Added validation script redoing analysis for case clustering for data from 2018
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# content: (1) Load data
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# (2) Clustering
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# (3) Fit tree
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# (4) Investigate variants
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#
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# output: results/haum/eventlogs_pre-corona_case-clusters.csv
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# input: results/haum/dataframes_case_2019.RData
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# output: results/haum/eventlogs_2019_case-clusters.csv
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# results/haum/tmp_user-navigation.RData
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# ../../thesis/figures/data/clustering_cases.RData
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#
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# last mod: 2024-03-14
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# last mod: 2024-03-15
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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@ -126,7 +127,7 @@ write.table(res,
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save(res, dist_mat, hcs, acs, coor_2d, coor_3d,
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file = "results/haum/tmp_user-navigation.RData")
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save(coor_2d, coor_3d, cluster,
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save(coor_2d, coor_3d, cluster, dattree,
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file = "../../thesis/figures/data/clustering_cases.RData")
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@ -140,9 +141,7 @@ c1 <- rpart::rpart(as.factor(cluster) ~ ., data = dattree[, c("PropMoves",
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"InfocardOnly")],
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method = "class")
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pdf("results/figures/tree_cases_rpart.pdf", height = 5, width = 15, pointsize = 10)
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plot(partykit::as.party(c1), tp_args = list(fill = mycols, col = mycols))
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dev.off()
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# with conditional tree
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c2 <- partykit::ctree(as.factor(cluster) ~ ., data = dattree[, c("PropMoves",
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@ -153,9 +152,7 @@ c2 <- partykit::ctree(as.factor(cluster) ~ ., data = dattree[, c("PropMoves",
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"InfocardOnly")],
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alpha = 0.001)
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pdf("results/figures/tree_cases_ctree.pdf", height = 7, width = 20, pointsize = 10)
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plot(c2, tp_args = list(fill = mycols, col = mycols))
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dev.off()
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@ -163,7 +160,7 @@ dev.off()
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factoextra::fviz_dend(as.hclust(hc), k = k,
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cex = 0.5,
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k_colors = mycols,
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#type = "phylogenic",
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type = "phylogenic",
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rect = TRUE,
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main = "",
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ylab = ""
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181
code/11_validation.R
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181
code/11_validation.R
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# 11_validation.R
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#
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# content: (1) Load data
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# (2) Extract characteristics for cases
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# (3) Select features for navigation behavior
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# (4) Clustering
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# (5) Fit tree
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#
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# output: results/haum/eventlogs_pre-corona_case-clusters.csv
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#
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# last mod: 2024-03-15
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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source("R_helpers.R")
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#--------------- (1) Read data ---------------
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load("results/haum/eventlogs_pre-corona_cleaned.RData")
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# Select one year to handle number of cases
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dat <- dat[as.Date(dat$date.start) > "2017-12-31" &
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as.Date(dat$date.start) < "2019-01-01", ]
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#--------------- (2) Extract characteristics for cases ---------------
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datcase <- aggregate(cbind(distance, scaleSize, rotationDegree) ~
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case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
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datcase$length <- aggregate(item ~ case, dat, length)$item
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eventtab <- aggregate(event ~ case, dat, table)["case"]
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eventtab$nmove <- aggregate(event ~ case, dat, table)$event[, "move"]
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eventtab$nflipCard <- aggregate(event ~ case, dat, table)$event[, "flipCard"]
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eventtab$nopenTopic <- aggregate(event ~ case, dat, table)$event[, "openTopic"]
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eventtab$nopenPopup <- aggregate(event ~ case, dat, table)$event[, "openPopup"]
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datcase <- datcase |>
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merge(eventtab, by = "case", all = TRUE)
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rm(eventtab)
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datcase$nitems <- aggregate(item ~ case, dat, function(x)
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length(unique(x)), na.action = NULL)$item
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datcase$npaths <- aggregate(path ~ case, dat, function(x)
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length(unique(x)), na.action = NULL)$path
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dat_split <- split(dat, ~ case)
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dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
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dat_minmax <- dplyr::bind_rows(dat_list)
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datcase$min_time <- aggregate(min_time ~ case, dat_minmax, unique)$min_time
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datcase$max_time <- aggregate(max_time ~ case, dat_minmax, unique)$max_time
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datcase$duration <- datcase$max_time - datcase$min_time
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datcase$min_time <- NULL
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datcase$max_time <- NULL
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artworks <- unique(dat$item)[!unique(dat$item) %in% c("501", "502", "503")]
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datcase$infocardOnly <- pbapply::pbsapply(dat_split, check_infocards, artworks = artworks)
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# Clean up NAs
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datcase$distance <- ifelse(is.na(datcase$distance), 0, datcase$distance)
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datcase$scaleSize <- ifelse(is.na(datcase$scaleSize), 1, datcase$scaleSize)
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datcase$rotationDegree <- ifelse(is.na(datcase$rotationDegree), 0, datcase$rotationDegree)
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#--------------- (3) Select features for navigation behavior ---------------
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dattree18 <- data.frame(case = datcase$case,
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PropItems = datcase$nitems / length(unique(dat$item)),
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SearchInfo = (datcase$nopenTopic +
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datcase$nopenPopup) / datcase$length,
