similar to: Help with Profiling Memory Use in R with summaryRprof

Hi All, I am a new user of R currently trying to profile memory usage of some R code with summaryRprof in R version 2.7.2 in Windows. If I use the memory = "both" option in summaryRprof(), I have no problems viewing the profiling of both the time and memory usage. However if I try to use memory = "stats," I get the following error: Error in tapply(1:4369L, list(index =

Hi all, I have some long-running code that I'm trying to profile. I am seeing a lot of time spent inside the <Anonymous> function. Of course, this can in fact be any of several functions, but I am unable to see how I could use the information from Rprof.out to discern which function is taking the most time. An example line from my Rprof.out is: rbernoulli <Anonymous>

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

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

I'm trying to configure Version 2.12.0 or R to do memory profiling. I've reconfigured the code: % ./compile --enable-memory-profiling=YES and verified that it's configured correctly by examining the output. I then rebuild R: % make Then I fire up R and run a script, using Rprof with the memory-profiling switch set to TRUE: Rprof("output", memory.profiling=TRUE); # a

Yes. It should have something to do with read/write permissions, but it is not clear how it happens. I can write file to C drive using R. I usually write my results matrix to a txt file in C drive. For Rprof(), the boot.out file can be created, but only with one line sample.interval=20000 The situation is the same even if I specify the directory to the D drive,where I have the full

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

Hi, >From reading ?Rprof, I checked R CMD Rprof --help and learned that there are options to specify the min % to print. This is currently (R-devel r52975) displayed with the --help option as --min%total minimum % to print for 'by total' --min%self minimum % to print for 'by self' So I tried R CMD Rprof --min%total 5 and got an error. After looking at

Sorry, this might be a really basic question, but I'm trying to interpret the results from memory profiling, and I have a few questions (marked by *Q#*). From the summaryRprof() documentation, it seems that the four columns of statistics that are reported when setting memory.profiling=TRUE are - vector memory in small blocks on the R heap - vector memory in large blocks (from malloc) - memory

Hi folks, I'm attempting to use the EMD package to analyze some neuroimaging data (timeseries with 64 channels sampled across 1 million time points within each of 20 people). I found that processing a single channel of data using EMD::emd() took about 8 hours. Exploration using Rprof() suggested that most of the compute time was spent in EMD::extrema(). Looking at the code for EMD:extrema(),

Dear R helpers, I have what another member on this forum described as an embarrassingly parallel problem. I am trying to fit models on subsets of some data based on unique combinations of two id factors in the dataset. Total number of combinations is 30^5, and this takes a long time. So, I would like fit models for each of the datasets produced by subsetting on the unique combinations, splitting

Dear Jim, Here is again exactly what I did and with the output of Rprof (with this reduced dataset and with a simpler function, it is here much faster than in real life). Thanks you again for your help! ## CODE ## mydata1<- structure(list(species = structure(1:8, .Label = c("alsen","gogor", "loalb", "mafas", "pacyn", "patro",

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:

Hello R-sig-debian and (hopefully) Dirk: On Debian wheezy, I have the R packaging that CRAN (you) provide. I run into a little trouble while trying to fiddle with alternative BLAS. I know you and I went around on this last year and I think perhaps I've found something wrong in the framework, or I've just done something wrong. I installed the packages openblas-base and openblas-dev, and

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 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

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

Dear R's, I have been using R lately to perform some statistical analysis and, based on them, simulations to be exported in flat text files to other programs. These text files are nowadays of about 30MB in size, but they could finally be of up to 300MB. Writing these files with either write.table or write.matrix was desperately slow and the bottleneck of the whole process. Besides, the

Hello! I have run Rprof on a function of mine and the results look very strange, to say the least. At the end I of this email is an output of summaryRprof. Can someone help me interpret this output? I have read the appropriate section in the manual "Writing R Extensions" and help pages. If I understand this output correctly, it is saying that "unlist" has been active in

PROFILE OUTPUT PROCESSING TOOLS FOR R ===================================== This package provides some simple tools for examining Rprof output and, in particular, extracting and viewing call graph information. Call graph information, including which direct calls where observed and how much time was spent in these calls, can be very useful in identifying performance bottlenecks.