Updated DFGs for case clusters; exported data for tables
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@ -23,7 +23,7 @@ dat <- res
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dat$start <- as.POSIXct(dat$date.start)
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dat$complete <- as.POSIXct(dat$date.stop)
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alog <- bupaR::activitylog(dat[dat$cluster == cluster, ],
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alog <- bupaR::activitylog(dat[dat$cluster == 4, ],
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case_id = "case",
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activity_id = "item",
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resource_id = "path",
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@ -36,7 +36,7 @@ tab <- table(tr$absolute_frequency)
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tab[1] / nrow(tr)
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alog |> edeaR::filter_infrequent_flows(min_n = 20) |> processmapR::process_map()
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alog |> edeaR::filter_infrequent_flows(min_n = 5) |> processmapR::process_map()
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#--------------- (2) Export DFGs for clusters ---------------
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@ -44,6 +44,7 @@ mycols <- c("#3CB4DC", "#FF6900", "#78004B", "#91C86E", "#434F4F")
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cl_names <- c("Scanning", "Exploring", "Flitting", "Searching", "Info")
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ns <- c(30, 20, 10, 5, 30)
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#ns <- c(20, 20, 20, 5, 20)
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for (i in 1:5) {
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@ -67,27 +68,30 @@ for (i in 1:5) {
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title = cl_names[i])
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}
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# cluster 1: 50
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# cluster 2: 30 o. 20
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# cluster 3: 20 - 30
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# cluster 4: 5
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# cluster 5: 20
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# What data is used and how many traces are unique
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get_percent_variants <- function(log, cluster, min_n) {
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perc_filter <- numeric(5)
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perc_unqiue <- numeric(5)
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alog <- bupaR::activitylog(log[log$cluster == cluster, ],
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for (i in 1:5) {
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alog <- bupaR::activitylog(dat[dat$cluster == i, ],
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case_id = "case",
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activity_id = "item",
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resource_id = "path",
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timestamps = c("start", "complete"))
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nrow(edeaR::filter_infrequent_flows(alog, min_n = min_n)) /
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perc_filter[i] <- nrow(edeaR::filter_infrequent_flows(alog, min_n = ns[i])) /
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nrow(alog)
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cs <- bupaR::cases(alog)
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infreq_tr <- names(which(table(cs$trace) == 1))
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infreq_cs <- cs$case[cs$trace %in% infreq_tr]
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perc_unqiue[i] <- nrow(alog[alog$case %in% infreq_cs, ]) / nrow(alog)
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}
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perc <- numeric(5)
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for (i in 1:5) {
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perc[i] <- get_percent_variants(log = dat, cluster = i, min_n = ns[i])
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}
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save(ns, perc_filter, perc_unqiue,
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file = "../../thesis/figures/data/dfgs_case-cluster.RData")
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