similar to: Functions with the same name: best practices

Hi all, I've been trying to learn more about memory profiling in R and I've been trying memory profiling out on read.table. I'm getting a bit of a strange result, and I hope that someone might be able to explain why. After running Rprof("read-table.prof", memory.profiling = TRUE, line.profiling = TRUE, gc.profiling = TRUE, interval = interval) diamonds <-

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

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

This looks like a bug? Unable to use scan(pipe()) while profiling. I have no idea whether this version of R violates the "do not use `Rprof' in an executable built for profiling" warning in ?Rprof. Thanks -Don > version _ platform powerpc-apple-darwin1.4 arch powerpc os darwin1.4 system powerpc, darwin1.4 status Patched major 1 minor 3.1 year

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(),

Hi Consider the following example: f <- function(expr) g(expr) g <- function(expr) { ? h(expr) } h <- function(expr) { ? expr # evaluation happens here ? i(expr) } i <- function(expr) { ? expr # already evaluated, no costs here ? invisible() } rprof <- tempfile() Rprof(rprof) f(replicate(1e2, sample.int(1e4))) Rprof(NULL) cat(readLines(rprof), sep = "\n") #>

I am using Rprof() to help find ways to improve performance. I found a function whose total seconds and self seconds were large. I replaced it with something else. The something else had a small number of total seconds and self seconds. But the total time did not decrease. I don't understand how that could be, and would appreciate any suggestions. Thanks -Don Details, unfortunately

I'm trying to use Rprof() to identify bottlenecks and speed up a particullary slow section of code which reads in a portion of a tif file and compares each of the values to values of predictors used for model fitting. I've written up an example that anyone can run. Generally temp would be a section of a tif read into a data.frame and used later for other processing. The first portion

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

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>

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 noticed this oddity about R profiling and setMethod. First, I "test out" Rprof. > require(methods) Loading required package: methods [1] TRUE > > Rprof("test.out") > data.frame("a") X.a. 1 a > Rprof(NULL) So far, so good. Next, I define myClass. > setClass("myClass", representation(mySlot = "numeric")) [1]

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

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

Can anyone help me with doing searches using thesaurus. I really want to do searches that are simple that I make up. For example, a search on "TV" will bring back results that include "Television" and vice versa. Any help appreciated. Clare -- Posted via http://www.ruby-forum.com/.

Dear useRs, First off, sorry about the long post. Figured it's better to give context to get good answers (I hope!). Some time ago I wrote an R function that will get all pairwise interactions of variables in a data frame. This worked fine at the time, but now a colleague would like me to do this with a much larger dataset. They don't know how many variables they are going to have in the

Hi I want to add a row to a "data.table" in each round of a for loop. "rbind" seems to be a inefficient way to implement this. How would you do this? The "slow" solution: library(data.table) Rprof("test.out") dt <- data.table() for (i in (1:10000)) { # algorithm that generates a list with different values, # but same key-names, each round, for

Dear List, I'm trying to figure out some best practice way with respect to the naming conventions when building own packages. I'd like to minimize the risk of choosing function names that might interfere with those of other packages (both available ones and those yet to come). I came up with following alternatives 1. Prefixing the actual names (e.g. myPkgfoo() instead of foo()): pretty

Hello everyone, I'm trying to parse a very large XML file using SAX with the XML package (i.e., mainly the xmlEventParsing function). This function takes as an argument a list of other functions (handlers) that will be called to handle particular xml nodes. If when I use Rprof(), all the handler functions are lumped together under the <anonymous> label, and I get something like this: