# 09_user-navigation.R
#
# content: (1) Read data
#           (1.1) Read log event data
#           (1.2) Extract additional infos for clustering
#          (2) Clustering
#          (3) Fit tree
#          (4) Investigate variants
#
# input:  results/haum/event_logfiles_2024-02-21_16-07-33.csv
# output: results/haum/eventlogs_pre-corona_case-clusters.csv
#
# last mod: 2024-03-06


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

library(bupaverse)
library(factoextra)

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

#--------------- (1.1) Read log event data ---------------

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

# Select one year to handle number of cases
dat <- dat[as.Date(dat$date.start) > "2018-12-31" &
           as.Date(dat$date.start) < "2020-01-01", ]

#--------------- (1.2) Extract additional infos for clustering ---------------

datcase <- aggregate(cbind(distance, scaleSize, rotationDegree) ~
                     case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)

datcase$length <- aggregate(item ~ case, dat, length)$item

eventtab <- aggregate(event ~ case, dat, table)["case"]
eventtab$nmove <- aggregate(event ~ case, dat, table)$event[, "move"]
eventtab$nflipCard <- aggregate(event ~ case, dat, table)$event[, "flipCard"]
eventtab$nopenTopic <- aggregate(event ~ case, dat, table)$event[, "openTopic"]
eventtab$nopenPopup <- aggregate(event ~ case, dat, table)$event[, "openPopup"]

topictab <- aggregate(topic ~ case, dat, table)["case"]
topictab$artist <- aggregate(topic ~ case, dat, table)$topic[, 1]
topictab$details <- aggregate(topic ~ case, dat, table)$topic[, 2]
topictab$extra_info <- aggregate(topic ~ case, dat, table)$topic[, 3]
topictab$komposition <- aggregate(topic ~ case, dat, table)$topic[, 4]
topictab$leben_des_kunstwerks <- aggregate(topic ~ case, dat, table)$topic[, 5]
topictab$licht_und_farbe <- aggregate(topic ~ case, dat, table)$topic[, 6]
topictab$technik <- aggregate(topic ~ case, dat, table)$topic[, 7]
topictab$thema <- aggregate(topic ~ case, dat, table)$topic[, 8]

datcase <- datcase |>
  merge(eventtab, by = "case", all = TRUE) |>
  merge(topictab, by = "case", all = TRUE)

rm(eventtab, topictab)

datcase$ntopiccards <- aggregate(topic ~ case, dat,
                             function(x) ifelse(all(is.na(x)), NA,
                             length(na.omit(x))), na.action =
                             NULL)$topic
datcase$ntopics <- aggregate(topic ~ case, dat,
                             function(x) ifelse(all(is.na(x)), NA,
                             length(unique(na.omit(x)))), na.action =
                             NULL)$topic
datcase$nitems <- aggregate(item ~ case, dat, function(x)
                            length(unique(x)), na.action = NULL)$item
datcase$npaths <- aggregate(path ~ case, dat, function(x)
                            length(unique(x)), na.action = NULL)$path
datcase$vacation <- aggregate(vacation ~ case, dat,
                              function(x) ifelse(all(is.na(x)), 0, 1),
                              na.action = NULL)$vacation
datcase$holiday <- aggregate(holiday ~ case, dat,
                             function(x) ifelse(all(is.na(x)), 0, 1),
                             na.action = NULL)$holiday
datcase$weekend <- aggregate(weekdays ~ case, dat,
                             function(x) ifelse(any(x %in% c("Saturday", "Sunday")), 1, 0),
                             na.action = NULL)$weekdays
datcase$morning <- aggregate(date.start ~ case, dat,
                             function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
                             na.action = NULL)$date.start

dat_split <- split(dat, ~ case)

time_minmax_ms <- function(subdata) {
  subdata$min_time <- min(subdata$timeMs.start)
    if (all(is.na(subdata$timeMs.stop))) {
      subdata$max_time <- NA
    } else {
       subdata$max_time <- max(subdata$timeMs.stop, na.rm = TRUE)
    }
  subdata
}
# TODO: Move to helper file

dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
dat_minmax <- dplyr::bind_rows(dat_list)

datcase$min_time <- aggregate(min_time ~ case, dat_minmax, unique)$min_time
datcase$max_time <- aggregate(max_time ~ case, dat_minmax, unique)$max_time

datcase$duration <- datcase$max_time - datcase$min_time
datcase$min_time <- NULL
datcase$max_time <- NULL


check_infocards <- function(subdata, artworks) {
  infocard_only <- NULL
  if(any(unique(subdata$item) %in% artworks)) {
    infocard_only <- FALSE
  } else {
    infocard_only <- TRUE
  }
  as.numeric(infocard_only)
}
# TODO: Move to helper file

artworks <- unique(dat$item)[!unique(dat$item) %in% c("501", "502", "503")]

datcase$infocardOnly <- pbapply::pbsapply(dat_split, check_infocards, artworks = artworks)


