Re-ran preprocessing and updated files; worked on user navigation behavior -- intermediate step

This commit is contained in:
Nora Wickelmaier 2024-02-27 09:08:20 +01:00
parent 3b518a553a
commit 42f12b9256
8 changed files with 451 additions and 64 deletions

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@ -126,6 +126,8 @@ dat1 <- merge(datlogs, hd, by.x = "date", by.y = "date", all.x = TRUE)
dat2 <- merge(dat1, sfdat, by.x = "date", by.y = "date", all.x = TRUE) dat2 <- merge(dat1, sfdat, by.x = "date", by.y = "date", all.x = TRUE)
dat2$date <- NULL dat2$date <- NULL
dat2 <- dat2[order(dat2$fileId.start, dat2$date.start, dat2$timeMs.start), ]
## Export data ## Export data

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@ -9,7 +9,7 @@
# (3.4) Artwork sequences # (3.4) Artwork sequences
# (3.5) Topics # (3.5) Topics
# #
# input: results/haum/event_logfiles_glossar_2023-12-28_09-49-43.csv # input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
# output: # output:
# #
# last mod: 2023-11-15, NW # last mod: 2023-11-15, NW
@ -27,7 +27,7 @@ library(bupaverse)
#--------------- (1) Read data --------------- #--------------- (1) Read data ---------------
datlogs <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv", datlogs <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
colClasses = c("character", "character", "POSIXct", colClasses = c("character", "character", "POSIXct",
"POSIXct", "character", "integer", "POSIXct", "character", "integer",
"numeric", "character", "character", "numeric", "character", "character",

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@ -5,7 +5,7 @@ from python_helpers import eval_pm, pn_infos_miner
###### Load data and create event logs ###### ###### Load data and create event logs ######
dat = pd.read_csv("results/haum/event_logfiles_2024-01-18_09-58-52.csv", sep = ";") dat = pd.read_csv("results/haum/event_logfiles_2024-02-21_16-07-33.csv", sep = ";")
event_log = pm4py.format_dataframe(dat, case_id = "path", event_log = pm4py.format_dataframe(dat, case_id = "path",
activity_key = "event", activity_key = "event",
@ -53,6 +53,7 @@ for i in range(len(replayed_traces)):
set(l1) set(l1)
x1 = np.array(l1) x1 = np.array(l1)
index_broken = np.where(x1 == 1)[0].tolist() index_broken = np.where(x1 == 1)[0].tolist()
len(index_broken)
set(l3) set(l3)
l4.count([]) l4.count([])

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@ -2,11 +2,11 @@
#--------------- (1) Look at broken trace --------------- #--------------- (1) Look at broken trace ---------------
datraw <- read.table("results/haum/raw_logfiles_2024-01-18_09-58-52.csv", datraw <- read.table("results/haum/raw_logfiles_2024-02-21_16-07-33.csv",
header = TRUE, sep = ";") header = TRUE, sep = ";")
datlogs <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv", datlogs <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
colClasses = c("character", "character", "POSIXct", colClasses = c("character", "character", "POSIXct",
"POSIXct", "character", "integer", "POSIXct", "character", "integer",
"numeric", "character", "character", "numeric", "character", "character",

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@ -6,7 +6,7 @@ from python_helpers import eval_pm, pn_infos
###### Load data and create event logs ###### ###### Load data and create event logs ######
dat = pd.read_csv("results/haum/event_logfiles_2024-01-18_09-58-52.csv", sep = ";") dat = pd.read_csv("results/haum/event_logfiles_2024-02-21_16-07-33.csv", sep = ";")
dat = dat[dat["date.start"] < "2020-03-13"] dat = dat[dat["date.start"] < "2020-03-13"]
# --> only pre corona (before artworks were updated) # --> only pre corona (before artworks were updated)
dat = dat[dat["path"] != 106098] dat = dat[dat["path"] != 106098]

