Mgiyuk

Julia loops are as slow as R loops The code below in Julia and R is to show that the estimator of the population variance is a biased estimator, that is it depends on the sample size and no matter how many times we average over different observations, for small number of data points it is not equal to the variance of the population. It takes for Julia ~10 seconds to finish the two loops and R does it in ~7 seconds. If I leave the code inside the loops commented then the loops in R and Julia take the same time and if I only sum the iterators by s = s + i+ j Julia finishes in ~0.15s and R in ~0.5s. s = s + i+ j Is it that Julia loops are slow or R became fast? How can I improve the speed of the code below for Julia? Can the R code become faster? Julia: using Plots trials = 100000 sample_size = 10; sd = Array{Float64}(trials,sample_size-1) tic() for i = 2:sample_size for j = 1:trials res = randn(i) sd[j,i-1] = (1/(i))*(sum(res.^2))-(1/((i)*i))*(sum(res)*sum(res)) ...

Sorting a vector of vectors by multiple elements in Julia I've had good read of the 'Sorting Functions' section of the Julia manual, and had a look at some of the similar questions that have already been asked on this board, but I don't think I've quite found the answer to my question. Apologies if I've missed something. Essentially I have a vector of vectors, with the enclosed vectors containing integers. For the purposes of the example, each enclosed vector contains 3 integers, but it could be any number. I want to sort the enclosed vectors by the first element, then by the second element, then by the third element etc. Let's start with the vector: v = [[3, 6, 1], [2, 2, 6], [1, 5, 9], [2, 1, 8], [3, 7, 9], [1, 1, 2], [2, 2, 2], [3, 6, 2], [1, 2, 5], [1, 5, 6], [3, 7, 4], [2, 1, 4], [2, 2, 1], [3, 1, 2], [1, 2, 8]] And continue with what I'm actually looking for: v = [[1, 1, 2], [1, 2, 5], [1, 2, 8], [1, 5, 6], [1, 5, 9], [2, 1, 4], [2, 1, ...