similar to: Odd timing behaviour in reading a file

Hello, when I work in R, I write code in a text file that I run with the "source(filename)" command. In R2.1.1 the file is read-only while the source command is executed. This was not the case in R2.0.1. Is this a bug-fix or is it possible not to have the file read-only when executed? Best regards Vidar

Hi, Please see below for post on r-help regarding data.frame() and the possibility of dropping rownames, for space and time reasons. I've made some changes, attached, and it seems to be working well. I see the expected space (90% saved) and time (10 times faster) savings. There are no doubt some bugs, and needs more work and testing, but I thought I would post first at this stage. Could some

hi all If I wanna get the total number of lines in a big file without reading the file's content into R as matrix or data frame, any methods or functions? thanks in advance. Regards

Hi, I have a 5x731 array A, and I want to compute the sums of the columns. Currently I do: apply(A, 2, sum) But it turns out, this is slow: 70% of my CPU time is spent here, even though there are many complicated steps in my computation. Is there a faster way? Thanks, Martin

Hi, I am experiencing a long delay when using dataframes inside loops and was wordering if this is a bug or not. Example code: > st <- rep(1,100000) > ed <- rep(2,100000) > for(i in 1:length(st)) st[i] <- ed[i] # works fine > df <- data.frame(start=st,end=ed) > for(i in 1:dim(df)[1]) df[i,1] <- df[i,2] #takes for ever R: R 2.0.0 (2004-10-04) OS: Linux, Fedora Core 2

Hi, I was trying to evaluate the pearson correlation and the p-values for an nxm matrix, where each row represents a vector. One way to do it would be to iterate through each row, and find its correlation value( and the p-value) with respect to the other rows. Is there some function by which I can use the matrix as input? Ideally, the output would be an nxn matrix, containing the p-values

Dear Rdevelopers The background for this email is that I was helping a PhD student to improve the speed of her R code. I suggested to replace calls like t(AA)%*% BB by crossprod(AA,BB) since I expected this to be faster. The surprising result to me was that this change actually made her code slower. > ## Examples : > > AA <- matrix(rnorm(3000*1000),3000,1000) > BB <-

What is the difference between the above two operations ? [[alternative HTML version deleted]]

Hello, In order to increase the performance of a script I'd like to sort very large vectors containing repeated integer values. I'm not interesting in having the values sorted, but only grouped. I also need the equivalent of index.return from the standard "sort" function: f(c(10,1,10,100,1,10)) => grouped: c(10,10,10,1,1,100) ix: c(1,3,6,2,5,4) is there a way

Hello, The problem I'm working now requires to operate on big matrices. I've noticed that there are some packages that allows to run some commands in parallel. I've tried snow and NetWorkSpaces, without much success (they are far more slower that the normal functions) My problem is very simple, it doesn't require any communication between parallel tasks; only that it divides

Hi, Couldn't is.unsorted() bail out immediately here (after comparing the first 2 elements): > x <- 20000000:1 > system.time(is.unsorted(x), gcFirst=TRUE) user system elapsed 0.084 0.040 0.124 > x <- 200000000:1 > system.time(is.unsorted(x), gcFirst=TRUE) user system elapsed 0.772 0.440 1.214 Thanks! H.

Hi, Encouraged by a tip from Simon Urbanek I tried to use the S3 machinery to write a faster version of the data.frame class. This quickly hits a snag: the "[.default"(x, i) for some reason cares about the dimensionality of x. In the end there is a full transcript of my R session. It includes the motivation for writing the class and the problems I have encountered. As a result I see

Hi, In the example below why is d 10 times bigger than m, according to object.size ? It also takes around 10 times as long to create, which fits with object.size() being truthful. gcinfo(TRUE) also indicates a great deal more garbage collector activity caused by data.frame() than matrix(). $ R --vanilla .... > nr = 1000000 > system.time(m<<-matrix(integer(1), nrow=nr, ncol=2)) [1]

> -----Original Message----- > From: Prof Brian Ripley [mailto:ripley@stats.ox.ac.uk] > Sent: Wednesday, April 27, 2005 1:13 AM > To: Vadim Ogranovich > Cc: Luke Tierney; r-devel@stat.math.ethz.ch > Subject: Re: [Rd] RE: [R] when can we expect Prof Tierney's > compiled R? > > On Tue, 26 Apr 2005, Vadim Ogranovich wrote: > ... > > The arithmetic shows

Hello, is it possible to create my own connection which I could use with read.table or scan ? I would like to create a connection that would read from multiple files in sequence (like if they were concatenated), possibly with an option to skip first n lines of each file. I would like to avoid using platform specific scripts for that... (currently I invoke "/bin/cat" from R to create a

Is there an explanation anywhere of what code that uses a stack map looks like? I'm interested in writing a garbage collector, but it's not clear to me how my code should make use of the stack map format to actually locate roots in memory. -------------- next part -------------- An HTML attachment was scrubbed... URL:

write.table with large data frames takes quite a long time > system.time({ + write.table(df, '/tmp/dftest.txt', row.names=FALSE) + }, gcFirst=TRUE) user system elapsed 97.302 1.532 98.837 A reason is because dimnames is always called, causing 'anonymous' row names to be created as character vectors. Avoiding this in src/library/utils, along the lines of Index:

On Aug 14, 2007, at 4:35 AM, Gordon Henriksen wrote: > On Aug 14, 2007, at 06:24, Gordon Henriksen wrote: > >> The two major problems I had really boil down to identifying GC >> points in machine code and statically identifying live roots at >> those GC points, both problems common to many collection >> techniques. Looking at the problem from that perspective

Hi, I'm trying to write a general-purpose "lexicon" class and associated methods for storing and accessing information about large numbers of specific words (e.g., their frequencies in different genres). Crucial to making such a class practically useful is to get hashing working correctly so that information about specific words can be accessed quickly. But I've never really

Hi, I implemented a garbage collector for a language I wrote in college using the llvm gc statepoint infrastructure. Information for statepoints: https://llvm.org/docs/Statepoints.html Example usage of parsing the llvm stackmap can be found at: https://github.com/dotnet/llilc/blob/master/lib/GcInfo/GcInfo.cpp https://llvm.org/docs/StackMaps.html#stackmap-format