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PropMoves = datcase$nmove / datcase$length,
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PathLinearity = datcase$nitems / datcase$npaths,
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Singularity = datcase$npaths / datcase$length
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)
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# centrality <- pbapply::pbsapply(dattree18$case, get_centrality, data = dat)
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# save(centrality, file = "results/haum/tmp_centrality_2018.RData")
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load("results/haum/tmp_centrality_2018.RData")
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dattree18$BetweenCentrality <- centrality
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# Average duration per item
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dat_split <- split(dat[, c("item", "case", "path", "timeMs.start", "timeMs.stop")], ~ path)
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dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
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dat_minmax <- dplyr::bind_rows(dat_list)
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tmp <- aggregate(min_time ~ path, dat_minmax, unique)
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tmp$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
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tmp$duration <- tmp$max_time - tmp$min_time
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tmp$case <- aggregate(case ~ path, dat_minmax, unique)$case
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dattree18$AvDurItem <- aggregate(duration ~ case, tmp, mean)$duration
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rm(tmp)
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# Indicator variable if table was used as info terminal only
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dattree18$InfocardOnly <- factor(datcase$infocardOnly, levels = 0:1,
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labels = c("no", "yes"))
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# Add pattern to datcase; loosely based on Bousbia et al. (2009)
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dattree18$Pattern <- "Dispersion"
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dattree18$Pattern <- ifelse(dattree18$PathLinearity > 0.8, "Scholar",
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dattree18$Pattern)
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dattree18$Pattern <- ifelse(dattree18$PathLinearity <= 0.8 &
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dattree18$BetweenCentrality >= 0.5, "Star",
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dattree18$Pattern)
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dattree18$Pattern <- factor(dattree18$Pattern)
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dattree18$AvDurItemNorm <- normalize(dattree18$AvDurItem)
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#--------------- (4) Clustering ---------------
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df <- dattree18[, c("PropItems", "SearchInfo", "PropMoves", "AvDurItemNorm",
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"Pattern", "InfocardOnly")]
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dist_mat18 <- cluster::daisy(df, metric = "gower")
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coor_3d <- smacof::mds(dist_mat, ndim = 3, type = "ordinal")$conf
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coor_2d <- coor_3d[, 1:2]
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plot(coor_2d)
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rgl::plot3d(coor_3d)
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hc18 <- cluster::agnes(dist_mat, method = "ward")
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k <- 5
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mycols <- c("#91C86E", "#FF6900", "#3CB4DC", "#78004B", "#434F4F")
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cluster18 <- cutree(as.hclust(hc18), k = k)
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table(cluster18)
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plot(coor_2d, col = mycols[cluster18], pch = 16)
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legend("topleft", c("Searching", "Exploring", "Scanning", "Flitting", "Info"),
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col = mycols, bty = "n", pch = 16)
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rgl::plot3d(coor_3d, col = mycols[cluster18])
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print(ftable(xtabs( ~ InfocardOnly + Pattern + cluster18, dattree18)), zero = "-")
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aggregate(. ~ cluster18, df, mean)
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aggregate(. ~ cluster18, dattree18[, -1], mean)
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save(coor_2d, coor_3d, cluster18, dattree18, dist_mat18, hc18,
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file = "../../thesis/figures/data/clustering_cases_2018.RData")
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#--------------- (5) Fit tree ---------------
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c1 <- rpart::rpart(as.factor(cluster18) ~ ., data = dattree18[, c("PropMoves",
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"PropItems",
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"SearchInfo",
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"AvDurItem",
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"Pattern",
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"InfocardOnly")],
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method = "class")
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plot(partykit::as.party(c1), tp_args = list(fill = mycols, col = mycols))
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## Load data
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load("../../thesis/figures/data/clustering_cases.RData")
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c19 <- rpart::rpart(as.factor(cluster) ~ ., data = dattree[, c("PropMoves",
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"PropItems",
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"SearchInfo",
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"AvDurItem",
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"Pattern",
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"InfocardOnly")],
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method = "class")
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cl18 <- rpart:::predict.rpart(c1, type = "class", newdata = dattree18)
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cl18 <- factor(cl18, labels = c("Searching", "Exploring", "Scanning", "Flitting", "Info"))
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cl19 <- rpart:::predict.rpart(c19, type = "class", newdata = dattree18)
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cl19 <- factor(cl19, labels = c("Scanning", "Exploring", "Flitting", "Searching", "Info"))
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xtabs( ~ cl18 + cl19)
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@ -48,13 +48,23 @@ vioplot::vioplot(tr$absolute_frequency)
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# Power law for frequencies of traces
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tab <- table(tr$absolute_frequency)
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x <- as.numeric(tab)
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y <- as.numeric(names(tab))
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x <- as.numeric(names(tab))
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y <- as.numeric(tab)
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plot(x, y, log = "xy")
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p1 <- lm(log(y) ~ log(x))
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pre <- exp(coef(p1)[1]) * x^coef(p1)[2]
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lines(x, pre)
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pdf("results/figures/freq-traces_powerlaw.pdf", height = 3.375,
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width = 3.375, pointsize = 10)
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par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
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plot(x, y, log = "xy", xlab = "Absolute Frequency of Traces",
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ylab = "Frequency", pch = 16, col = rgb(0.262, 0.309, 0.309, 0.5))
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lines(x, pre, col = "#434F4F")
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legend("topright", paste0("Proportion of traces only occurring once: ",
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round(tab[1] / nrow(tr), 2)), cex = .7, bty = "n")
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dev.off()
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# Look at individual traces as examples
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tr[trace_varied == 5 & trace_length > 50, ]
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