31 lines
1.5 KiB
R
31 lines
1.5 KiB
R
source("packrat/init.R")
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library(dplyr)
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library(entropy)
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library(robustbase)
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calls <- read.csv(snakemake@input[[1]])
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day_segment <- snakemake@params[["day_segment"]]
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metric <- snakemake@params[["metric"]]
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type <- snakemake@params[["call_type"]]
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output_file <- snakemake@output[[1]]
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metrics <- calls %>% filter(call_type == ifelse(type == "incoming", "1", ifelse(type == "outgoing", "2", "3")))
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if(day_segment == "daily"){
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metrics <- metrics %>% group_by(local_date)
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} else {
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metrics <- metrics %>% filter(day_segment == local_day_segment) %>% group_by(local_date)
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}
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metrics <- switch(metric,
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"count" = metrics %>% summarise(!!paste("com", "call", type, day_segment, metric, sep = "_") := n()),
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"distinctcontacts" = metrics %>% summarise(!!paste("com", "call", type, day_segment, metric, sep = "_") := n_distinct(trace)),
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"meanduration" = metrics %>% summarise(!!paste("com", "call", type, day_segment, metric, sep = "_") := mean(call_duration)),
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"sumduration" = metrics %>% summarise(!!paste("com", "call", type, day_segment, metric, sep = "_") := sum(call_duration)),
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"hubermduration" = metrics %>% summarise(!!paste("com", "call", type, day_segment, metric, sep = "_") := huberM(call_duration)$mu),
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"varqnduration" = metrics %>% summarise(!!paste("com", "call", type, day_segment, metric, sep = "_") := Qn(call_duration)),
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"entropyduration" = metrics %>% summarise(!!paste("com", "call", type, day_segment, metric, sep = "_") := entropy.MillerMadow(call_duration)))
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write.csv(na.omit(metrics), output_file, row.names = F)
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