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mtt_haum/code/09_user-navigation.R

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# 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
# (3) Investigate variants
#
# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
# output: results/haum/event_logfiles_pre-corona_with-clusters_cases.csv
# results/haum/dattree.csv
#
# last mod: 2024-02-27
# 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 ---------------
dat0 <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
colClasses = c("character", "character", "POSIXct",
"POSIXct", "character", "integer",
"numeric", "character", "character",
rep("numeric", 3), "character",
"character", rep("numeric", 11),
"character", "character"),
sep = ";", header = TRUE)
dat0$event <- factor(dat0$event, levels = c("move", "flipCard", "openTopic",
"openPopup"))
dat0$topic <- factor(dat0$topic)
dat0$weekdays <- factor(weekdays(dat0$date.start),
levels = c("Montag", "Dienstag", "Mittwoch",
"Donnerstag", "Freitag", "Samstag",
"Sonntag"),
labels = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday",
"Sunday"))
# Select data pre Corona
dat <- dat0[as.Date(dat0$date.start) < "2020-03-13", ]
dat <- dat[dat$path != 106098, ]
rm(dat0)
#--------------- (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 <- 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: Export from package mtt
dat_list <- pbapply::pblapply(dat_split, time_minmax)
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$case,
NumItems = datcase$nitems,
NumTopic = datcase$nopenTopic,
NumPopup = datcase$nopenPopup,
PropMoves = datcase$nmove / datcase$length,
PathLinearity = datcase$nitems / datcase$npaths,
Singularity = datcase$npaths / datcase$length
)
dattree$NumTopic <- ifelse(is.na(dattree$NumTopic), 0, dattree$NumTopic)
dattree$NumPopup <- ifelse(is.na(dattree$NumPopup), 0, dattree$NumPopup)
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)
}
# TODO: Move to helper file
# centrality <- lapply(dattree$case, get_centrality, data = dat)
# centrality <- do.call(rbind, centrality)
#
# save(centrality, file = "results/haum/tmp_centrality.RData")
load("results/haum/tmp_centrality.RData")
#dattree$DegreeCentrality <- centrality[, 2]
dattree$BetweenCentrality <- centrality[, 3]
## Add average duration per item
dat_split <- split(dat[, c("item", "case", "path", "timeMs.start", "timeMs.stop")], ~ path)
dat_list <- pbapply::pblapply(dat_split, time_minmax)
dat_minmax <- dplyr::bind_rows(dat_list)
tmp <- aggregate(min_time ~ path, dat_minmax, unique)
tmp$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
#dattree$AvDurItem <- dattree$AvDurItem / datcase$duration
rm(tmp)
summary(dattree)
plot(dattree[, -1], pch = ".")
par(mfrow = c(2,4))
hist(dattree$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$BetweenCentrality, breaks = 50, main = "")
# Indicator variable if table was used as info terminal only
dattree$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)
# 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 ---------------
library(cluster)
df <- dattree[1:10000, -1] # remove case variable
# TODO: Do I need to scale or does normalization also work?
# Normalize Duration and Numbers
# df$AvDurItem <- normalize(df$AvDurItem)
# df$NumItems <- normalize(df$NumItems)
# df$NumTopic <- normalize(df$NumTopic)
# df$NumPopup <- normalize(df$NumPopup)
# summary(df)
# Look at collinearity
cor_mat <- cor(df)
diag(cor_mat) <- NA
heatmap(cor_mat)
#df <- as.data.frame(scale(dattree[, -1]))
#--------------- (2.2) Hierarchical clustering ---------------
mat <- daisy(df, metric = "gower")
# "Flatten" with MDS
coor_2d <- as.data.frame(cmdscale(mat, k = 2))
coor_3d <- as.data.frame(cmdscale(mat, k = 3))
plot(coor_2d)
rgl::plot3d(coor_3d)
#mat <- dist(df)
# https://uc-r.github.io/hc_clustering
method <- c(average = "average", single = "single", complete = "complete",
ward = "ward.D2")
hc_method <- function(x) {
hclust(mat, method = x)
}
hcs <- lapply(method, hc_method)
cds <- lapply(hcs, cophenetic)
cors <- sapply(cds, cor, y = mat)
