# 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")

library(bupaverse)
library(factoextra)
library(rpart)
library(partykit)

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

load("results/haum/dataframes_case_2019.RData")

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

df <- dattree[, -1]

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 ---------------

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)