source("packrat/init.R") library(dplyr) library(entropy) library(robustbase) filter_by_day_segment <- function(data, day_segment) { if(day_segment %in% c("morning", "afternoon", "evening", "night")) data <- data %>% filter(local_day_segment == day_segment) return(data %>% group_by(local_date)) } Mode <- function(v) { uniqv <- unique(v) uniqv[which.max(tabulate(match(v, uniqv)))] } compute_call_feature <- function(calls, requested_feature, day_segment){ if(requested_feature == "countmostfrequentcontact"){ # Get the most frequent contact calls <- calls %>% group_by(trace) %>% mutate(N=n()) %>% ungroup() %>% filter(N == max(N)) return(calls %>% filter_by_day_segment(day_segment) %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := n())) } else { calls <- calls %>% filter_by_day_segment(day_segment) feature <- switch(requested_feature, "count" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := n()), "distinctcontacts" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := n_distinct(trace)), "meanduration" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := mean(call_duration)), "sumduration" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := sum(call_duration)), "minduration" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := min(call_duration)), "maxduration" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := max(call_duration)), "stdduration" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := sd(call_duration)), "modeduration" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := Mode(call_duration)), "hubermduration" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := huberM(call_duration)$mu), "varqnduration" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := Qn(call_duration)), "entropyduration" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := entropy.MillerMadow(call_duration)), "timefirstcall" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := first(local_hour) + (first(local_minute)/60)), "timelastcall" = calls %>% summarise(!!paste("call", type, day_segment, requested_feature, sep = "_") := last(local_hour) + (last(local_minute)/60))) return(feature) } } calls <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE) day_segment <- snakemake@params[["day_segment"]] requested_features <- snakemake@params[["features"]] type <- snakemake@params[["call_type"]] features = data.frame(local_date = character(), stringsAsFactors = FALSE) calls <- calls %>% filter(call_type == ifelse(type == "incoming", "1", ifelse(type == "outgoing", "2", "3"))) for(requested_feature in requested_features){ feature <- compute_call_feature(calls, requested_feature, day_segment) features <- merge(features, feature, by="local_date", all = TRUE) } if("countmostfrequentcontact" %in% requested_features) features <- features %>% mutate_at(vars(contains('countmostfrequentcontact')), funs(ifelse(is.na(.), 0, .))) write.csv(features, snakemake@output[[1]], row.names = FALSE)