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@ -1,4 +1,4 @@
# 08_item-clustering.R # 07_item-clustering.R
# #
# content: (1) Read data # content: (1) Read data
# (1.1) Read log event data # (1.1) Read log event data
@ -7,11 +7,11 @@
# (2) Clustering # (2) Clustering
# (3) Visualization with pictures # (3) Visualization with pictures
# #
# input: results/haum/event_logfiles_2024-01-18_09-58-52.csv # input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
# results/haum/pn_infos_items.csv # results/haum/pn_infos_items.csv
# output: results/haum/event_logfiles_pre-corona_with-clusters.csv # output: results/haum/event_logfiles_pre-corona_with-clusters.csv
# #
# last mod: 2024-01-30 # last mod: 2024-02-23
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code") # setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
@ -23,7 +23,7 @@ library(factoextra)
#--------------- (1.1) Read log event data --------------- #--------------- (1.1) Read log event data ---------------
dat0 <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv", dat0 <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
colClasses = c("character", "character", "POSIXct", colClasses = c("character", "character", "POSIXct",
"POSIXct", "character", "integer", "POSIXct", "character", "integer",
"numeric", "character", "character", "numeric", "character", "character",
@ -48,21 +48,47 @@ datitem <- read.table("results/haum/pn_infos_items.csv", header = TRUE,
#--------------- (1.3) Extract additional infos for clustering --------------- #--------------- (1.3) Extract additional infos for clustering ---------------
datitem$duration <- aggregate(duration ~ item, dat, mean)$duration dat_split <- split(dat, ~ path)
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
}
dat_list <- pbapply::pblapply(dat_split, time_minmax)
dat_minmax <- dplyr::bind_rows(dat_list)
datpath <- aggregate(duration ~ item + path, dat, mean, na.action = NULL)
datpath$min_time <- aggregate(min_time ~ path, dat_minmax, unique, na.action = NULL)$min_time
datpath$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
datpath$duration_path <- datpath$max_time - datpath$min_time
# average duration per path
datitem$duration <- aggregate(duration ~ item, datpath, mean)$duration
datitem$distance <- aggregate(distance ~ item, dat, mean)$distance datitem$distance <- aggregate(distance ~ item, dat, mean)$distance
datitem$scaleSize <- aggregate(scaleSize ~ item, dat, mean)$scaleSize datitem$scaleSize <- aggregate(scaleSize ~ item, dat, mean)$scaleSize
datitem$rotationDegree <- aggregate(rotationDegree ~ item, dat, mean)$rotationDegree datitem$rotationDegree <- aggregate(rotationDegree ~ item, dat, mean)$rotationDegree
datitem$npaths <- aggregate(path ~ item, dat, function(x) length(unique(x)))$path datitem$npaths <- aggregate(path ~ item, dat, function(x) length(unique(x)))$path
datitem$ncases <- aggregate(case ~ item, dat, function(x) length(unique(x)))$case datitem$ncases <- aggregate(case ~ item, dat, function(x) length(unique(x)))$case
datitem$ntopics <- aggregate(topic ~ item, dat, function(x) length(unique(x)))$topic datitem$nmoves <- aggregate(event ~ item, dat, table)$event[,"move"]
datitem$mostfreq_num <- as.numeric(gsub(".*: (.*)}", "\\1", datitem$mostfreq)) datitem$nflipCard <- aggregate(event ~ item, dat, table)$event[,"flipCard"]
datitem$nopenTopic <- aggregate(event ~ item, dat, table)$event[,"openTopic"]
datitem$nopenPopup <- aggregate(event ~ item, dat, table)$event[,"openPopup"]
#--------------- (2) Clustering --------------- #--------------- (2) Clustering ---------------
