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Updated DFGs for case clusters; exported data for tables

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This commit is contained in:
Nora Wickelmaier 2024-03-27 10:07:36 +01:00
parent 48a4bcbcdb
commit 42cb777d36
1 changed files with 20 additions and 16 deletions
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36
code/12_dfgs-case-clusters.R
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@ -23,7 +23,7 @@ dat <- res
dat$start <- as.POSIXct(dat$date.start) dat$start <- as.POSIXct(dat$date.start)
dat$complete <- as.POSIXct(dat$date.stop) dat$complete <- as.POSIXct(dat$date.stop)
alog <- bupaR::activitylog(dat[dat$cluster == cluster, ], alog <- bupaR::activitylog(dat[dat$cluster == 4, ],
case_id = "case", case_id = "case",
activity_id = "item", activity_id = "item",
resource_id = "path", resource_id = "path",
@ -36,7 +36,7 @@ tab <- table(tr$absolute_frequency)
tab[1] / nrow(tr) tab[1] / nrow(tr)
alog |> edeaR::filter_infrequent_flows(min_n = 20) |> processmapR::process_map() alog |> edeaR::filter_infrequent_flows(min_n = 5) |> processmapR::process_map()
#--------------- (2) Export DFGs for clusters --------------- #--------------- (2) Export DFGs for clusters ---------------
@ -44,6 +44,7 @@ mycols <- c("#3CB4DC", "#FF6900", "#78004B", "#91C86E", "#434F4F")
cl_names <- c("Scanning", "Exploring", "Flitting", "Searching", "Info") cl_names <- c("Scanning", "Exploring", "Flitting", "Searching", "Info")
ns <- c(30, 20, 10, 5, 30) ns <- c(30, 20, 10, 5, 30)
#ns <- c(20, 20, 20, 5, 20)
for (i in 1:5) { for (i in 1:5) {
@ -52,7 +53,7 @@ for (i in 1:5) {
activity_id = "item", activity_id = "item",
resource_id = "path", resource_id = "path",
timestamps = c("start", "complete")) timestamps = c("start", "complete"))
dfg <- processmapR::process_map(edeaR::filter_infrequent_flows(alog, min_n = ns[i]), dfg <- processmapR::process_map(edeaR::filter_infrequent_flows(alog, min_n = ns[i]),
type_nodes = processmapR::frequency("relative", color_scale = "Greys"), type_nodes = processmapR::frequency("relative", color_scale = "Greys"),
sec_nodes = processmapR::frequency("absolute"), sec_nodes = processmapR::frequency("absolute"),
@ -67,27 +68,30 @@ for (i in 1:5) {
title = cl_names[i]) title = cl_names[i])
} }
# cluster 1: 50 # What data is used and how many traces are unique
# cluster 2: 30 o. 20
# cluster 3: 20 - 30
# cluster 4: 5
# cluster 5: 20
get_percent_variants <- function(log, cluster, min_n) { perc_filter <- numeric(5)
perc_unqiue <- numeric(5)
alog <- bupaR::activitylog(log[log$cluster == cluster, ], for (i in 1:5) {
alog <- bupaR::activitylog(dat[dat$cluster == i, ],
case_id = "case", case_id = "case",
activity_id = "item", activity_id = "item",
resource_id = "path", resource_id = "path",
timestamps = c("start", "complete")) timestamps = c("start", "complete"))
nrow(edeaR::filter_infrequent_flows(alog, min_n = min_n)) / perc_filter[i] <- nrow(edeaR::filter_infrequent_flows(alog, min_n = ns[i])) /
nrow(alog) nrow(alog)
cs <- bupaR::cases(alog)
infreq_tr <- names(which(table(cs$trace) == 1))
infreq_cs <- cs$case[cs$trace %in% infreq_tr]
perc_unqiue[i] <- nrow(alog[alog$case %in% infreq_cs, ]) / nrow(alog)
} }
perc <- numeric(5) save(ns, perc_filter, perc_unqiue,
file = "../../thesis/figures/data/dfgs_case-cluster.RData")
for (i in 1:5) {
perc[i] <- get_percent_variants(log = dat, cluster = i, min_n = ns[i])
}