search for: rollapplyr

...,   The code below does exactly what I want in sequential mode. But, it is slow and I want to run it in parallel mode. I examined some windows version packages (parallel, snow, snowfall,..) but could not solve my specific problem. As far as I understood, either I have to write a new function like sfRollapplyr or I have to change my code in a way that it utilizes lapply, or sapply instead of 'rollapplyr' first then use sfInit, sfExport, and sfLapply,.. for parallel computing. I could not perform either so please help me :)   ## nc<-313 rs<-500000 ema<-10 h<-4 gomin1sd<-function (x,...

...lement, not necessarily sum() I am looking for a generic solution for above problem. Any better idea? Thanks, On Fri, Aug 11, 2017 at 12:04 AM, Joshua Ulrich <josh.m.ulrich at gmail.com> wrote: > Use a `width` of integer index locations. And you likely want = > "right" (or rollapplyr(), as I used). > > R> set.seed(21) > R> x <- rnorm(10) > R> rs <- rollapplyr(x, seq_along(x), sum) > R> cs <- cumsum(x) > R> identical(rs, cs) > [1] TRUE > > > On Thu, Aug 10, 2017 at 1:28 PM, Christofer Bogaso > <bogaso.christofer at gmai...

Replace "sum" with your custom function's name. I don't see any reason why that wouldn't work, and the problem with my solution is not clear in your response. r <- rollapplyr(x, seq_along(x), yourCustomFunctionGoesHere) On Thu, Aug 10, 2017 at 1:39 PM, Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > Hi Joshua, thanks for your prompt reply. However as I said, sum() > function I used here just for demonstrating the problem, I have other > custo...

Use a `width` of integer index locations. And you likely want = "right" (or rollapplyr(), as I used). R> set.seed(21) R> x <- rnorm(10) R> rs <- rollapplyr(x, seq_along(x), sum) R> cs <- cumsum(x) R> identical(rs, cs) [1] TRUE On Thu, Aug 10, 2017 at 1:28 PM, Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > Hi again, > > I am wonde...

Hi again, I am wondering there is any function for 'zoo' time series, where I can apply a user defined function rolling window basis, wherein window size is ever increasing i.e. not fixed. For example, let say I have below user defined function and a zoo time series : > library(zoo) > UDF = function(x) sum(x) > TS = zoo(rnorm(10), seq(as.Date('2017-01-01'),

Dear all, I have weekly data by city (variable citycode). I would like to take the average of the previous two, three, four weeks (without the current week) of the variable called value. This is what I have tried to compute the average of the two previous weeks; df = df %>% mutate(value.lag1 = lag(value, n = 1)) %>% mutate(value .2.previous = rollapply(data = value.lag1,

Hi, I have an xts starting with a number of columns (currency pairs see below), then I add new ones which are derived from existing ones (like adding the moving average of a column) by merging the new columns one by one. These get the name of the column they are calculated from concatenated with ".1". All done by merge.xts, easy. Now, I have a function (procState below) which generates

I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",