df <- datitem[, c("precision", "generalizability", "nvariants", df <- datitem[, c("precision", "generalizability", "nvariants", "duration",
"mostfreq_num", "duration", "distance", "scaleSize", "distance", "scaleSize", "rotationDegree", "npaths",
"rotationDegree", "npaths", "ncases", "ntopics")] |> "ncases", "nmoves", "nopenTopic", "nopenPopup")] |>
scale() scale()
mat <- dist(df) mat <- dist(df)
heatmap(as.matrix(mat)) heatmap(as.matrix(mat))
@ -88,6 +114,7 @@ cor(mat, c3)
cor(mat, c4) cor(mat, c4)
cor(mat, c5) cor(mat, c5)
# https://en.wikipedia.org/wiki/Cophenetic_correlation # https://en.wikipedia.org/wiki/Cophenetic_correlation
# https://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
# Dendograms # Dendograms
par(mfrow=c(3,2)) par(mfrow=c(3,2))
@ -101,15 +128,15 @@ plot(h5, main = "Single Linkage")
hc <- h1 hc <- h1
# Note that agnes(*, method="ward") corresponds to hclust(*, "ward.D2") # Note that agnes(*, method="ward") corresponds to hclust(*, "ward.D2")
k <- 7 # number of clusters k <- 4 # number of clusters
grp <- cutree(hc, k = k) grp <- cutree(hc, k = k)
datitem$grp <- grp datitem$grp <- grp
fviz_dend(hc, k = k, fviz_dend(hc, k = k,
cex = 0.5, cex = 0.5,
k_colors = c("#78004B", "#000000", "#3CB4DC", "#91C86E", k_colors = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
"#FF6900", "gold", "#434F4F"), "#000000", "gold", "#434F4F"),
#type = "phylogenic", #type = "phylogenic",
rect = TRUE rect = TRUE
) )
@ -120,15 +147,16 @@ rect.hclust(hc, k=7, border="blue")
rect.hclust(hc, k=6, border="green") rect.hclust(hc, k=6, border="green")
p <- fviz_cluster(list(data = df, cluster = grp), p <- fviz_cluster(list(data = df, cluster = grp),
palette = c("#78004B", "#000000", "#3CB4DC", "#91C86E", palette = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
"#FF6900", "#434F4F", "gold"), "#000000", "#434F4F", "gold"),
ellipse.type = "convex", ellipse.type = "convex",
repel = TRUE, repel = TRUE,
show.clust.cent = FALSE, ggtheme = theme_bw()) show.clust.cent = FALSE, ggtheme = theme_bw())
p p
aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths, aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
ncases, ntopics) ~ grp, datitem, median) ncases, nmoves, nflipCard, nopenTopic, nopenPopup) ~ grp,
datitem, median)
# Something like a scree plot (??) # Something like a scree plot (??)
plot(rev(seq_along(hc$height)), hc$height, type = "l") plot(rev(seq_along(hc$height)), hc$height, type = "l")
@ -189,15 +217,15 @@ library(png)
library(jpeg) library(jpeg)
library(grid) library(grid)
colors <- c("#78004B", "#000000", "#3CB4DC", "#91C86E", "#FF6900", colors <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")
"#434F4F")
#pdf("results/haum/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 10) pdf("results/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 10)
png("results/haum/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 10, res = 300) #png("results/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 10, res = 300)
par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0)) par(mai = c(.4,.4,.1,.1), mgp = c(2.4, 1, 0))
plot(y ~ x, p$data, type = "n", ylim = c(-3.2, 3), xlim = c(-4.7, 6.4)) plot(y ~ x, p$data, type = "n", ylim = c(-3.5, 2.8), xlim = c(-5, 10),
xlab = "", ylab = "")
for (item in sprintf("%03d", as.numeric(rownames(p$data)))) { for (item in sprintf("%03d", as.numeric(rownames(p$data)))) {
@ -221,12 +249,13 @@ for (item in sprintf("%03d", as.numeric(rownames(p$data)))) {
pch = 15) pch = 15)
rasterImage(img, rasterImage(img,
xleft = x - .4, xleft = x - .45,
xright = x + .4, xright = x + .45,
ybottom = y - .2, ybottom = y - .2,
ytop = y + .2) ytop = y + .2)
} }
legend("topright", paste("Cluster", 1:k), col = colors, pch = 15, bty = "n")
dev.off() dev.off()