# Clean up NAs
datcase$distance <- ifelse(is.na(datcase$distance), 0, datcase$distance)
datcase$scaleSize <- ifelse(is.na(datcase$scaleSize), 1, datcase$scaleSize)
datcase$rotationDegree <- ifelse(is.na(datcase$rotationDegree), 0, datcase$rotationDegree)
datcase$artist <- ifelse(is.na(datcase$artist), 0, datcase$artist)
datcase$details <- ifelse(is.na(datcase$details), 0, datcase$details)
datcase$extra_info <- ifelse(is.na(datcase$extra_info), 0, datcase$extra_info)
datcase$komposition <- ifelse(is.na(datcase$komposition), 0, datcase$komposition)
datcase$leben_des_kunstwerks <- ifelse(is.na(datcase$leben_des_kunstwerks), 0, datcase$leben_des_kunstwerks)
datcase$licht_und_farbe <- ifelse(is.na(datcase$licht_und_farbe), 0, datcase$licht_und_farbe)
datcase$technik <- ifelse(is.na(datcase$technik), 0, datcase$technik)
datcase$thema <- ifelse(is.na(datcase$thema), 0, datcase$thema)
datcase$ntopics <- ifelse(is.na(datcase$ntopics), 0, datcase$ntopics)
datcase$ntopiccards <- ifelse(is.na(datcase$ntopiccards), 0, datcase$ntopiccards)

cor_mat <- cor(datcase[, -1], use = "pairwise")
diag(cor_mat) <- NA
heatmap(cor_mat)


normalize <- function(x) {
  (x - min(x)) / (max(x) - min(x))
}
# TODO: Move to helper file

# Features for navigation types for MTT:
# - Scanning / Overviewing:
#   * Proportion of artworks looked at is high
#   * Duration per artwork is low: "ave_duration_item" / datcase$duration
# - Exploring:
#   * Looking at additional information is high
# - Searching / Studying:
#   * Proportion of artworks looked at is low
#   * Opening few cards
#   datcase$nflipCard / mean(datcase$nflipCard) or median(datcase$nflipCard) is low
#   * but for most cards popups are opened:
#   datcase$nopenPopup / datcase$nflipCard is high
# - Wandering / Flitting:
#   * Proportion of moves is high
#   * Duration per case is low:
#   datcase$duration / mean(datcase$duration) or median(datcase$duration)
#   * Duration per artwork is low: "ave_duration_item" / datcase$duration

dattree <- data.frame(case = datcase$case,
                      PropItems = datcase$nitems / length(unique(dat$item)),
                      SearchInfo = datcase$nopenTopic + datcase$nopenPopup,
                      PropMoves = datcase$nmove / datcase$length,
                      PathLinearity = datcase$nitems / datcase$npaths,
                      Singularity = datcase$npaths / datcase$length
)

dattree$SearchInfo <- ifelse(is.na(dattree$NumTopic), 0, dattree$NumTopic)

get_centrality <- function(case, data) {

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

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

  net <- process_map(alog, render = FALSE)
  inet <- DiagrammeR::to_igraph(net)

  #c(igraph::centr_degree(inet, loops = FALSE)$centralization,
  #  igraph::centr_degree(inet, loops = TRUE)$centralization,
  #  igraph::centr_betw(inet)$centralization)
  igraph::centr_betw(inet)$centralization
}
# TODO: Move to helper file

centrality <- pbapply::pblapply(dattree$case, get_centrality, data = dat)
centrality <- do.call(rbind, centrality)

# save(centrality, file = "results/haum/tmp_centrality.RData")
#load("results/haum/tmp_centrality.RData")

dattree$BetweenCentrality <- unlist(centrality)

# Average duration per item
dat_split <- split(dat[, c("item", "case", "path", "timeMs.start", "timeMs.stop")], ~ path)
dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
dat_minmax <- dplyr::bind_rows(dat_list)

tmp <- aggregate(min_time ~ path, dat_minmax, unique)
tmp$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
tmp$duration <- tmp$max_time - tmp$min_time
tmp$case <- aggregate(case ~ path, dat_minmax, unique)$case

dattree$AvDurItem <- aggregate(duration ~ case, tmp, mean)$duration

rm(tmp)

# Indicator variable if table was used as info terminal only
dattree$InfocardOnly <- factor(datcase$infocardOnly, levels = 0:1,
                               labels = c("no", "yes"))

# Add pattern to datcase; loosely based on Bousbia et al. (2009)
dattree$Pattern <- "Dispersion"
dattree$Pattern <- ifelse(dattree$PathLinearity > 0.8 & dattree$Singularity > 0.8, "Scholar",
                          dattree$Pattern)
dattree$Pattern <- ifelse(dattree$PathLinearity <= 0.8 &
                          dattree$BetweenCentrality > 0.5, "Star",
                          dattree$Pattern)
dattree$Pattern <- factor(dattree$Pattern)
# TODO: Get rid of PathLinearity and Singularity as features when I am
# using Pattern?

dattree$PathLinearity <- NULL
dattree$Singularity <- NULL
dattree$BetweenCentrality <- NULL

summary(dattree)

plot(dattree[, -1], pch = ".")

