I have *not* followed this in any detail, but this line seems wrong: arvaia_catture_order <- arvaia_catture[order(arvaia_catture$tempo)]
Perhaps it should be: arvaia_catture_order <- arvaia_catture[order(arvaia_catture$tempo), ] ## note the comma! If I am mistaken, just ignore and move on. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Jun 9, 2021 at 2:41 PM Rui Barradas <ruipbarra...@sapo.pt> wrote: > Hello, > > I'm not getting your simple sum: > > > arvaia_catture_order[, week := as.integer(format(tempo, "%U"))] > aggregate(catture ~ week, arvaia_catture_order, sum) > # week catture > #1 19 15 > #2 20 5 > #3 21 78 > #4 22 120 > > > Can you explain your result better? > > Hope this helps, > > Rui Barradas > > Às 18:57 de 09/06/21, Enrico Gabrielli escreveu: > > Hello > > I just registered on the list. > > I am an agricultural technician and I am collaborating on a research > > project on agroforestry and Brown Marmorated Stink Bug (Halyomorpha > > halys, abbreviated BMSB). > > > > Through kobotoolbox we are collecting data of catches in traps on > > farms. Farms register inconsistently. > > I am trying to use packages for irregular time series. > > > > Here an exemple > > the data: > > # variable monitoring time as register an import from kobotoolbox > > tempo <- as.POSIXct(c("2021-05-29 17:00:00 UTC","2021-06-05 10:52:00 > > UTC","2021-06-01 17:00:00 UTC","2021-05-16 08:34:00 UTC","2021-06-05 > > 17:00:00 UTC","2021-05-29 05:30:00 UTC","2021-05-23 06:30:00 > > UTC","2021-05-20 13:00:00 UTC","2021-05-15 12:09:00 UTC")) > > # variable capture of BMSB > > catture <- c(25,92,23,2,5,30,23,3,15) > > # resulting table > > library(data.table) > > arvaia_catture <- data.table(tempo,catture) > > # order by time > > arvaia_catture_order <- arvaia_catture[order(arvaia_catture$tempo)] > > > > the catches refer to an interval, which goes from the previous > > monitoring up to the one recorded on the date > > our aim is to calculate the weekly catch > > also when a farmer, for example, enters the data on Thursday and > > Tuesday > > of the following week, on Tuesday in the trap he will find individuals > > who were also captured on Friday and Saturday, which formally are to be > > considered in the previous week. > > > > With the data of the example > > the results (with a spreadsheet) is > > two possibile solution > > week simple SUM week right SUM week > > 19 17 19,44027778 > > 20 26 52,91388889 > > 21 55 46,4375integrand_2 <- function(x) {0.62 * (1/x)} > > integrate(integrand, lower = , upper = 5) > > > 22 120 99,20833333 > > The right SUM week is right! > > > > I have made several attempts > > with lubridate, padr, xts > > but the last one seems interesting to me > > with DTSg > > doing > > x_periodic <- alter(x,na.status = 'explicit',from="2021-05-15 > > 12:00:00",by="min") > > colapply(x_periodic, fun = interpolateLinear) > > I managed to create a vector with all interpolated hours > > but with DTSg I still can't aggregate by week > > > > Has anyone on the list ever faced such a problem? > > > > Thank you > > > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.