# https://en.wikipedia.org/wiki/Cophenetic_correlation
# https://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
hc <- hcs$average
# Something like a scree plot (??)
plot(rev(hc$height)[1:100], type = "b", pch = 16, cex = .5)
k <- 4
mycols <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")
grp_hclust <- cutree(hc, k = k)
table(grp_hclust)
fviz_cluster(list(data = df, cluster = grp_hclust),
palette = mycols,
ellipse.type = "convex",
show.clust.cent = FALSE,
ggtheme = theme_bw())
plot(coor_2d, col = mycols[grp_hclust])
legend("topleft", paste("Cl", 1:4), col = mycols, pch = 21)
rgl::plot3d(coor_3d, col = mycols[grp_hclust])
table(datcase[grp_hclust == 1, "Pattern"])
table(datcase[grp_hclust == 2, "Pattern"])
table(datcase[grp_hclust == 3, "Pattern"])
table(datcase[grp_hclust == 4, "Pattern"])
aggregate(. ~ grp_hclust, df, mean)
aggregate(cbind(duration, distance, scaleSize, rotationDegree, length,
nmove, nflipCard, nopenTopic, nopenPopup) ~ grp_hclust, datcase,
mean)
#--------------- (2.3) DBSCAN clustering ---------------
library(dbscan)
d1 <- dbscan(df, eps = 1, minPts = ncol(df) + 1)
hullplot(df, d1)
grp_db <- d1$cluster
table(grp_db)
kNNdistplot(df, k = ncol(df))
abline(h = 0.2, col = "red")
abline(h = 1, col = "red")
fviz_cluster(list(data = df[grp_db != 0, ], cluster = grp_db[grp_db != 0]),
palette = mycols,
ellipse.type = "convex",
show.clust.cent = FALSE,
ggtheme = theme_bw())
mycols <- c("black", mycols)
plot(coor_2d, col = mycols[grp_db + 1])
legend("topleft", paste("Cl", 0:4), col = mycols, pch = 21)
rgl::plot3d(coor_3d, col = mycols[grp_db + 1])
aggregate(. ~ grp_db, df, mean)
table(datcase[grp_db == 0, "Pattern"])
table(datcase[grp_db == 1, "Pattern"])
table(datcase[grp_db == 2, "Pattern"])
table(datcase[grp_db == 3, "Pattern"])
table(datcase[grp_db == 4, "Pattern"])
### Look at selected cases ###########################################
dattree[grp_db == 0, ]
tmp <- dat
tmp$start <- tmp$date.start
tmp$complete <- tmp$date.stop
alog <- activitylog(tmp[tmp$case == 15, ],
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$case, grp_km, grp_hclust, grp_db),
by = "case", all.x = TRUE)
res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
xtabs( ~ item + grp_db, res)
aggregate(event ~ grp_db, res, table)
# Look at clusters
par(mfrow = c(2, 2))
vioplot::vioplot(duration ~ grp_db, res)
vioplot::vioplot(distance ~ grp_db, res)
vioplot::vioplot(scaleSize ~ grp_db, res)
vioplot::vioplot(rotationDegree ~ grp_db, res)
aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ grp_db, res, mean)
aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ grp_db, res, median)
write.table(res,
file = "results/haum/event_logfiles_pre-corona_with-clusters_cases.csv",
sep = ";",
quote = FALSE,
row.names = FALSE)
save(res, mat, h1, h2, h3, h4, h5, c1, c2, c3, c4, c5, datcase, dattree, df,
file = "results/haum/tmp_user-navigation.RData")
#--------------- (3) Fit tree ---------------
library(rpart)
library(partykit)
dattree_db <- dattree[grp_db != 0, -1]
dattree_db$grp <- factor(grp_db[grp_db != 0])
c1 <- rpart(grp ~ ., data = dattree_db, method = "class")
plot(as.party(c1))
c2 <- rpart(as.factor(grp_hclust) ~ ., data = dattree[, -1], method = "class")
plot(as.party(c2))
# with conditional tree
c2 <- ctree(grp ~ ., data = dattree_db, alpha = 0.05)
plot(c2)
# with excluded points
c5 <- ctree(factor(grp_db) ~ ., data = dattree[, -1], alpha = 0)
plot(c5)
# with excluded points
c6 <- ctree(factor(grp_db) ~ ., data = df, alpha = 0)
plot(c6)
# --> just checking
#--------------- (4) Investigate variants ---------------
res$start <- res$date.start
res$complete <- res$date.stop
alog <- activitylog(res,
case_id = "case",
activity_id = "item",
resource_id = "path",
timestamps = c("start", "complete"))
trace_explorer(alog, n_traces = 25)
# --> sequences of artworks are just too rare
tr <- traces(alog)
trace_length <- sapply(strsplit(tr$trace, ","), length)
tr[trace_length > 10, ]
trace_varied <- sapply(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,]
sapply(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))
}
########################### TODO: Still need it?
net <- process_map(alog, render = FALSE)
#DiagrammeR::get_node_df(net)
DiagrammeR::get_node_info(net)
DiagrammeR::get_degree_distribution(net)
DiagrammeR::get_degree_in(net)
DiagrammeR::get_degree_out(net)
DiagrammeR::get_degree_total(net)
N <- DiagrammeR::count_nodes(net) - 2 # Do not count start and stop nodes
dc <- DiagrammeR::get_degree_total(net)[1:N, "total_degree"] / (N - 1)
inet <- DiagrammeR::to_igraph(net)
igraph::centr_degree(inet, loops = FALSE)
igraph::centr_betw(inet)
igraph::centr_clo(inet)
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