similar to: which() vs. just logical selection in df

Hi Dr. Snow, & R-helpers, Thank you for your reply! I hadn't heard of the {microbenchmark} package & was excited to try it! Thank you for the suggestion! I did check the reference source for which() beforehand, which included the statement to remove NAa, and I didn't have any missing values or NAs: sum(is.na(dat$gender2)) sum(is.na(dat$gender)) sum(is.na(dat$y)) [1] 0 [1] 0 [1]

Hello R users I have a question with Kaplan-Meier Curve with respect to my research. We have done a retrospective study on fillings in the tooth and their survival in relation to the many influencing factors. We had a long follow-up time (upto 8yrs for some variables). However, we decided to stop the analysis at the 6year follow up time, so that we can have uniform follow-up time for all the

Dear Rdevelopers The background for this email is that I was helping a PhD student to improve the speed of her R code. I suggested to replace calls like t(AA)%*% BB by crossprod(AA,BB) since I expected this to be faster. The surprising result to me was that this change actually made her code slower. > ## Examples : > > AA <- matrix(rnorm(3000*1000),3000,1000) > BB <-

Hi, Couldn't is.unsorted() bail out immediately here (after comparing the first 2 elements): > x <- 20000000:1 > system.time(is.unsorted(x), gcFirst=TRUE) user system elapsed 0.084 0.040 0.124 > x <- 200000000:1 > system.time(is.unsorted(x), gcFirst=TRUE) user system elapsed 0.772 0.440 1.214 Thanks! H.

write.table with large data frames takes quite a long time > system.time({ + write.table(df, '/tmp/dftest.txt', row.names=FALSE) + }, gcFirst=TRUE) user system elapsed 97.302 1.532 98.837 A reason is because dimnames is always called, causing 'anonymous' row names to be created as character vectors. Avoiding this in src/library/utils, along the lines of Index:

> -----Original Message----- > From: Prof Brian Ripley [mailto:ripley@stats.ox.ac.uk] > Sent: Wednesday, April 27, 2005 1:13 AM > To: Vadim Ogranovich > Cc: Luke Tierney; r-devel@stat.math.ethz.ch > Subject: Re: [Rd] RE: [R] when can we expect Prof Tierney's > compiled R? > > On Tue, 26 Apr 2005, Vadim Ogranovich wrote: > ... > > The arithmetic shows

Hi, I'm trying to write a general-purpose "lexicon" class and associated methods for storing and accessing information about large numbers of specific words (e.g., their frequencies in different genres). Crucial to making such a class practically useful is to get hashing working correctly so that information about specific words can be accessed quickly. But I've never really

Inline. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Oct 14, 2020 at 3:23 PM 1/k^c <kchamberln at gmail.com> wrote: Is which() invoking c-level code by chance, making it slightly faster > on average? > You do not need

Hi, Encouraged by a tip from Simon Urbanek I tried to use the S3 machinery to write a faster version of the data.frame class. This quickly hits a snag: the "[.default"(x, i) for some reason cares about the dimensionality of x. In the end there is a full transcript of my R session. It includes the motivation for writing the class and the problems I have encountered. As a result I see

Full_Name: Alessandra Iacobucci Version: 2.5.1 OS: Mac OS X 10.4.11 Submission from: (NULL) (193.48.71.92) Hi, I am making some intensive simulations for the testing of a Population Monte Carlo algorithm. This involves also a study of the CPU times in two different case. What I am trying to measure is the "real" CPU time, the one which is independent on the %CPU. I'm using the

This creates a named vector of length nx, then repeatedly draws a single sample from it. lkup <- function(nx, m=10000L) { tbl <- seq_len(nx) names(tbl) <- as.character(tbl) v <- sample(names(tbl), m, replace=TRUE) system.time(for(k in v) tbl[k], gcFirst=TRUE) } There is an abrupt performance degredation at nx=1000 > lkup(1000) user system elapsed 0.180

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, I have a 5x731 array A, and I want to compute the sums of the columns. Currently I do: apply(A, 2, sum) But it turns out, this is slow: 70% of my CPU time is spent here, even though there are many complicated steps in my computation. Is there a faster way? Thanks, Martin

Hi, I am experiencing a long delay when using dataframes inside loops and was wordering if this is a bug or not. Example code: > st <- rep(1,100000) > ed <- rep(2,100000) > for(i in 1:length(st)) st[i] <- ed[i] # works fine > df <- data.frame(start=st,end=ed) > for(i in 1:dim(df)[1]) df[i,1] <- df[i,2] #takes for ever R: R 2.0.0 (2004-10-04) OS: Linux, Fedora Core 2

hi all If I wanna get the total number of lines in a big file without reading the file's content into R as matrix or data frame, any methods or functions? thanks in advance. Regards

Hi, I was trying to evaluate the pearson correlation and the p-values for an nxm matrix, where each row represents a vector. One way to do it would be to iterate through each row, and find its correlation value( and the p-value) with respect to the other rows. Is there some function by which I can use the matrix as input? Ideally, the output would be an nxn matrix, containing the p-values

What is the difference between the above two operations ? [[alternative HTML version deleted]]

Hello, is it possible to create my own connection which I could use with read.table or scan ? I would like to create a connection that would read from multiple files in sequence (like if they were concatenated), possibly with an option to skip first n lines of each file. I would like to avoid using platform specific scripts for that... (currently I invoke "/bin/cat" from R to create a

Hello, In order to increase the performance of a script I'd like to sort very large vectors containing repeated integer values. I'm not interesting in having the values sorted, but only grouped. I also need the equivalent of index.return from the standard "sort" function: f(c(10,1,10,100,1,10)) => grouped: c(10,10,10,1,1,100) ix: c(1,3,6,2,5,4) is there a way

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of > Ted.Harding at nessie.mcc.ac.uk > Sent: Sunday, December 05, 2004 7:14 PM > To: r-help at stat.math.ethz.ch > Subject: Re: [R] What is the most useful way to detect > nonlinearity in lo > > > On 05-Dec-04 Peter Dalgaard wrote: