142 lines
3.8 KiB
R
142 lines
3.8 KiB
R
|
# 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)
|
||
|
|
||
|
|