similar to: How to create ff objects from database connection

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'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.

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

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)

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,

Hi, I am using RpgSQL to retrieve data from a PostgreSQL database wich is with encoding UTF8, and I have some Chinese character in one of the columns, unfortunately R can't show it correctly. > df <- dbGetQuery(con, "select * from test") > df a b 1 1 ????????\xa2 2 2 ???? EURO\xa1 I see the following option, do I need to change the encoding option to show

Hi, I am using Rattle 2.6.4 with R 2.12.2 on win64, is this a bug ? Following is the content after execute the associate analysis process: Summary of the Apriori Association Rules: Number of Rules: 23351 Summary of the Measures of Interestingness: support confidence lift Min. :0.1250 Min. :1 Min. :2.667 1st Qu.:0.1250 1st Qu.:1 1st Qu.:2.667 Median :0.1250

*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 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)

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. 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

Hi, I am trying to write a function do cast columns of data frame as factor in a loop, the source is : as.factor.loop <- function(df, cols){ if (!is.null(df) && !is.null(cols) && length(cols) > 0) { for(col in cols) { df[[col]] <- as.factor(df[[col]]) } } } source('D:/ambertuil.r') x <- 1:5 y <- 2:6 df <- data.frame(x=x, y=y)

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 Dirk I think there are problems with pg_config, the configure script of RPostgreSQL checks for pg_config and got ¡°checking for pg_config... /usr/bin/pg_config¡±. In Solaris 10u7 X64, three versions of PostgreSQL are installed, there are in /usr/postgres/8.2(8.2.9) and /usr/postgres/8.3(8.3.3), the corresponding bin files are in /usr/postgres/<version>/bin and

Hi, I see the following is the credit scoreing in R guide : m2<-glm(formula = good_bad ~ checking + duration + history+ purpose +amount + savings + employed + installp + marital + coapp +age + other + depends + telephon + foreign +checking:amount What does checking:amount mean? Regards, Xiaobo Gu

Hi, Since we can?t access Google Groups here in China, so please forgive me if anyone has asked this question before. Sincerely Xiaobo Gu

Hi, Can the plenty of analytical functions provided by base R and contributed packages be called with ff objects as parameters directly, or do we have to write special version of the functions for ff objects? If it is the latter case, is there a list of functions which support ff objects already. Xiaobo.Gu [[alternative HTML version deleted]]

A question, please about development of R packages: Are there any guidelines or best practices for deciding when and why to implement an operation in R, vs. implementing it in C? The "Writing R Extensions" recommends "working in interpreted R code . . . this is normally the best option." But we do write C-functions and access them in R - the question is, when/why is this

Especially for large ff objects. Xiaobo.Gu [[alternative HTML version deleted]]