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@ -6,10 +6,11 @@
# (2) Clustering # (2) Clustering
# (3) Investigate variants # (3) Investigate variants
# #
# input: results/haum/event_logfiles_2024-01-18_09-58-52.csv # input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
# output: results/haum/event_logfiles_pre-corona_with-clusters_cases.csv # output: results/haum/event_logfiles_pre-corona_with-clusters_cases.csv
# results/haum/dattree.csv
# #
# last mod: 2024-02-07 # last mod: 2024-02-23
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code") # setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
@ -21,7 +22,7 @@ library(factoextra)
#--------------- (1.1) Read log event data --------------- #--------------- (1.1) Read log event data ---------------
dat0 <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv", dat0 <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
colClasses = c("character", "character", "POSIXct", colClasses = c("character", "character", "POSIXct",
"POSIXct", "character", "integer", "POSIXct", "character", "integer",
"numeric", "character", "character", "numeric", "character", "character",
@ -32,6 +33,7 @@ dat0 <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv",
dat0$event <- factor(dat0$event, levels = c("move", "flipCard", "openTopic", dat0$event <- factor(dat0$event, levels = c("move", "flipCard", "openTopic",
"openPopup")) "openPopup"))
dat0$topic <- factor(dat0$topic)
dat0$weekdays <- factor(weekdays(dat0$date.start), dat0$weekdays <- factor(weekdays(dat0$date.start),
levels = c("Montag", "Dienstag", "Mittwoch", levels = c("Montag", "Dienstag", "Mittwoch",
@ -48,18 +50,43 @@ dat <- dat[dat$path != 106098, ]
#--------------- (1.2) Extract additional infos for clustering --------------- #--------------- (1.2) Extract additional infos for clustering ---------------
datcase <- aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ datcase <- aggregate(cbind(distance, scaleSize, rotationDegree) ~
case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL) case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
datcase$length <- aggregate(item ~ case, dat, length)$item datcase$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)$topic[, 8]
datcase <- datcase |>
merge(eventtab, by = "case", all = TRUE) |>
merge(topictab, by = "case", all = TRUE)
datcase$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) datcase$nitems <- aggregate(item ~ case, dat, function(x)
length(unique(x)), na.action = NULL)$item length(unique(x)), na.action = NULL)$item
datcase$npaths <- aggregate(path ~ case, dat, function(x) datcase$npaths <- aggregate(path ~ case, dat, function(x)
length(unique(x)), na.action = NULL)$path length(unique(x)), na.action = NULL)$path
# datcase$ntopics <- aggregate(topic ~ case, dat,
# function(x) ifelse(all(is.na(x)), NA,
# length(unique(na.omit(x)))), na.action =
# NULL)$topic
datcase$vacation <- aggregate(vacation ~ case, dat, datcase$vacation <- aggregate(vacation ~ case, dat,
function(x) ifelse(all(is.na(x)), 0, 1), function(x) ifelse(all(is.na(x)), 0, 1),
na.action = NULL)$vacation na.action = NULL)$vacation
@ -73,42 +100,370 @@ datcase$morning <- aggregate(date.start ~ case, dat,
function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1), function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
na.action = NULL)$date.start na.action = NULL)$date.start
datcase <- na.omit(datcase) dat_split <- split(dat, ~ case)
#--------------- (2) Clustering --------------- 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
}
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$max_time <- NULL
cor_mat <- cor(datcase[, -1], use = "pairwise")
diag(cor_mat) <- NA
heatmap(cor_mat)
