mtt_haum/code/10_validation.R

# 11_validation.R
#
# content: (1) Load data
#          (2) Extract characteristics for cases
#          (3) Select features for navigation behavior
#          (4) Clustering
#          (5) Fit tree
#
# input:  results/event_logfiles_2024-02-21_16-07-33.csv
# output: --
#
# last mod: 2024-03-22

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

source("R_helpers.R")

#--------------- (1) Read data ---------------

load("results/eventlogs_pre-corona_cleaned.RData")

# Select one year to handle number of cases
dat <- dat[as.Date(dat$date.start) > "2017-12-31" &
           as.Date(dat$date.start) < "2019-01-01", ]

#--------------- (2) Extract characteristics for cases ---------------

datcase18 <- aggregate(cbind(distance, scaleSize, rotationDegree) ~
                     case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)

datcase18$length <- aggregate(item ~ case, dat, length)$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"]

datcase18 <- datcase18 |>
  merge(eventtab, by = "case", all = TRUE)

rm(eventtab)

datcase18$nitems <- aggregate(item ~ case, dat, function(x)
                            length(unique(x)), na.action = NULL)$item
datcase18$npaths <- aggregate(path ~ case, dat, function(x)
                            length(unique(x)), na.action = NULL)$path

dat_split <- split(dat, ~ case)
dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
dat_minmax <- dplyr::bind_rows(dat_list)

datcase18$min_time <- aggregate(min_time ~ case, dat_minmax, unique)$min_time
datcase18$max_time <- aggregate(max_time ~ case, dat_minmax, unique)$max_time

datcase18$duration <- datcase18$max_time - datcase18$min_time
datcase18$min_time <- NULL
datcase18$max_time <- NULL

artworks <- unique(dat$item)[!unique(dat$item) %in% c("501", "502", "503")]
datcase18$infocardOnly <- pbapply::pbsapply(dat_split, check_infocards, artworks = artworks)

# Clean up NAs
datcase18$distance <- ifelse(is.na(datcase18$distance), 0, datcase18$distance)
datcase18$scaleSize <- ifelse(is.na(datcase18$scaleSize), 1, datcase18$scaleSize)
datcase18$rotationDegree <- ifelse(is.na(datcase18$rotationDegree), 0, datcase18$rotationDegree)

#--------------- (3) Select features for navigation behavior ---------------

dattree18 <- data.frame(case = datcase18$case,
                      PropItems = datcase18$nitems / length(unique(dat$item)),
                      SearchInfo = (datcase18$nopenTopic +
                                    datcase18$nopenPopup) / datcase18$length,
                      PropMoves = datcase18$nmove / datcase18$length,
                      PathLinearity = datcase18$nitems / datcase18$npaths,
                      Singularity = datcase18$npaths / datcase18$length
)

# centrality <- pbapply::pbsapply(dattree18$case, get_centrality, data = dat)
# save(centrality, file = "results/centrality_2018.RData")
load("results/centrality_2018.RData")

dattree18$BetweenCentrality <- centrality

# 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_ms)
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

dattree18$AvDurItem <- aggregate(duration ~ case, tmp, mean)$duration

rm(tmp)

# Indicator variable if table was used as info terminal only
dattree18$InfocardOnly <- factor(datcase18$infocardOnly, levels = 0:1,
                               labels = c("no", "yes"))

# Add pattern
dattree18$Pattern <- "Dispersion"
dattree18$Pattern <- ifelse(dattree18$PathLinearity > 0.8, "Scholar",
                          dattree18$Pattern)
dattree18$Pattern <- ifelse(dattree18$PathLinearity <= 0.8 &
                          dattree18$BetweenCentrality >= 0.5, "Star",
                          dattree18$Pattern)
dattree18$Pattern <- factor(dattree18$Pattern)

dattree18$AvDurItemNorm <- normalize(dattree18$AvDurItem)

#--------------- (4) Clustering ---------------

df <- dattree18[, c("PropItems", "SearchInfo", "PropMoves", "AvDurItemNorm",
                  "Pattern", "InfocardOnly")]

dist_mat18 <- cluster::daisy(df, metric = "gower")

coor_3d_18 <- smacof::mds(dist_mat18, ndim = 3, type = "ordinal")$conf
coor_2d_18 <- coor_3d_18[, 1:2]

plot(coor_2d_18)
rgl::plot3d(coor_3d_18)

hc18 <- cluster::agnes(dist_mat18, method = "ward")

k <- 5

mycols <- c("#91C86E", "#FF6900", "#3CB4DC", "#78004B", "#434F4F")

cluster18 <- cutree(as.hclust(hc18), k = k)

table(cluster18)

plot(coor_2d_18, col = mycols[cluster18], pch = 16)
legend("topleft", c("Searching", "Exploring", "Scanning", "Flitting", "Info"),
       col = mycols, bty = "n", pch = 16)
rgl::plot3d(coor_3d_18, col = mycols[cluster18])

print(ftable(xtabs( ~ InfocardOnly + Pattern + cluster18, dattree18)), zero = "-")

aggregate(. ~ cluster18, df, mean)
aggregate(. ~ cluster18, dattree18[, -1], mean)

save(coor_2d_18, coor_3d_18, cluster18, dattree18, dist_mat18, hc18,
     file = "../../thesis/figures/data/clustering_cases_2018.RData")

#--------------- (5) Fit tree ---------------

c1 <- rpart::rpart(as.factor(cluster18) ~ ., data = dattree18[, c("PropMoves",
                                                                  "PropItems",
                                                                  "SearchInfo",
                                                                  "AvDurItem",
                                                                  "Pattern",
                                                                  "InfocardOnly")],
                   method = "class")

plot(partykit::as.party(c1), tp_args = list(fill = mycols, col = mycols))


## Load data
load("../../thesis/figures/data/clustering_cases.RData")

c19 <- rpart::rpart(as.factor(cluster) ~ ., data = dattree[, c("PropMoves",
                                                               "PropItems",
                                                               "SearchInfo",
                                                               "AvDurItem",
                                                               "Pattern",
                                                               "InfocardOnly")],
                   method = "class")

cl18 <- rpart:::predict.rpart(c1, type = "class", newdata = dattree18)
cl18 <- factor(cl18, labels = c("Searching", "Exploring", "Scanning", "Flitting", "Info"))

cl19 <- rpart:::predict.rpart(c19, type = "class", newdata = dattree18)
cl19 <- factor(cl19, labels = c("Scanning", "Exploring", "Flitting", "Searching", "Info"))

xtabs( ~ cl18 + cl19)