similar to: List comprehensions for R

Laurent, Thank you for introducing me to a package I did not know existed as I use features like list comprehension in python all the time and could see using it in R now that I know it is available. As to why you see your example as slow, I see you used a fairly complex and nested expression and wonder if it was a better way to go. As you are dealing with an interpreter doing delayed

Dear RHelp-list, ?? I try to use the package comprehenr to replace a for loop by a list comprehension. ?I wrote the code but I certainly miss something because it is very slower compared to the for loops. May you please explain to me why the list comprehension is slower in my case. Here is my example. I do the calculation of the square difference between the values of two vectors vec1 and

I would be more strong on this advice: learn to think in R, rather than thinking in Python, when programming in R. R has atomic vectors... Python does not (until you import a package that implements them). I find that while it is possible to import R thinking into Python, Python programmers seem to object for stylistic reasons even though such thinking speeds up Python also. A key step in that

I fully agree with Jeff that the best way to use ANY language is to evaluate the language in terms of not just the capabilities it offers but also the philosophy behind what it was created for and how people do things and just grok it and use it mostly in the way intended. I do that with all the languages I learn, whether for computers or humans. Bringing in something you like from another

This can be vectorized. Try ix <- seq_along(vec2) S_diff2 <- sapply(seq_len(N1-(N2-1)*ratio_sampling), \(j) sum((vec1[(ix-1)*ratio_sampling+j] - vec2[ix])**2)) On Sun, Jun 16, 2024 at 11:27?AM Laurent Rhelp <laurentRHelp at free.fr> wrote: > > Dear RHelp-list, > > I try to use the package comprehenr to replace a for loop by a list > comprehension. > > I

Full_Name: Manuel L?pez-Ib??ez Version: R version 2.6.2 (2008-02-08) OS: linux-gnu Submission from: (NULL) (164.15.10.156) This is only relevant for CPU intensive child processes. Otherwise, the problem is not obvious. Therefore, we need a CPU intensive program like this one: /************************************/ /*** Compile with: gcc -o timer-test -O0 timer-test.c -lm */ #include

Hi, I would like to use the multinomRob function to test election results. However, depending on which independent variables I include and how many categories I have in the dependent variable, the model cannot be estimated. My data look like this (there are 68 observations): > head(database) RESTE09 GAUCHE09 PDC09 PLR09 UDC09 MCG09 RESTE05 GAUCHE05 PDC05 D1 1455

Dear R listers, I am trying to create a new variable that uniquely identifies groups of observations in a dataset. So far I couldn't figure out how to do this in R. In Stata I would simply type: egen newvar = group(dim1, dim2, dim3) Please, find below a quick example to show what I am dealing with: I have a dataset with 4 variables: var <- runif(50) ## a variable that I want to group

My goal is simple: calcuate GC content of each sequence in a list of nucleotide sequences. I have figured out how to vectorize, but all my attempts at memoization failed. Can you show me how to properly memoize my function? There is a StackOverflow post on the subject of memoization, but it does not help me: http://stackoverflow.com/questions/7262485/options-for-caching-memoization-hashing-in-r

Dear R-help community, I am working with multidimensional contingency tables and I am having trouble getting loglm to run on all dimensions without typing out each dimension. I have generated random data and provided output for the results I want below: d1.c1 <- rnorm(20, .10, .02) d1.c2 <- rnorm(20, .10, .02) d2.c1 <- rnorm(20, .09, .02) d2.c2 <- rnorm(20, .09, .02) d3.c1 <-

Hello all, I have been using the function ordirgl to plot 3D dynamic ordinations. The ordirgl function works just fine. IN fact, I was even able to write a function that allows me to identify points in the 3D plot: identify.rgl<-function(env_var,ord,dim1,dim2,dim3) { tmp<-select3d(button="left") tmp.keep<-tmp(ord[,dim1],ord[,dim2],ord[,dim3])

Hi, I'm sometimes getting a failure of connecting 2 nodes when Tinc is started and configured in a LAN. In the logs, there are some unexpected dropped packets with very high or negative seq. I can reproduce this issue ~2% of the time. When this happens, the 2 nodes can no longer ping or ssh each other through the tunnel interface but using eth0 works fine. The connection can recover after at

Hi - When I'm trying to read in a text file into a labeled character array, the memory stamp/footprint of R will exceed 4 gigs or more. I've seen this behavior on Mac OS X, Linux for AMD_64 and X86_64., and the R versions are 2.4, 2.4 and 2.2, respectively. So, it would seem that this is platform and R version independant. The file that I'm reading contains the upstream regions

Dear all, I had to extracts/set elements from/in a matrix. Let say I have two vectors dim1 and dim2 of indices in the respective two dimensions of a matrix: I want to extract all the corresponding elements. I the case of a nxn matrix, dim1 <- 1:n and dim2 <- 1:n would extract the diagonal. I know one way would be to use the functions 'row' and 'col', but the matrixes I

Dear R users, I'am using marray and Limma packages to analyze genepix output. 1) how can I filter bad spots from my data (data contains 3 types of bad spots). my experiment contains 12 samples and the bad spot are not associated to the same probes 2) how can I remove control probes from my data ? I'm sorry, i'm new with R and I can't find answer in packages doc. best regards,

Ok, I have a new, multipart problem that I need help figuring out. Part 1. I have a three dimensional array (species, sites, repeat counts within sites). Sampling effort per site varies so the array should be ragged. Maximum number of visits at any site = 22 Number of species = 161 Number of sites = 56 I generated the array first by;

Hi, See my example below. a<-array(1:12,c(2,3,2)) > a , , 1 [,1] [,2] [,3] [1,] 1 3 5 [2,] 2 4 6 , , 2 [,1] [,2] [,3] [1,] 7 9 11 [2,] 8 10 12 I want to get a result something like dim1 dim2 dim3 elements 1 1 1 1 2 1 1 3 2 2 1 2 I tried the data.frame and expand.grid functions,

Hello R experts, I've used metaMDS to run NMDS on some fish abundance data, and am also working on correlating environmental data to the NMDS coordinates. I'm fairly new to metaMDS and NMDS in general, so I have what are probably some very basic questions. My fish abundance data consists of 66 sites for which up to 20 species of fish were identified and counted. I ran metaMDS on this data

Hello everyone, I am not sure if this should go on the general R mailing list (for example, if there is a text mining solution that might work here) or the bioconductor mailing list (since I wasn't able to find a solution to my question on searching their lists) - so this time I tried both, and in the future I'll know better (in case it should go to only one of the two). The task

I've found a problem when using categorical variables in lmp() from package lmPerm According to help(lmp): "This function will behave identically to lm() if the following parameters are set: perm="", seq=TRUE, center=FALSE.") But not in the case of including categorical variables: require(lmPerm) set.seed(42) testx1 <- rnorm(100,10,5) testx2 <-