# TODO: Add info if all items of a case are information cards??
# Navigation types by Bousbia et al. (2010):
# - Overviewing: this value is close to the Canter “scanning” value. It
# implies that the learner is covering a large proportion of course pages.
# Through this phase of fast-reading, the user seeks to acquire an
# overall view of the course.
# - Flitting: close to “wandering”. It reflects a browsing activity without a
# strategy or a particular goal. The main difference with the overviewing
# type is the lack of focus on the course.
# - Studying: corresponds to a partial or complete reading of the course
# pages where the learner spends time on each page.
# - Deepening: This describes a learner who spends relatively long time on a
# course, checking details, and seeking Web documents related to the course
# topics. The main difference with studying is the Web search part that the
# learner uses to obtain a deeper understanding of the course.
# Taxonomy defined by Canter et al. (1985):
# - Scanning: seeking an overview of a theme (i.e. subpart of the hypermedia)
# by requesting an important proportion of its pages but without spending
# much time on them.
# - Browsing: going wherever the data leads the navigator until catching an
# interest.
# - Exploring: reading the viewed pages thoroughly.
# - Searching: seeking for a particular document or information.
# - Wandering: navigating in an unstructured fashion without any particular
# goal or strategy.
# Features for navigation types for MTT:
# - Scanning / Overviewing:
# * Proportion of artworks looked at is high: datcase$nitems / 70
# * Duration per artwork is low: "ave_duration_item" / datcase$duration
# - Exploring:
# * Looking at additional information for most items touched (high value):
# harmonic mean of datcase$nopenTopic / datcase$nflipCard and
# datcase$nopenPopup / datcase$nopenTopic
# - Searching / Studying:
# * Looking only at a few items
# datcase$nitems / 70 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$nflipCard is high
# - Wandering / Flitting:
# * Items are mostly just moved:
# datcase$nmove / datcase$length is high
# * Duration per case is low:
# datcase$duration / mean(datcase$duration) or median(datcase$duration)
# * Duration per artwork is low: "ave_duration_item" / datcase$duration
# TODO: Come up with relevant features for navigation behavior
dattree <- data.frame(case = datcase$case,
Duration = datcase$duration,
PropItems = datcase$nitems / length(unique(dat$item)),
SearchInfo =
2*(((datcase$nopenPopup / datcase$nopenTopic) *
(datcase$nopenTopic / datcase$nflipCard)) /
((datcase$nopenPopup / datcase$nopenTopic) +
(datcase$nopenTopic / datcase$nflipCard))
),
PropMoves = datcase$nmove / datcase$length,
PathLinearity = datcase$nitems / datcase$npaths,
Singularity = datcase$npaths / datcase$length
)
dattree$SearchInfo <- ifelse(dattree$SearchInfo %in% 0, 0.1, dattree$SearchInfo)
dattree$SearchInfo <- ifelse(is.na(dattree$SearchInfo), 0, dattree$SearchInfo)
get_centrality <- function(case, data) {
data$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)$centralization)
}
centrality <- lapply(dattree$case, get_centrality, data = dat)
centrality <- do.call(rbind, centrality)
save(centrality, file = "results/haum/tmp_centrality.RData")
dattree$centr_degree <- centrality[, 1]
dattree$centr_degree_loops <- centrality[, 2]
dattree$centr_between <- centrality[, 3]
par(mfrow = c(3,3))
hist(dattree$Duration, breaks = 50, main = "")
hist(dattree$SearchInfo, breaks = 50, main = "")
hist(dattree$PropItems, breaks = 50, main = "")
hist(dattree$PropMoves, breaks = 50, main = "")
