48 lines
1.8 KiB
R
48 lines
1.8 KiB
R
source("packrat/init.R")
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library(dplyr)
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filter_by_day_segment <- function(data, day_segment) {
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if(day_segment %in% c("morning", "afternoon", "evening", "night"))
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data <- data %>% filter(local_day_segment == day_segment)
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return(data %>% group_by(local_date))
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}
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compute_sms_feature <- function(sms, metric, day_segment){
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if(metric == "countmostfrequentcontact"){
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# Get the most frequent contact
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sms <- sms %>% group_by(trace) %>%
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mutate(N=n()) %>%
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ungroup() %>%
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filter(N == max(N))
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return(sms %>%
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filter_by_day_segment(day_segment) %>%
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summarise(!!paste("sms", sms_type, day_segment, metric, sep = "_") := n()))
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} else {
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sms <- sms %>% filter_by_day_segment(day_segment)
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feature <- switch(metric,
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"count" = sms %>% summarise(!!paste("sms", sms_type, day_segment, metric, sep = "_") := n()),
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"distinctcontacts" = sms %>% summarise(!!paste("sms", sms_type, day_segment, metric, sep = "_") := n_distinct(trace)),
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"timefirstsms" = sms %>% summarise(!!paste("sms", sms_type, day_segment, metric, sep = "_") := first(local_hour) + (first(local_minute)/60)),
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"timelastsms" = sms %>% summarise(!!paste("sms", sms_type, day_segment, metric, sep = "_") := last(local_hour) + (last(local_minute)/60)))
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return(feature)
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}
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}
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sms <- read.csv(snakemake@input[[1]])
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day_segment <- snakemake@params[["day_segment"]]
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metrics <- snakemake@params[["metrics"]]
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sms_type <- snakemake@params[["sms_type"]]
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features = data.frame(local_date = character(), stringsAsFactors = FALSE)
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sms <- sms %>% filter(message_type == ifelse(sms_type == "received", "1", "2"))
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for(metric in metrics){
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feature <- compute_sms_feature(sms, metric, day_segment)
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features <- merge(features, feature, by="local_date", all = TRUE)
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}
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write.csv(features, snakemake@output[[1]], row.names = FALSE)
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