is omitted, these columns are read as integer anyway. For example, let's consider a file named file.dat, containing: "1" "2" > read.table("file.dat",

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

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 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

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

I have a big data set that includes character variables of many different values. I'm trying to use ff to read the data and then use biglm.big.matrix to build linear models. However, since big.matrix will convert all character vectors to factors and the character labels will be lost. I decided to create a lookup table outside of R for my character columns and use numbers to represent different

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 <-

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, I frequently have to export a large quantity of data from some source (for example, a database, or a hand-written perl script) and then read it into R. This occasionally takes a lot of time; I'm usually using read.table("filename",comment.char="",quote="") to read the data once it is written to disk. However, I *know* that the program that generates

Dear Experienced R Practitioners, I have 4GB .txt data called "dataset.txt" and have attempted to use *ff, bigmemory, filehash and sqldf *packages to import it, but have had no success. The readLines output of this data is: readLines("dataset.txt",n=20) [1] " "

Dear folks ? I have a large (26 gig) ASCII flat file in fixed-width format with about 10 million observations of roughly 400 variables. (It is 51 years of Current Population Survey micro data from IPUMS, roughly half the fields for each record). The file was produced by automatic process in response to a data request of mine. The file is not accompanied by a human-readable file giving the

Hello fellow R users, I am trying to read a 6.9 million row text file with 26 columns separated by spaces into R using ff. When I specify a small number for first.rows, next.rows and nrows it is read with no issue. However, when I try to specify larger next.rows values and no nrows parameter to read the entire file, I keep getting errors. Please see code below. I am trying to this on a m1.large

Hi Does anybody know how to create ff objects with data reading from stream objects, such as data reading from PostgreSQL database through RPostgreSQL. For this purpose although we can save the data to a csv file through external tools and then read it through csv readers, but it requires one more data read and write operation, which is of high I/O cost for large datasets. Xiaobo.Gu

I'm trying to import a table into R the file is about 700MB. Here's my first try: > DD<-read.table("01uklicsam-20070301.dat",header=TRUE) Error: cannot allocate vector of size 15.6 Mb In addition: Warning messages: 1: In scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : Reached total allocation of 1535Mb: see help(memory.size) 2: In scan(file, what,

Greetings useRs, Can anyone provide an example how to use ff to feed a very large data frame to glm? The data.frame cannot be loaded in R using conventional read.csv as it is too big. glm(...,data=ff.file) ?? Thank you Stephen B

Hello to everyone. I'm trying to use the %in% to match to vectors in ff format. a<-as.ff(data[,1]) %in% fire$fecha > aff (open) logical length=3653 (3653) [1] [2] [3] [4] [5] [6] [7] [8] [3646] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE : FALSE [3647] [3648] [3649] [3650] [3651] [3652] [3653] FALSE FALSE FALSE FALSE FALSE FALSE