similar to: itertools 0.1-1

Hi, Try: set.seed(25) X<- matrix(sample(1:50,3*140,replace=TRUE),nrow=140) #either res1<-do.call(rbind,lapply(split(as.data.frame(X),((seq_len(nrow(X))-1)%/%10)+1),function(x) apply(x,2,mean))) #or ?res2<-t(sapply(split(as.data.frame(X),((seq_len(nrow(X))-1)%/%10)+1),colMeans)) ?identical(res1,res2) #[1] TRUE #orlibrary(itertools) it<-ihasNext(isplitRows(X,chunkSize=10))

I write about R every weekday at the Revolutions blog: http://blog.revolution-computing.com In case you missed them, here are some articles from last month of particular interest to R users. http://bit.ly/11YkB0 listed seven reasons of an anthropology professor for using R. http://bit.ly/9sbno linked to an intro of the ply package for performing SQL-like "group by" operations on data

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

REvolution Computing has just released three new packages for R to CRAN (under the open-source Apache 2.0 license): foreach, iterators, and doMC. Together, they provide a simple, scalable parallel computing framework for R that lets you take advantage of your multicore or multiprocessor workstation to program loops that run faster than traditional loops in R. The three packages build on each

REvolution Computing has just released three new packages for R to CRAN (under the open-source Apache 2.0 license): foreach, iterators, and doMC. Together, they provide a simple, scalable parallel computing framework for R that lets you take advantage of your multicore or multiprocessor workstation to program loops that run faster than traditional loops in R. The three packages build on each

Dear R-devel, REvolution appear to be offering ParallelR only when bundled with their R Enterprise edition. As such it appears to be non-free and closed source. http://www.revolution-computing.com/products/parallel-r.php Since R is GPL and not LGPL, is this a breach of the GPL ? Below is the "GPL and ParallelR" thread from their R forum. mdowle > It appears that ParallelR

I know this is not a revolution support forum, but as anyone noticed the following? I have a foreach loop to generate random samples. If I run the exact code below in normal r (2.14.1) it works as expected, but if I run it from revolution 4.2.0 each loop returns the same numbers. The only way I can get revolution to give different numbers is using 1 instead of 8 in registerDoSNOW(makeCluster(8,

I write about R every weekday at the Revolutions blog: http://blog.revolution-computing.com . In case you missed them, here are some articles from last month of particular interest to R users. http://bit.ly/un680 demonstrated reader Paul Bleicher's code for visualizing a time series as a heat-map calendar. http://bit.ly/5fgis0 and http://bit.ly/mepBH showed (with thanks to Drew Conway) how

I write about R every weekday at the Revolutions blog: http://blog.revolution-computing.com and every month I post a summary of articles from the previous month of particular interest to readers of r-help. (Sorry this week's roundup is a little later than usual -- I've was preempted by last week's release of REvolution R Community 3.2, and a webinar I'm giving tomorrow on parallel

I write about R every weekday at the Revolutions blog: http://blog.revolutionanalytics.com and every month I post a summary of articles from the previous month of particular interest to readers of r-help. In case you missed them, here are some articles related to R from the month of May: A review of "R Cookbook", a new how-to book for R programmers: http://bit.ly/j4e9Lg A detailed

Hi list, I was testing out the "doSMP" package from REvolutionR in my regular R2.11.0 installation and I got the following error message.? Well, one obvious thing is that R2.11.0 was built using "i386-pc-mingw32" which is different from what revoIPC used.? I could just use REvolutionR, but all my R peripherals were set up to work with the regular R2.11.0.? So, I really want

I write about R every weekday at the Revolutions blog: http://blog.revolutionanalytics.com and every month I post a summary of articles from the previous month of particular interest to readers of r-help. In case you missed them, here are some articles related to R from the month of June: Highlights of presentations from the R/Finance 2011 conference: http://bit.ly/pgOVfm Trulia uses R and

Does anyone know any more than is in the following press release about REvolution Computing and their commercialization of R? http://www.intel.com/capital/news/releases/080122.htm "Intel Capital, the global investment arm of Intel Corporation, today announced that it has invested in the Series A financing of REvolution Computing, creator of parallel computing software for computational

I write about R every weekday at the Revolutions blog: http://blog.revolutionanalytics.com and every month I post a summary of articles from the previous month of particular interest to readers of r-help. In case you missed them, here are some articles related to R from the month of August: RStan is a new package for Bayesian modeling with R. It's faster and can fit more highly-correlated

I write about R every weekday at http://blog.revolution-computing.com . In case you missed them, here are some articles from last month of particular interest to R users. http://bit.ly/tygLz announced the release of the "foreach" and "iterators" packages on CRAN, for simple scalable parallel programming in R. http://bit.ly/FDS67 linked to Thomas Levin's Joy-Division-esque

I write about R every weekday at http://blog.revolution-computing.com . In case you missed them, here are some articles from the past month that may be of particular interest to r-help: * http://tinyurl.com/cz3jmk REvolution Computing is hiring! We are looking for enthusiastic and talented people with a strong academic and industry track record in statistical computing for technical sales,

I write about R every weekday at the Revolutions blog: http://blog.revolutionanalytics.com and every month I post a summary of articles from the previous month of particular interest to readers of r-help. In case you missed them, here are some articles related to R from the month of October: The creator of the ggplot2 package, Hadley Wickham, shares details on some forthcoming big-data graphics

I write about R every weekday at the Revolutions blog: http://blog.revolutionanalytics.com and every month I post a summary of articles from the previous month of particular interest to readers of r-help. In case you missed them, here are some articles related to R from the month of November: R 2.14 now includes SVG support for Windows: http://bit.ly/vKQ0UC The submissions for the Applications

I write about R every weekday at the Revolutions blog: http://blog.revolutionanalytics.com and every month I post a summary of articles from the previous month of particular interest to readers of r-help. In case you missed them, here are some articles related to R from the month of September: http://bit.ly/cuFNat presented a profile of Hadley Wickham, author of many popular R packages