# 11_investigate-variants.R
#
# content: (1) Read data
#          (2) Extract characteristics for cases
#          (3) Select features for navigation behavior
#          (4) Export data frames
#
# input:  results/haum/event_logfiles_2024-02-21_16-07-33.csv
# output: results/haum/eventlogs_pre-corona_case-clusters.csv
#
# last mod: 2024-03-08

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

#--------------- (1) Read data ---------------

load("results/haum/eventlogs_pre-corona_cleaned.RData")

#--------------- (4) Investigate variants ---------------

res$start <- res$date.start
res$complete <- res$date.stop

alog <- activitylog(res,
                    case_id     = "case",
                    activity_id = "item",
                    resource_id = "path",
                    timestamps  = c("start", "complete"))

trace_explorer(alog, n_traces = 25)
# --> sequences of artworks are just too rare

tr <- traces(alog)
trace_length <- pbapply::pbsapply(strsplit(tr$trace, ","), length)
tr[trace_length > 10, ]

trace_varied <- pbapply::pbsapply(strsplit(tr$trace, ","), function(x) length(unique(x)))
tr[trace_varied > 1, ]
table(tr[trace_varied > 2, "absolute_frequency"])
table(tr[trace_varied > 3, "absolute_frequency"])

summary(tr$absolute_frequency)
vioplot::vioplot(tr$absolute_frequency)

# Power law for frequencies of traces
tab <- table(tr$absolute_frequency)
x <- as.numeric(tab)
y <- as.numeric(names(tab))

plot(x, y, log = "xy")
p1 <- lm(log(y) ~ log(x))
pre <- exp(coef(p1)[1]) * x^coef(p1)[2]
lines(x, pre)


# Look at individual traces as examples
tr[trace_varied == 5 & trace_length > 50, ]
# --> every variant exists only once, of course
datcase[datcase$nitems == 5 & datcase$length > 50,]

pbapply::pbsapply(datcase[, -c(1, 9)], median)

#ex <- datcase[datcase$nitems == 4 & datcase$length == 15,]
ex <- datcase[datcase$nitems == 5,]
ex <- ex[sample(1:nrow(ex), 20), ]
# --> pretty randomly chosen... TODO:

case_ids <- NULL

for (case in ex$case) {
  if ("080" %in% res$item[res$case == case] | "503" %in% res$item[res$case == case]) {
    case_ids <- c(case_ids, TRUE)
  } else {
    case_ids <- c(case_ids, FALSE)
  }
}

cases <- ex$case[case_ids]


for (case in cases) {

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

  dfg <- process_map(alog,
                     type_nodes = frequency("absolute", color_scale = "Greys"),
                     type_edges = frequency("absolute", color_edges = "#FF6900"),
                     rankdir    = "LR",
                     render     = FALSE)
  export_map(dfg,
             file_name = paste0("results/processmaps/dfg_example_cases_", case, "_R.pdf"),
             file_type = "pdf",
             title     = paste("Case", case))


}