152 lines
4.9 KiB
R
152 lines
4.9 KiB
R
# 12_dfgs-case-clusters.R
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#
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# content: (1) Read data
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# (2) Export DFGs for clusters
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#
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# input: results/user-navigation.RData
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# output: ../../thesis/figures/dfg_cases_cluster1_R.pdf
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# ../../thesis/figures/dfg_cases_cluster2_R.pdf
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# ../../thesis/figures/dfg_cases_cluster3_R.pdf
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# ../../thesis/figures/dfg_cases_cluster4_R.pdf
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# ../../thesis/figures/dfg_cases_cluster5_R.pdf
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#
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# last mod: 2024-03-30
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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#--------------- (1) Read data ---------------
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load("results/user-navigation.RData")
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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$cluster == 4, ],
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case_id = "case",
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activity_id = "item",
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resource_id = "path",
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timestamps = c("start", "complete"))
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processmapR::trace_explorer(alog, n_traces = 25)
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tr <- bupaR::traces(alog)
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tab <- table(tr$absolute_frequency)
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tab[1] / nrow(tr)
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alog |> edeaR::filter_infrequent_flows(min_n = 5) |> processmapR::process_map()
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#--------------- (2) 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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#ns <- c(20, 20, 20, 5, 20)
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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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if (i %in% c(4, 5)) {
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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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render = FALSE)
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} else {
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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 = "TB",
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render = FALSE)
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}
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processmapR::export_map(dfg,
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file_name = paste0("../../thesis/figures/dfg_cases_cluster_", cl_names[i], "_R.pdf"),
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file_type = "pdf")
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}
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## Black and white
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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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if (i %in% c(4, 5)) {
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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 = "black"),
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sec_edges = processmapR::frequency("absolute"),
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render = FALSE)
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} else {
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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 = "black"),
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sec_edges = processmapR::frequency("absolute"),
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rankdir = "TB",
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render = FALSE)
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}
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processmapR::export_map(dfg,
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file_name = paste0("../../thesis/figures/dfg_cases_cluster_", cl_names[i], "_R_bw.pdf"),
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file_type = "pdf")
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}
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# What data is used and how many traces are unique
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tr_unique <- numeric(5)
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perc_filter <- numeric(5)
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n_cases <- numeric(5)
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for (i in 1:5) {
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alog <- bupaR::activitylog(dat[dat$cluster == i, ],
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case_id = "case",
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activity_id = "item",
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resource_id = "path",
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timestamps = c("start", "complete"))
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cs <- bupaR::cases(alog)
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cs_filtered <- edeaR::filter_infrequent_flows(alog, min_n = ns[i]) |>
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bupaR::cases()
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n_cases[i] <- nrow(cs_filtered)
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perc_filter[i] <- n_cases[i] / nrow(cs)
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tr <- bupaR::traces(alog)
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sum_tr <- sum(tr$absolute_frequency == 1)
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tr_unique[i] <- sum_tr / nrow(tr)
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#infreq_tr <- names(which(table(cs$trace) == 1))
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#infreq_cs <- cs$case[cs$trace %in% infreq_tr]
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#cs_filtered[i] <- length(infreq_cs) / length(cs$case)
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}
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save(ns, n_cases, tr_unique, perc_filter,
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file = "../../thesis/figures/data/dfgs_case-cluster.RData")
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