search for: mcappli

Dear all, I have about 50 pages of R codes and ran it in both OS X and Windows. It takes at least haft a day to have the results. The running time is not very different in both Systems. I found that R does not use all cores of CPU by default. Can anybody help me to use all cores of CPU in my programming from the beginning or through programming in both OS X and Windows? Many thanks your

Hi, I have an R script which is time consuming because it has two nested loops in it of at least 5000 iterations each, I have tried to use the multicore package but id doesn't seem to improve the elapsed time of the script(a shorter script for example) and I can't use the mcapply because of technical reasons. I was wondering how can I make my script use more cores and memory because I am

Hi, I have an R script which is time consuming because it has two nested loops in it of at least 5000 iterations each, I have tried to use the multicore package but id doesn't seem to improve the elapsed time of the script(a shorter script for example) and I can't use the mcapply because of technical reasons. I was wondering how can I make my script use more cores and memory because I am

About to try this implementation. As a follow-up, this is the exact error: Lost warning messages Error: no more error handlers available (recursive errors?); invoking 'abort' restart Execution halted Error: cons memory exhausted (limit reached?) Error: cons memory exhausted (limit reached?) Error: cons memory exhausted (limit reached?) Error: cons memory exhausted (limit reached?)

It's Redhat Enterprise Linux 9 Specifically: OS Information: NAME="Red Hat Enterprise Linux" VERSION="9.3 (Plow)" ID="rhel" ID_LIKE="fedora" VERSION_ID="9.3" PLATFORM_ID="platform:el9" PRETTY_NAME="Red Hat Enterprise Linux 9.3 (Plow)" ANSI_COLOR="0;31" LOGO="fedora-logo-icon"

Thomas, I'm curious - what OS are you running this on, and how much memory does the computer have?? Let me know if that code worked out as I hoped. regards, gregg On Wednesday, December 11th, 2024 at 6:51 AM, Deramus, Thomas Patrick <tderamus at mgb.org> wrote: > About to try this implementation. > > As a follow-up, this is the exact error: > > Lost warning

Hello Thomas, Consider that the primary bottleneck may be tied to memory usage and the complexity of pivoting extremely large datasets into wide formats with tens of thousands of unique values per column. Extremely large expansions of columns inherently stress both memory and CPU, and splitting into 110k separate data frames before pivoting and combining them again is likely causing resource

How is the server configured to handle memory distribution for individual users. I see it has over 700GB of total system memory, but how much can be assigned it each individual user? AAgain - just curious, and wondering how much memory was assigned to your instance when you were running R. regards, Gregg On Wednesday, December 11th, 2024 at 9:49 AM, Deramus, Thomas Patrick <tderamus at

Hi Gregg. Just wanted to follow up on the solution you proposed. I had to make some adjustments to get exactly what I wanted, but it works, and takes about 15 minutes on our server configuration: temp <- ??????open_dataset( ????????????sources = input_files, ????????????format = 'csv', ????????????unify_schema = TRUE, ????????????col_types = schema( ????????????"ID_Key"

Hi Thomas, Glad to hear the suggestion helped, and that switching to a `data.table` approach reduced the processing time and memory overhead?15 minutes for one of the smaller datasets is certainly better! Sounds like the adjustments you devised, especially keeping the multicore approach for `make_clean_names()` and ensuring that `ID_Key` values remain intact, were the missing components you

No es un problema de tm ni de SnowfallC ni de mcapply (por el path utilizas linux, en windows mcapply según el manual no va bien) No defines bien los objetos que pasas. Pasas doc.corpus en lugar de corpus ( o asignas a corpus en lugar de a doc.corpus) . Depura los programas cuando salga un error de objeto, como te pone en el Error que pasas . Temporalmente lo tienes arreglado en

Creo que lo que quieres hacer necesita esta línea de código justo después de cargar el paquete tm: inmortal = unlist(strsplit(inmortal, " ", fixed = T)) De esta forma, trabajas con palabras, y NO con las frases enteras... Un saludo Isidro Hidalgo Arellano Observatorio Regional de Empleo Consejería de Empleo y Economía http://www.jccm.es > -----Mensaje original----- > De:

Hi R users. Apologies for the lack of concrete examples because the dataset is large, and it being so I believe is the issue. I multiple, very large datasets for which I need to generate 0/1 absence/presence columns Some include over 200M rows, with two columns that need presence/absence columns based on the strings contained within them, as an example, one set has ~29k unique values and the

Dear all, I would like to ask your help understand the subsequent steps for making my program faster. The following code: Gauslist<-array(data=NA,dim=c(dimx,dimy,dimz)) for (i in c(1:dimz)){ print(sprintf('Creating the %d map',i)); Gauslist[,,i]<-f <- GaussRF(x=x, y=y, model=model, grid=TRUE,param=c(mean,variance,nugget,scale,Whit.alpha)) } creates 100 GaussMaps (each

Muchas gracias isidro, a la noche reinstalo R y os digo si me ha funcionado. Perdona mi ignorancia de novato pero no he entendido muy bien eso de avisar al desarrollador. Entiendo que es a los de los paquetes, no? un saludo! ruben El 18 de junio de 2014, 13:10, Isidro Hidalgo <ihidalgo@jccm.es> escribió: > Ya he visto que tampoco así funciona. > Sí te puedo decir que me ha dejado

dear R experts. about once a year, I wonder where GPU computing is. this is of course a quickly moving field, so I am hoping to elicit some advice from those that have followed the developments more actively. fortunately, unlike my many other cries for help, this post is not critical. it is more curiosity. Integration: for me, I want to substitute the basic statistical functions with GPU

Hi all, I am trying to implement a Monte-Carlo simulation for each cell in a spatial matrix (using mcd2 package) . I have figured out how to conduct the simulation using data from a single location (where I manually input distribution parameters into the R code), but am having trouble (a) adjusting the code to pull input variables from my various data sets and then (b) applying the entire