# 10_user-navigation.R
#
# content: (1) Load data
#          (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: 2024-03-08

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

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

load("results/haum/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_2d <- as.data.frame(cmdscale(dist_mat, k = 2))
# coor_3d <- as.data.frame(cmdscale(dist_mat, k = 3))
 
# coor_2d <- prcomp(df)$x[, 1:2]
# coor_3d <- prcomp(df)$x[, 1:3]

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

plot(coor_2d)
rgl::plot3d(coor_3d)

# 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$ac)
# hc <- hcs$ward

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

k <- 5

mycols <- c("#434F4F", "#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 == 5, "Pattern"])

table(dattree[cluster == 1, "InfocardOnly"])
table(dattree[cluster == 2, "InfocardOnly"])
table(dattree[cluster == 3, "InfocardOnly"])
table(dattree[cluster == 4, "InfocardOnly"])
table(dattree[cluster == 5, "InfocardOnly"])

aggregate(. ~ cluster, df, mean)

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

### Look at selected cases ###########################################

load("")

tmp <- dat
tmp$start <- tmp$date.start
tmp$complete <- tmp$date.stop

alog <- bupaR::activitylog(tmp[tmp$case == 24016, ],
                           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/haum/eventlogs_2019_case-clusters.csv",
            sep = ";",
            quote = FALSE,
            row.names = FALSE)

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

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