similar to: New package: profr 0.1 - an alternative display for profiling information

Hello everybody, as a young/unexperienced R package developer (only a few, mainly for personal use) i was thinking it could be very useful having a "meta" task view for all package-development related packages and/or function. Something like ... Creation - utils::package.skeleton, pkgKitten, Rcpp::Rcpp.package.skeleton Foreign languages interfaces: - Rcpp Documentation - roxygen2

Hi all, The profr package provides a method for displaying its output with ggplot: ggplot.print. You don't need this ggplot2 to use profr, so ggplot2 is listed under enhances in the DESCRIPTION file. If I have just S3method(ggplot, profr) in my NAMESPACE, then I get: ** testing if installed package can be loaded Error : object 'ggplot' not found whilst loading namespace

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 <-

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

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

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") #>

Hi, I have searched R mail list archive and couldn't find my answers. The R extension describes how to make use of Rprof to profile R code. gprof can be also used for the same purpose for the C codes when the C codes are written independently and provided with a main() function. I'm currently writing R codes meshed with C Codes, and use .Call as the interface between the two parts.

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

clusterfly http://had.co.nz/clusterfly/ Typically, there is somewhat of a divide between statistics and visualisation software. Statistics software, particularly R, provides implementation of cutting edge research methods, but limited graphics. Visualisation software will provide sophisticated visual interfaces, but few statistical algorithms. The clusterfly package presents some early

clusterfly http://had.co.nz/clusterfly/ Typically, there is somewhat of a divide between statistics and visualisation software. Statistics software, particularly R, provides implementation of cutting edge research methods, but limited graphics. Visualisation software will provide sophisticated visual interfaces, but few statistical algorithms. The clusterfly package presents some early

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 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>

Hi all, Mango have released their updated training schedule which includes new courses and new locations. Hadley Wickham will also be coming to London to teach Data Visualisation in R. Please see details below: Public Courses Our public courses include introductory to advanced training in general data analysis through to industry specific data analysis. Attendees of our public courses receive

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

Ok, here's another best practices question - let's say I'm writing a package and I want to use a function name that is already claimed by a function in the base R packages. For the sake of argument, let's pretend this function is for profiling the performance of a function (like Rprof for example), and so an obvious name that comes to mind is profile. This, of course, clashes

Hi, I've been experimenting with a new way of displaying the output from RProf, to make it easier to optimise your functions. I've included an example below. I'd love to get your feedback on how easy you think this graphic is to read, and on ways that it could be improved. install.packages("butler") library(butler) # profile the glm example profile_glm <-

Full_Name: John Garvin Version: 1.9.0 OS: Linux Submission from: (NULL) (128.42.129.78) This may or may not technically be a bug, but it's certainly an annoyance. Rprof only takes into account computation that occurs inside functions. If a time-consuming operation occurs outside a function, it doesn't record the time it takes. Consider this program 'array.r': Rprof() foo <-

Hi all, Version 0.2.2 of the googVis package has been released on CRAN and will be available from your local CRAN mirror in due course. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google. We presented our initial ideas on

Hi all, Version 0.2.2 of the googVis package has been released on CRAN and will be available from your local CRAN mirror in due course. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google. We presented our initial ideas on

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