hist(dattree$PathLinearity, breaks = 50, main = "")
hist(dattree$Singularity, breaks = 50, main = "")
hist(dattree$centr_degree, breaks = 50, main = "")
hist(dattree$centr_degree_loops, breaks = 50, main = "")
hist(dattree$centr_between, breaks = 50, main = "")
cor_mat <- cor(dattree[, -1], use = "pairwise")
diag(cor_mat) <- NA
heatmap(cor_mat)
dattree$Pattern <- "Dispersion"
dattree$Pattern <- ifelse(dattree$PathLinearity > 0.8 &
dattree$Singularity > 0.8, "Scholar",
dattree$Pattern)
dattree$Pattern <- ifelse(dattree$PathLinearity <= 0.8 &
dattree$centr_between > 0.5, "Star",
dattree$Pattern)
write.table(dattree,
file = "results/haum/dattree.csv",
sep = ";",
quote = FALSE,
row.names = FALSE)
tmp <- dat
tmp$start <- tmp$date.start
tmp$complete <- tmp$date.stop
alog <- activitylog(tmp[tmp$case == 3448, ],
case_id = "case",
activity_id = "item",
resource_id = "path",
timestamps = c("start", "complete"))
process_map(alog)
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)
df <- dattree[, c("Duration", "PropItems", "SearchInfo", "PropMoves")]
df$Scholar <- ifelse(dattree$Pattern == "Scholar", 1, 0)
df$Star <- ifelse(dattree$Pattern == "Star", 1, 0)
df$Dispersion <- ifelse(dattree$Pattern == "Dispersion", 1, 0)
# scale Duration and min/max SearchInfo
df$Duration <- scale(df$Duration)
df$SearchInfo <- (df$SearchInfo - min(df$SearchInfo)) /
(max(df$SearchInfo) - min(df$SearchInfo))
df <- datcase[, c("duration", "distance", "scaleSize", "rotationDegree",
"length", "nitems", "npaths")] |>
scale()
#df <- cbind(df, datcase[, c("vacation", "holiday", "weekend", "morning")])
mat <- dist(df) mat <- dist(df)
# TODO: Do I need to scale all variables?
hc <- hclust(mat, method = "ward.D2") h1 <- hclust(mat, method = "average")
h2 <- hclust(mat, method = "complete")
h3 <- hclust(mat, method = "ward.D")
h4 <- hclust(mat, method = "ward.D2")
h5 <- hclust(mat, method = "single")
grp <- cutree(hc, k = 6) # Cophenetic Distances, for each linkage (runs quite some time!)
datcase$grp <- grp c1 <- cophenetic(h1)
c2 <- cophenetic(h2)
c3 <- cophenetic(h3)
c4 <- cophenetic(h4)
c5 <- cophenetic(h5)
# Correlations
cor(mat, c1)
# 0.9029232
cor(mat, c2)
# 0.8879478
cor(mat, c3)
# 0.5747296
cor(mat, c4)
# 0.5994121
cor(mat, c5)
# 0.5292353
# https://en.wikipedia.org/wiki/Cophenetic_correlation
# https://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
hc <- h1
# Something like a scree plot (??)
plot(rev(hc$height)[1:100], type = "b", pch = 16, cex = .5)
k <- 4
grp <- cutree(hc, k = k)
df$grp <- grp
table(grp) table(grp)
fviz_cluster(list(data = df, cluster = grp), fviz_cluster(list(data = df, cluster = grp),
palette = c("#78004B", "#000000", "#3CB4DC", "#91C86E", #palette = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
"#FF6900", "#434F4F"), # "#000000", "#434F4F"),
ellipse.type = "convex", ellipse.type = "convex",
show.clust.cent = FALSE, ggtheme = theme_bw()) show.clust.cent = FALSE, ggtheme = theme_bw())
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length, table(dattree[dattree$grp == 3, "Pattern"])
nitems, npaths) ~ grp, datcase, mean)
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length,
nitems, npaths) ~ grp, datcase, median)
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length,
nitems, npaths) ~ grp, datcase, max)
res <- merge(dat, datcase[, c("case", "grp")], by = "case", all.x = TRUE)
aggregate(cbind(Duration, PropItems, SearchInfo, PropMoves, PathLinearity,
Singularity, centr_degree, centr_degree_loops,
centr_between) ~ grp, dattree, mean)
aggregate(cbind(Duration, PropItems, SearchInfo, PropMoves, Dispersion,
Scholar, Star) ~ grp, df, mean)