par(mfrow = c(2,4))
hist(dattree$AvDurItem, breaks = 50, main = "")
hist(dattree$NumItems, breaks = 50, main = "")
hist(dattree$NumTopic, breaks = 50, main = "")
hist(dattree$NumPopup, breaks = 50, main = "")
hist(dattree$PropMoves, breaks = 50, main = "")
hist(dattree$PathLinearity, breaks = 50, main = "")
hist(dattree$Singularity, breaks = 50, main = "")
hist(dattree$BetweenCentrality, breaks = 50, main = "")

#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Remove cases with extreme outliers
# TODO: Do I want this???

quantile(datcase$nopenTopic, 0.999)
quantile(datcase$nopenPopup, 0.999)

dattree <- dattree[!(dattree$NumTopic > 40 | dattree$NumPopup > 40), ]

plot(dattree[, -1], pch = ".")

par(mfrow = c(2,4))
hist(dattree$AvDurItem, breaks = 50, main = "")
hist(dattree$NumItems, breaks = 50, main = "")
hist(dattree$NumTopic, breaks = 50, main = "")
hist(dattree$NumPopup, breaks = 50, main = "")
hist(dattree$PropMoves, breaks = 50, main = "")
hist(dattree$PathLinearity, breaks = 50, main = "")
hist(dattree$Singularity, breaks = 50, main = "")
hist(dattree$BetweenCentrality, breaks = 50, main = "")
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

#--------------- (2) Clustering ---------------

df <- dattree[, -1] # remove case variable

# Normalize Duration and SearchInfo
df$AvDurItem <- normalize(df$AvDurItem)
df$SearchInfo  <- normalize(df$SearchInfo)

summary(df)

# Look at collinearity
cor_mat <- cor(df)
diag(cor_mat) <- NA
heatmap(cor_mat)

#--------------- (2.2) Hierarchical clustering ---------------

dist_mat <- cluster::daisy(df, metric = "gower")

# "Flatten" with MDS
coor_2d <- as.data.frame(cmdscale(dist_mat, k = 2))
coor_3d <- as.data.frame(cmdscale(dist_mat, k = 3))
# TODO: Better use MASS::isoMDS() since I am not using Euclidean distances?

plot(coor_2d)
rgl::plot3d(coor_3d)

method <- c(average = "average", single = "single", complete = "complete",
            ward = "ward")

method <- "ward"

hcs <- pbapply::pblapply(method, function(x) cluster::agnes(dist_mat, method = x))
acs <- pbapply::pbsapply(hcs, function(x) x$ac)

hc <- hcs$ward

# Something like a scree plot (??)
plot(rev(hc$height)[1:100], type = "b", pch = 16, cex = .5)

k <- 4

mycols <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")

cluster <- cutree(as.hclust(hc), k = k)

table(cluster)

plot(coor_2d, col = mycols[cluster])
legend("topleft", paste("Cl", 1:4), col = mycols, pch = 21)
rgl::plot3d(coor_3d, col = mycols[cluster])

table(dattree[cluster == 1, "Pattern"])
table(dattree[cluster == 2, "Pattern"])
table(dattree[cluster == 3, "Pattern"])
table(dattree[cluster == 4, "Pattern"])

table(dattree[cluster == 1, "InfocardOnly"])
table(dattree[cluster == 2, "InfocardOnly"])
table(dattree[cluster == 3, "InfocardOnly"])
table(dattree[cluster == 4, "InfocardOnly"])

aggregate(. ~ cluster, df, mean)

aggregate(cbind(duration, distance, scaleSize, rotationDegree, length,
                nmove, nflipCard, nopenTopic, nopenPopup) ~ cluster, datcase,
          mean)

### Look at selected cases ###########################################
tmp <- dat
tmp$start <- tmp$date.start
tmp$complete <- tmp$date.stop

alog <- activitylog(tmp[tmp$case == 24016, ],
                    case_id     = "case",
                    activity_id = "item",
                    resource_id = "path",
                    timestamps  = c("start", "complete"))

process_map(alog)

rm(tmp)

######################################################################

res <- merge(dat, data.frame(case = dattree$case, cluster),
             by = "case", all.x = TRUE)
res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]

xtabs( ~ item + cluster, res)
aggregate(event ~ cluster, res, table)

# Look at clusters
par(mfrow = c(2, 2))
vioplot::vioplot(duration ~ cluster, res)
vioplot::vioplot(distance ~ cluster, res)
vioplot::vioplot(scaleSize ~ cluster, res)
vioplot::vioplot(rotationDegree ~ cluster, res)

aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, mean)
aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, median)

write.table(res,
            file = "results/haum/eventlogs_2019_case-clusters.csv",
            sep = ";",
            quote = FALSE,
            row.names = FALSE)

save(res, dist_mat, hcs, acs, datcase, dattree, coor_2d, coor_3d,
     file = "results/haum/tmp_user-navigation.RData")

#--------------- (3) Fit tree ---------------

library(rpart)
library(partykit)

c1 <- rpart(as.factor(cluster) ~ ., data = dattree[, -1], method = "class")
plot(as.party(c1))

# with conditional tree
c2 <- ctree(as.factor(cluster) ~ ., data = dattree[, -1], alpha = 0)
plot(c2)

#--------------- (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))


}