Here is what I want to do: I have a time series data frame with let us say 100 time-series of length 600 - each in one column of the data frame. I want to pick up 4 of the time-series randomly and then assign them random weights that sum up to one (ie 0.1, 0.5, 0.3, 0.1). Using those I want to compute the mean of the sum of the 4 weighted time series variables (e.g. convex combination). I want to do this let us say 100k times and store each result in the form ts1.name, ts2.name, ts3.name, ts4.name, weight1, weight2, weight3, weight4, mean so that I get a 9*100k df. I tried some things already but R is very bad with loops and I know vector oriented solutions are better because of R design. Thanks Here is what I did and I know it is horrible The df is in the form v1,v2,v2.....v100 1,5,6,.......9 2,4,6,.......10 3,5,8,.......6 2,2,8,.......2 etc e=NULL for (x in 1:100000) { s=sample(1:100,4)#pick 4 variables randomly a=sample(seq(0,1,0.01),1) b=sample(seq(0,1-a,0.01),1) c=sample(seq(0,(1-a-b),0.01),1) d=1-a-b-c e=c(a,b,c,d)#4 random weights average=mean(timeseries.df[,s]%*%t(e)) e=rbind(e,s,average)#in the end i get the 9*100k df } The procedure runs way to slow. EDIT: Thanks for the help i had,i am not used to think R and i am not very used to translate every problem into a matrix algebra equation which is what you need in R. Then the problem becomes a little bit complex if i want to calculate the standard deviation. i need the covariance matrix and i am not sure i can if/how i can pick random elements for each sample from the original timeseries.df covariance matrix then compute the sample variance (t(sampleweights)%*%sample_cov.mat%*%sampleweights) to get in the end the ts.weighted_standard_dev matrix Last question what is the best way to proceed if i want to bootstrap the original df x times and then apply the same computations to test the robustness of my datas thanks