Hello, Using base R only, the following seems to do what you want.
with(mydf, ave(speed, date_time, type, FUN = weighted.mean, w = n_vehicles)) Hope this helps, Rui Barradas Em 09-11-2017 13:16, Massimo Bressan escreveu:
Hello an update about my question: I worked out the following solution (with the package "dplyr") library(dplyr) mydf%>% mutate(speed_vehicles=n_vehicles*mydf$speed) %>% group_by(date_time,type) %>% summarise( sum_n_times_speed=sum(speed_vehicles), n_vehicles=sum(n_vehicles), vel=sum(speed_vehicles)/sum(n_vehicles) ) In fact I was hoping to manage everything in a "one-go": i.e. without the need to create the "intermediate" variable called "speed_vehicles" and with the use of the function weighted.mean() any hints for a different approach much appreciated thanks Da: "Massimo Bressan" <massimo.bres...@arpa.veneto.it> A: "r-help" <r-help@r-project.org> Inviato: Giovedì, 9 novembre 2017 12:20:52 Oggetto: weighted average grouped by variables hi all I have this dataframe (created as a reproducible example) mydf<-structure(list(date_time = structure(c(1508238000, 1508238000, 1508238000, 1508238000, 1508238000, 1508238000, 1508238000), class = c("POSIXct", "POSIXt"), tzone = ""), direction = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"), type = structure(c(1L, 2L, 3L, 4L, 1L, 2L, 3L), .Label = c("car", "light_duty", "heavy_duty", "motorcycle"), class = "factor"), avg_speed = c(41.1029082774049, 40.3333333333333, 40.3157894736842, 36.0869565217391, 33.4065155807365, 37.6222222222222, 35.5), n_vehicles = c(447L, 24L, 19L, 23L, 706L, 45L, 26L)), .Names = c("date_time", "direction", "type", "speed", "n_vehicles"), row.names = c(NA, -7L), class = "data.frame") mydf and I need to get to this final result mydf_final<-structure(list(date_time = structure(c(1508238000, 1508238000, 1508238000, 1508238000), class = c("POSIXct", "POSIXt"), tzone = ""), type = structure(c(1L, 2L, 3L, 4L), .Label = c("car", "light_duty", "heavy_duty", "motorcycle"), class = "factor"), weighted_avg_speed = c(36.39029, 38.56521, 37.53333, 36.08696), n_vehicles = c(1153L,69L,45L,23L)), .Names = c("date_time", "type", "weighted_avg_speed", "n_vehicles"), row.names = c(NA, -4L), class = "data.frame") mydf_final my question: how to compute a weighted mean i.e. "weighted_avg_speed" from "speed" (the values whose weighted mean is to be computed) and "n_vehicles" (the weights) grouped by "date_time" and "type"? to be noted the complication of the case "motorcycle" (not present in both directions) any help for that? thank you max
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