# "We first extract the graph sub-sequences corresponding to the four
# patterns of Canter et al. (1985). We also identified the number of nodes
# to which the learner often goes back to (Fig. 4). These nodes are called
# “central nodes”. If the number of central nodes is lower than or equal to
# half of the sub-sequences, the browsing pattern indicator takes on the
# value “Star”." Bousbia et al. (2010)
# I do not know how they got the sub-sequences. I am taking the ratio of
# strongly connected nodes to weakly connected nodes. If the number of
# weakly connected nodes is twice as high, the pattern is classified as a
# star, i.e., NodeConnect <= 0.5.
# TODO: This does not make sense, smallest and most frequent number is 3!
# (and I do not understand it...)
# count_asymmetric_node_pairs Get the number of asymmetrically-connected node pairs
# count_edges Get a count of all edges
# count_loop_edges Get count of all loop edges
# count_mutual_node_pairs Get the number of mutually-connected node pairs
# count_unconnected_node_pairs Get the number of unconnected node pairs
# count_unconnected_nodes Get count of all unconnected nodes
# TODO: Read up on centrality measures
# https://www.r-bloggers.com/2018/12/network-centrality-in-r-an-introduction/
# https://www.datacamp.com/tutorial/centrality-network-analysis-R
# http://davidrajuh.net/reggie/publications/publications-filer/rd114-2018-Network-Centrality.pdf
# https://link.springer.com/article/10.1007/s10618-024-01003-4
#--------------- (2) Clustering ---------------
df <- na.omit(datcase[, c("duration", "distance", "scaleSize",
"rotationDegree", "length", "nmove",
"nitems", "npaths")])
#df <- cbind(df, datcase[, c("vacation", "holiday", "weekend", "morning")])
mat <- dist(scale(df))
#mat <- dist(df)
h1 <- hclust(mat, method = "average")
h2 <- hclust(mat, method = "complete")
h3 <- hclust(mat, method = "ward.D")
h4 <- hclust(mat, method = "ward.D2")
h5 <- hclust(mat, method = "single")
# Cophenetic Distances, for each linkage (runs quite some time!)
c1 <- cophenetic(h1)
c2 <- cophenetic(h2)
c3 <- cophenetic(h3)
c4 <- cophenetic(h4)
c5 <- cophenetic(h5)
# Correlations
cor(mat, c1)
cor(mat, c2)
cor(mat, c3)
cor(mat, c4)
cor(mat, c5)
# https://en.wikipedia.org/wiki/Cophenetic_correlation
# https://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
hc <- h1
# Something like a scree plot (??)
plot(rev(hc$height)[1:100], type = "b", pch = 16, cex = .5)
# TODO: Something is wrong
k <- 4
grp <- cutree(hc, k = k)
df$grp <- grp
table(grp)
fviz_cluster(list(data = df, cluster = grp),
#palette = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
# "#000000", "#434F4F"),
ellipse.type = "convex",
show.clust.cent = FALSE, ggtheme = theme_bw())
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length, nmove,
nitems, npaths) ~ grp, df, mean)
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length, nmove,
nitems, npaths) ~ grp, df, median)
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length, nmove,
nitems, npaths) ~ grp, df, max)
df$case <- na.omit(datcase[, c("case", "duration", "distance", "scaleSize",
"rotationDegree", "length", "nmove",
"nitems", "npaths")])$case
res <- merge(dat, df[, c("case", "grp")], by = "case", all.x = TRUE)
res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ] res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
xtabs( ~ item + grp, res) xtabs( ~ item + grp, res)
aggregate(event ~ grp, res, table)
# Look at clusters # Look at clusters
par(mfrow = c(2, 2))
vioplot::vioplot(duration ~ grp, res) vioplot::vioplot(duration ~ grp, res)
vioplot::vioplot(distance ~ grp, res) vioplot::vioplot(distance ~ grp, res)
vioplot::vioplot(scaleSize ~ grp, res) vioplot::vioplot(scaleSize ~ grp, res)