search for: dimorder

...:* x.class <- c('NULL', 'numeric','NULL','NULL','NULL', 'NULL', 'NULL') ##* The following command works fine:* > read.csv.ffdf(file=csvfile, header=FALSE, skip=100, > colClasses=x.class, nrows=1e3) ffdf (all open) dim=c(1000,1), dimorder=c(1,2) row.names=NULL ffdf virtual mapping PhysicalName VirtualVmode PhysicalVmode AsIs VirtualIsMatrix V2 V2 double double FALSE FALSE PhysicalIsMatrix PhysicalElementNo PhysicalFirstCol PhysicalLastCol V2 FALSE 1 1...

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

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 FALSE FALSE FALSE FALSE FALSE FALSE .. $ PhysicalIsMatrix : logi FALSE FALSE FALSE FALSE FALSE FALSE .. $ PhysicalElementNo: int 1 2 3 4 5 6 .. $ PhysicalFirstCol : int 1 1 1 1 1 1 .. $ PhysicalLastCol : int 1 1 1 1 1 1 .. - attr(*, "Dim")= int 65640757 6 .. - attr(*, "Dimorder")= int 1 2 $ physical: List of 6 .. $ fecha : list() .. ..- attr(*, "physical")=Class 'ff_pointer' <externalptr> .. .. ..- attr(*, "vmode")= chr "double" .. .. ..- attr(*, "maxlength")= int 65640757 .. .. ..- attr(*, "pat...

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

...="~/path/archiveName", list="objectName") If my workspace is emply, I can see the object enter. However, when I wish to interrogate the object, it appears that I have no physical connection to the data: > objectName ff (deleted) double length=21024 (21024) dim=c(73,144,2) dimorder=c(1,2,3) > open(objectName) Error: file.access(filename, 0) == 0 is not TRUE In simple words, my data is no longer usable...WHY? This shouldn't be so hard! -- View this message in context: http://r.789695.n4.nabble.com/HELP-ff-package-tp4632949.html Sent from the R help mailing list ar...

...ts now have support for 'logical', 'raw' and 'integer' atomic datatypes, plus close-to-atomic types like 'factor', 'POSIXct' or custom close-to-atomic types. In addition to fast vector access, ff now has native support for matrices and arrays with flexible dimorder (major column-order, major row-order and generalizations for arrays). While the raw data still gets stored on binary flat files in native encoding, 'ff' objects have been extended to carry their meta information as physical and virtual attributes. ff objects have well-defined hybrid copyi...

...ts now have support for 'logical', 'raw' and 'integer' atomic datatypes, plus close-to-atomic types like 'factor', 'POSIXct' or custom close-to-atomic types. In addition to fast vector access, ff now has native support for matrices and arrays with flexible dimorder (major column-order, major row-order and generalizations for arrays). While the raw data still gets stored on binary flat files in native encoding, 'ff' objects have been extended to carry their meta information as physical and virtual attributes. ff objects have well-defined hybrid copyi...

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",

...y check agains md5 collisions # row identity for ff and R matrices (less overhead compared to id.ff via ffapply ) ffrowid <- function(x, ...){ id(ffrowapply(x, apply(x[i1:i2,,drop=FALSE], 1, md5), return="unlist", use.names=FALSE, ...)) } if (FALSE){ a <- ff(0, dim=c(100000,10),dimorder=2:1) ffapply(a, a[i1:i2]<-runif(i2-i1+1)) r <- ffrowhash(a) } if (FALSE){ n <- 100000 m <- 10 x <- ff(0, dim=c(n,m)) x[,dimorder=2:1] <- 1:(m*n/2) hash(x, MARGIN=integer()) # hash single cells, no RAM optimization hash(x) # hash rows, RAM savin...

...ING FF*/### #First, try with the 29 row "datatset2.txt", # open a connection to the file con <- file('dataset2.txt', 'rt') # read the remainder using read.table.ffdf ffdf <- read.table.ffdf(file=con) # close connection close(con) ffdf #ffdf (all open) dim=c(29,9), dimorder=c(1,2) row.names=NULL #ffdf virtual mapping # PhysicalName VirtualVmode PhysicalVmode AsIs VirtualIsMatrix PhysicalIsMatrix PhysicalElementNo PhysicalFirstCol PhysicalLastCol PhysicalIsOpen V1 V1 integer integer FALSE FALSE FALSE 1...