# 13_dfgs-case-clusters.R
#
# content: 
#
# input:  results/haum/tmp_user-navigation.RData
# output: 
#
# last mod: 2024-03-19

# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")

load("results/haum/tmp_user-navigation.RData")

#dat <- read.table("results/haum/eventlogs_2019_case-clusters.csv", header = TRUE, sep = ";")

dat <- res

dat$start <- as.POSIXct(dat$date.start)
dat$complete <- as.POSIXct(dat$date.stop)



alog <- bupaR::activitylog(dat[dat$cluster == cluster, ],
                            case_id     = "case",
                            activity_id = "item",
                            resource_id = "path",
                            timestamps  = c("start", "complete"))

processmapR::trace_explorer(alog, n_traces = 25)

tr <- bupaR::traces(alog)
tab <- table(tr$absolute_frequency)

tab[1] / nrow(tr)


alog |> edeaR::filter_infrequent_flows(min_n = 20) |> processmapR::process_map()




## Export DFGs for clusters

mycols <- c("#3CB4DC", "#FF6900", "#78004B", "#91C86E", "#434F4F")
cl_names <- c("Scanning", "Exploring", "Flitting", "Searching", "Info")

ns <- c(30, 20, 10, 5, 30)

for (i in 1:5) {

  alog <- bupaR::activitylog(dat[dat$cluster == i, ],
                             case_id     = "case",
                             activity_id = "item",
                             resource_id = "path",
                             timestamps  = c("start", "complete"))
  
  dfg <- processmapR::process_map(edeaR::filter_infrequent_flows(alog, min_n = ns[i]),
    type_nodes = processmapR::frequency("relative", color_scale = "Greys"),
    sec_nodes  = processmapR::frequency("absolute"),
    type_edges = processmapR::frequency("relative", color_edges = mycols[i]),
    sec_edges  = processmapR::frequency("absolute"),
    rankdir    = "LR",
    render     = FALSE)

  processmapR::export_map(dfg,
    file_name = paste0("results/processmaps/dfg_cases_cluster", i, "_R.pdf"),
    file_type = "pdf",
    title     = cl_names[i])
}


# cluster 1: 50
# cluster 2: 30 o. 20
# cluster 3: 20 - 30
# cluster 4: 5
# cluster 5: 20

get_percent_variants <- function(log, cluster, min_n) {

  alog <- bupaR::activitylog(log[log$cluster == cluster, ],
                             case_id     = "case",
                             activity_id = "item",
                             resource_id = "path",
                             timestamps  = c("start", "complete"))

  nrow(edeaR::filter_infrequent_flows(alog, min_n = min_n)) /
    nrow(alog)
}


perc <- numeric(5)

for (i in 1:5) {
  perc[i] <- get_percent_variants(log = dat, cluster = i, min_n = ns[i])
}