# 10_user-navigation.R
#
# content: (1) Load data
#          (2) Clustering
#          (3) Fit tree
#
# input:  results/dataframes_case_2019.RData
# output: results/eventlogs_2019_case-clusters.csv
#         results/user-navigation.RData
#         ../../thesis/figures/data/clustering_cases.RData
#
# last mod: 2024-03-22

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

#--------------- (1) Load data ---------------

load("results/dataframes_case_2019.RData")

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

df <- dattree[, c("PropItems", "SearchInfo", "PropMoves", "AvDurItemNorm",
                  "Pattern", "InfocardOnly")]

summary(df)

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

# "Flatten" with MDS
# coor_3d <- as.data.frame(cmdscale(dist_mat, k = 3))
# coor_3d <- prcomp(df)$x[, 1:3]

coor_3d <- smacof::mds(dist_mat, ndim = 3, type = "ordinal")$conf
coor_2d <- coor_3d[, 1:2]

plot(coor_2d)
rgl::plot3d(coor_3d)

# pm <- cluster::pam(dist_mat, k = k)
# cluster <- pm$clustering
# --> Does not look as good as the hierarchical clustring

method <- c(average = "average", single = "single", complete = "complete",
            ward = "ward")
hcs <- pbapply::pblapply(method, function(x) cluster::agnes(dist_mat, method = x))
acs <- pbapply::pbsapply(hcs, function(x) x$ac)
#   average    single  complete      ward
# 0.9881224 0.9725661 0.9937669 0.9994267
hc <- hcs$ward

#hc <- cluster::agnes(dist_mat, method = "ward")

k <- 5

mycols <- c("#3CB4DC", "#FF6900", "#78004B", "#91C86E", "#434F4F")

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

table(cluster)

plot(coor_2d, col = mycols[cluster], pch = 16)
#legend("topleft", paste("Cl", 1:5), col = mycols, pch = 21)
legend("topleft", c("Scanning", "Exploring", "Flitting", "Searching", "Info"),
       col = mycols, bty = "n", pch = 16)
rgl::plot3d(coor_3d, col = mycols[cluster])

print(ftable(xtabs( ~ InfocardOnly + Pattern + cluster, dattree)), zero = "-")

aggregate(. ~ cluster, df, mean)

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

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

# Examples:
## Scholar:     29679
## Star:        24456
## Dispersion:  26000

## only info cards:     24299
## not only info cards: 24013

#head(dattree[dattree$Pattern == "Dispersion" & datcase$nitems == 10, ])
#head(dattree[dattree$InfocardOnly == "yes" & datcase$nitems == 3, ])

alog <- bupaR::activitylog(tmp[tmp$case == 24013, ],
                           case_id     = "case",
                           activity_id = "item",
                           resource_id = "path",
                           timestamps  = c("start", "complete"))

processmapR::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/eventlogs_2019_case-clusters.csv",
            sep = ";",
            quote = FALSE,
            row.names = FALSE)

save(res, dist_mat, hcs, acs, coor_2d, coor_3d,
     file = "results/user-navigation.RData")

save(coor_2d, coor_3d, cluster, dattree,
     file = "../../thesis/figures/data/clustering_cases.RData")


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

c1 <- rpart::rpart(as.factor(cluster) ~ ., data = dattree[, c("PropMoves",
                                                              "PropItems",
                                                              "SearchInfo",
                                                              "AvDurItem",
                                                              "Pattern",
                                                              "InfocardOnly")],
                   method = "class")

plot(partykit::as.party(c1), tp_args = list(fill = mycols, col = mycols))

# with conditional tree
c2 <- partykit::ctree(as.factor(cluster) ~ ., data = dattree[, c("PropMoves",
                                                                 "PropItems",
                                                                 "SearchInfo",
                                                                 "AvDurItem",
                                                                 "Pattern",
                                                                 "InfocardOnly")],
                      alpha = 0.001)

plot(c2, tp_args = list(fill = mycols, col = mycols))




factoextra::fviz_dend(as.hclust(hc), k = k,
                      cex = 0.5,
                      k_colors = mycols,
                      type = "phylogenic",
                      rect = TRUE,
                      main = "",
                      ylab = ""
                      #ggtheme = ggplot2::theme_bw()
)