similar to: More simple implementation is slow.

Dear Rs: Hi, I am trying to read a big text file (nrows=243440, ncols=144). It seems the computational time of all the read methods (scan,readtable,read.delim) is not linear to the number of rows I want to read in: things became really slow once I tried to read in 100000 lines compare to 10000 lines). If I am reading the profiling result right, I guess scan wouldn't help either. My

Hi, I'm running some routines with standard matrix operations like solve() and diag(). When I do a profile, the lead item under total time is standardGeneric(). Furthermore, solve() and diag() have much greater total time than self time. ??? I assume there is some time-consuming decision going on in the usual functions; is there any way to avoid that and go straight to the calculaions? Thanks

Hello, (This is follow up from this thread: http://www.nabble.com/execution-time-of-.packages-td22304833.html but with a different focus) I am often confused by the result of the profiler, when a loop is involved. Consider these two scripts: script1: Rprof( ) x <- numeric( ) for( i in 1:10000){ x <- c( x, rnorm(10) ) } Rprof( NULL ) print( summaryRprof( ) ) script2:

I have some trouble interpreting the output from profiling. I have read the help pages Rprof, summaryRprof and consult the R extensions manual, but I still have problems understanding the output. Basically the output consist of self.time and total.time. I have the understanding that total.time is the time spent in a given function including any subcalls or child functions or whatever the

Hello, This is about the new "line profiling" feature in R 3.0.0. As I was testing it, I find the results somewhat disappointing so I'd like to get your opinion. I put some poorly written code in a test.R file, here are the contents: double <- function(x) { out <- c() for (i in x) { out <- c(out, 2*i) # line 4 } return(out) } Then this how I source the file

Short: get rid of the loops I use and optimize runtime Dear all, I want to calculate for each row the amount of the month ago. I use a matrix with 2100 rows and 22 colums (which is still a very small matrix. nrows of other matrixes can easily be more then 100000) Table before Year month quarter yearmonth Service ... Amount 2009 9 Q3 092009 A ...

** Disclaimer: I'm looking for general suggestions ** I'm sorry, but can't send out the file I'm using, so there is no reproducible example. I'm using read.table and it's taking over 30 seconds to read a tiny file. The strange thing is that it takes roughly the same amount of time if the file is 100 times larger. After re-reviewing the data Import / Export manual I think

Hello there, I am not quite sure how to interpret the output of Rprof (in the following the output I was staring at). I was poking around the web a little bit for documentation but without much success. I guess if I want to figure out what takes so long in my code the 2nd table $by.total and the total.pct column (pct = percent) is the most helpful. What does it mean that [ or [.data.frame is

Hello, I am looking for more information about lazy loading introduced in R 2.0.0. Doing ?lazyLoad I got some and there is a 'see also' section that points to 'makeLazyLoading'... But I cannot reach this page. My problem is: I recompiled a library that uses a lot of functions from other libraries (of course I can give details if needed). I load it in my computer: library(svGUI),

Hello, Appending information to the profiler's output seems to generate problems. Here is a small example of code : <code r> require(boot) Rprof( memory.profiling = TRUE) Rprof(NULL) for(i in 1:2){ Rprof( memory.profiling = TRUE, append = TRUE) example(boot) Rprof(NULL) } </code> The problem is that the file Rprof.out contains more than once the header information: $ grep

I have noticed a significant performance degradation using merge in 2.9.1 relative to 2.8.1. Here is what I observed: N <- 100000 X <- data.frame(group=rep(12:1, each=N), mon=rep(rev(month.abb), each=N)) X$mon <- as.character(X$mon) Y <- data.frame(mon=month.abb, letter=letters[1:12]) Y$mon <- as.character(Y$mon) Z <- cbind(Y, group=1:12) system.time(Out

Hello, I am forwarding this from ESS mailing list, as it's a failure of internal R completion system: This fails: utils:::.assignLinebuffer('iris[iris$Spec') utils:::.assignEnd(15) utils:::.guessTokenFromLine() utils:::.completeToken() utils:::.retrieveCompletions() ## -> [1] "iris[iris$Spec" This works utils:::.assignLinebuffer('iris[ iris$Spec') # note the

Rprof seems to ignore all time spent inside system() calls. E.g., this simple example actually takes about 10 seconds, but Rprof thinks the total time is only 0.12 seconds: > Rprof("sleep-system.out") ; system.time(system(command="sleep 10")) ; Rprof(NULL) user system elapsed 0.000 0.004 10.015 > summaryRprof("sleep-system.out")$by.total

Hi, Is nchar function knowingly slow in R? I'm doing some string formatting that requires multiple call to nchar, and nchar seems to be very slow. Experiment 1, pass nchar inside sprintf, and it takes 0.7 seconds > system.time(for (i in 1:10000) + str = sprintf('0005%020d', nchar(op)) + )[3] [1] 0.7 Experiment 2, get the length of op separately using nchar, and then pass

Dear R-help, A colleague of mine was running some code on two of our boxes, and noticed a rather large difference in running time. We've so far isolated the problem to the difference between R 1.7.1 and 1.8.0, but not more than that. The exact same code took 933.5 seconds in 1.7.1, and 3594.4 seconds in 1.8.1, on the same box. Basically, the code calls boot() to bootstrap fitting mixture

Dear All, I regularly want to "apply" some function to an array in a way that the arguments to the user function depend on the index on which the apply is working. A simple example is: A <- array( runif(160), dim=c(5,4,8) ) x <- matrix( runif(32), nrow=4, ncol=8 ) b <- runif(8) f1 <- function( A, x, b ) { sum( A %*% x ) + b } result <- rep(0.0,8) for (i in 1:8) {

Hi List, I'm hoping to get opinions for enhancing the efficiency of the following code designed to take a vector of probabilities (outcomes) and calculate a union of the probability space. As part of the union calculation, combn() must be used, which returns a matrix, and the parallelized version of lapply() provided in the multicore package requires a list. I've found that

Hi all, I'm using R to find duplicates in a set of 6 files containing Part Number information. Before applying the intersect method to identify the duplicates I need to normalize the P/Ns. Converting the P/N to uppercase if alphanumerical and applying an 18 char long zero padding if numerical. When I apply the pn_formatting function (see code below) to "Part Number" column of the

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

Hi, I am trying to use a CGI service (Pubchem PUG) via RCurl and am running into a problem where the data must be supplied via POST - but I don't know the keyword for the argument. The data to be sent is an XML fragment. I can do this via the command line using curl: I save the XML string to a file called query.xml and then do curl -d @query.xml