97 lines
2.6 KiB
R
97 lines
2.6 KiB
R
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# 13_dfgs-case-clusters.R
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#
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# content:
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#
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# input: results/haum/tmp_user-navigation.RData
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# output:
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#
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# last mod: 2024-03-19
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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load("results/haum/tmp_user-navigation.RData")
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#dat <- read.table("results/haum/eventlogs_2019_case-clusters.csv", header = TRUE, sep = ";")
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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$cluster == cluster, ],
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case_id = "case",
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activity_id = "item",
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resource_id = "path",
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timestamps = c("start", "complete"))
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processmapR::trace_explorer(alog, n_traces = 25)
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tr <- bupaR::traces(alog)
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tab <- table(tr$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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## Export DFGs for clusters
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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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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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dfg <- processmapR::process_map(edeaR::filter_infrequent_flows(alog, min_n = ns[i]),
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type_nodes = processmapR::frequency("relative", color_scale = "Greys"),
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sec_nodes = processmapR::frequency("absolute"),
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type_edges = processmapR::frequency("relative", color_edges = mycols[i]),
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sec_edges = processmapR::frequency("absolute"),
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rankdir = "LR",
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render = FALSE)
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processmapR::export_map(dfg,
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file_name = paste0("results/processmaps/dfg_cases_cluster", i, "_R.pdf"),
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file_type = "pdf",
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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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get_percent_variants <- function(log, cluster, min_n) {
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alog <- bupaR::activitylog(log[log$cluster == cluster, ],
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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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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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