similar to: extracting rows from a data frame by looping over the row names: performance issues

Hi, I'm quite new to the R-project. I was suggested to look into it because I am trying to solve the "Subset sum" problem", which basically is: Given a set of integers and an integer s, does any non-empty subset sum to s? (See http://en.wikipedia.org/wiki/Subset_sum_problem) I have been searching the web for quite some time now (which is how I eventually discovered that my

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 all, In GOOS[1] they use an assertion called assertEventually which samples the system for a success state until a certain timeout has elapsed. This allows you to synchronise the tests with asynchronous code. Do we have an equivalent of that in the Ruby / RSpec world already? I know capybara has wait_until { } but that''s fairly rudimentary - the failure message isn''t very

I am trying to use t.test on the following data: date type INTERVAL nCASES MTF SDF MTO SDO nFST MF nOBS MO MB BIASCV BIASEV ME MAE RMSE CRCF 2001-06-15 avn GE1.00 4385 0.246 0.300 1.502 0.556 1367 1.373 4385 1.502 1.471 0.285 0.164 -1.256 1.266 1.399 0.056 2001-06-15 avn

setTimeLimit(elapsed=1) causes a timeout error whenever a call takes more than one second. For instance, this is how it works on Windows (R 3.3.1): > setTimeLimit(elapsed=1) > Sys.sleep(10); message("done") Error in Sys.sleep(10) : reached elapsed time limit Also, the error propagates immediately and causes an interrupt after ~1 second; > system.time({ Sys.sleep(10);

Considering the python code: for k, v in d.items(): do_something(k); do_something_else(v) I have the following for R: for (i in c(1:length(d))) { do_something(names(d[i])); do_something_else(d[[i]]) } This does not seem seems idiomatic. What is the best way of doing the same with R? Thanks. Luis

There's something weird going on for certain non-integer values of argument 'timeout' to base::socketSelect(). For such values, there is no timeout and you effectively end up with an infinite timeout. I can reproduce this on R 3.3.1 on Ubuntu 16.04 and RedHat 6.6, but not on Windows (via Linux Wine). # 1. In R master session > con <- socketConnection('localhost', port

Hi all, I'm working with a bunch of large graphs, and stumbled across something useful. Probably many of you know this, but I didn't and so others might benefit. Using pch="." speeds up plotting considerably over using symbols. > x <- runif(1000000) > y <- runif(1000000) > system.time(plot(x, y, pch=".")) user system elapsed 1.042 0.030 1.077

Fixed in 73470 Best, Tomas On 10/05/2017 06:11 AM, Henrik Bengtsson wrote: > I'd like to follow up/bump the attention to this bug causing the > timeout to fail for socketSelect() on Unix. It is still there in R > 3.4.2 and R-devel. I've identified the bug in the R source code - the > bug is due to floating-point precisions and comparison using >=. See > PR17203

Hello, Suppose in the following code, PROTECT(sr = R_tryEval( .... )) sr is a RAWSXP vector. I wish to return another RAWSXP starting at position 13 onwards (base=0). I could create another RAWSXP of the correct length and then memcpy the required bytes and length to this new one. However is there a more efficient method? Regards Saptarshi Guha

Hi, I can't load XML_1.4-0.zip in last R devel (Windows): R version 2.5.0 Under development (unstable) (2007-01-05 r40386) Copyright (C) 2007 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for

I would like to use setTimeLimit to abort operations that are stuck waiting (idle) after n seconds. Below a toy example in which Sys.sleep is a placeholder call that is idle: testlimit <- function(){ setTimeLimit(elapsed=3, transient=TRUE); Sys.sleep(10); } system.time(testlimit()); However this is giving inconsistent results. On windows and in r-studio server (linux) the call is

Hello -- I would like to know of a more efficient way of writing the following piece of code. Thanks. options(stringsAsFactors=FALSE) orig <- c(rep('11111111',100000),rep('22222222',200000),rep('33333333',300000),rep('44444444',400000)) orig.unique <- unique(orig) system.time(df <- as.data.frame(sapply(orig.unique, function(x) ifelse(orig==x, 1, 0))))

Hi all, Can anyone help me understand why an implicit print (i.e. just typing df at the console), is so much slower than an explicit print (i.e. print(df)) in the example below? I see the difference in both Rstudio and in a terminal. # Construct large df as quickly as possible dummy <- 1:18e6 df <- lapply(1:10, function(x) dummy) names(df) <- letters[1:10] class(df) <-

Hi, FYI, I just notice that on Windows (but not Linux) it is orders of magnitude (below it's 50x) faster to serialize() and object to a temporary file and then read it back, than to serialize to an object directly. This has for instance impact on how fast digest::digest() can provide a checksum. Example: x <- 1:1e7; t1 <- system.time(raw1 <- serialize(x, connection=NULL));

foo <- rnorm(30*34*12) dim(foo) <- c(30, 34, 12) I want to make a data.frame out of this three-dimensional array. Each dimension will be a variabel (column) in the data.frame. I know how this can be done in a very slow way using for loops, like this: x <- rep(seq(from = 1, to = 30), 34) y <- as.vector(sapply(1:34, function(x) {rep(x, 30)})) month <- as.vector(sapply(1:12,

... and the mailing list is picky about attachments... whatever you attached did not conform to the stringent requirements mentioned in the Posting Guide. Pasting the code right into the email is usually safest, though you DO have to post using plain text (as the Posting Guide indicates) or your code may get mangled by the automatic html format removal. On May 15, 2018 7:04:31 AM PDT, Bert Gunter

Hello, colnames seems to be not optimized well for data.frame. It escapes processing for data.frame in if (is.data.frame(x) && do.NULL) return(names(x)) but only when do.NULL true. This makes huge difference when do.NULL false. Minimal edit to `colnames`: if (is.data.frame(x)) { nm <- names(x) if (do.NULL || !is.null(nm)) return(nm) else

Hi, everyone. I need to run lm with the same response vector but with varying predictor vectors. (i.e. 1 response vector on each individual 6,000 predictor vectors) After looking through the R archive, I found roughly 3 methods that has been suggested. Unfortunately, I need to run this task multiple times(~ 5,000 times) and would like to find a faster way than the existing methods. All three

Hi, the function stats::convolve does not mention efficient usage of the underlying FFT algorithm, such as (a) if type="circular", then length(x)=length(y) should have many factors (e.g. length(x) = length(y) = 2^n) (b) if type="open" or "filter", then length(x)+length(y)-1 should have many factors (e.g. length(x)+length(y)-1 = 2^n) In particular the latter may