similar to: how to read numeric vector as factors using read.table.ffdf

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 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

I have no direct experience with such horrific models, but your formula is a mess and Google suggests the biglm package with ffdf. Specifically, you should convert your discrete variables to factors before you build the model, particularly since you want to use predict after the fact, for which you will need a new data set with the exact same levels in the factors. Also, your use of I() is

*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

Hi All,   I am currently trying to familiarize with "ff" package which allows me to store R objects in the hard drive. One of things that I notice when reading in a text file with "read.csv.ffdf" function - is that, in the R temp folder, 1000+ small files get created, each file having a name like "ffdf1bcd8b4aa0.ff". Each file is about 5KB in size.   My understanding

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.

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

Hi all - I'm trying to find a way to create dummy variables from factors in a regression. I have been using biglm along the lines of ff <- log(Price) ~ factor(Colour):factor(Store) + factor(DummyVar):factor(Colour):factor(Store) lm1 <- biglm(ff, data=my.dataset) but because there are lots of colours (>100) and lots of stores (>250), I run it to memory problems. Now, not every

Dear all, I am running a panel regression with time and location fixed effects: ### reg1 <- lm(lny ~ factor(id) + factor(year) + x1+ I(x1)^2 + x2+ I(x2)^2 , data=mydata, na.action="na.omit") ### My goal is to use the estimation for prediction. However, I have 8,500 IDs, which is resulting in very slow computation. Ideally, I would like to do the following: ### reg2 <-

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

Hi My aim is to read a large .csv file into R. I ran the following code and am using R version 10.1 on Windows. >library(ff) > read.csv.ffdf(x=NULL,"file.csv",fileEncoding="",nrows=-1,first.rows=NULL,next.rows=NULL,levels=NULL,appendLevels=TRUE,FUN="read.table",transFUN=NULL,asffdf_args=list(),BATCHBYTES=getOption("ffbatchbytes"),VERBOSE=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",

Dear R community, ff Version 2.1.1 is available on CRAN. It now supports large data.frames, csv import/export, packed atomic datatypes and bit filtering from package 'bit' on which it depends from now. Some performance results in seconds from test data with 78 mio rows and 7 columns on a 3 GB notebook: sequential reading 1 mio rows: csv = 32.7 ffdf = 1.3 sequential writing 1 mio

Dear R community, ff Version 2.1.1 is available on CRAN. It now supports large data.frames, csv import/export, packed atomic datatypes and bit filtering from package 'bit' on which it depends from now. Some performance results in seconds from test data with 78 mio rows and 7 columns on a 3 GB notebook: sequential reading 1 mio rows: csv = 32.7 ffdf = 1.3 sequential writing 1 mio

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, I'm trying to compile the package biglm, but when I build it with R CMD build biglm, it failed : C:\LOCAL\c-dutang\code\R\biglm2>R CMD build biglm * checking for file 'biglm/DESCRIPTION' ... OK * preparing 'biglm': * checking DESCRIPTION meta-information ...C:/DOCUME~1/c-dutang/Local: Can't op n C:/DOCUME~1/c-dutang/Local: No such file or directory

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

Hello folks, I am very excited to have discovered R and have been exploring its capabilities. R's regression models are of great interest to me as my company is in the business of running thousands of linear regressions on large datasets. I am using biglm to run linear regressions on datasets that are as large as several GB's. I have been pleasantly surprised that biglm runs the

I am trying to figure out why 'biglm' can handle large data set... According to the R document - "biglm creates a linear model object that uses only p^2 memory for p variables. It can be updated with more data using update. This allows linear regression on data sets larger than memory." After reading the source code below? I still could not figure out how 'update'

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] " "