169 lines
5.8 KiB
R
169 lines
5.8 KiB
R
# 07_item-clustering.R
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#
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# content: (1) Read data
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# (1.1) Read log event data
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# (1.2) Read infos for PM for infos
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# (1.3) Extract additional infos for clustering
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# (2) Clustering
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# (3) Visualization with pictures
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#
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# input: results/eventlogs_pre-corona_cleaned.RData
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# results/pn_infos_items.csv
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# output: results/eventlogs_pre-corona_item-clusters.csv
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# ../thesis/results/clustering_items.RData"
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#
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# last mod: 2024-03-22
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/")
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source("code/R_helpers.R")
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#--------------- (1) Read data ---------------
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#--------------- (1.1) Read log event data ---------------
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load("results/eventlogs_pre-corona_cleaned.RData")
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#--------------- (1.2) Read infos for PM for items ---------------
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datitem <- read.table("results/pn_infos_items.csv", header = TRUE,
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sep = ";", row.names = 1)
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#--------------- (1.3) Extract additional infos for clustering ---------------
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# Get average duration per path
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dat_split <- split(dat, ~ 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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datpath <- aggregate(duration ~ item + path, dat, mean, na.action = NULL)
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datpath$min_time <- aggregate(min_time ~ path, dat_minmax, unique, na.action = NULL)$min_time
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datpath$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
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datpath$duration <- datpath$max_time - datpath$min_time
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datitem$duration <- aggregate(duration ~ item, datpath, mean)$duration
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datitem$distance <- aggregate(distance ~ item, dat, mean)$distance
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datitem$scaleSize <- aggregate(scaleSize ~ item, dat, mean)$scaleSize
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datitem$rotationDegree <- aggregate(rotationDegree ~ item, dat, mean)$rotationDegree
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datitem$npaths <- aggregate(path ~ item, dat, function(x) length(unique(x)))$path
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datitem$ncases <- aggregate(case ~ item, dat, function(x) length(unique(x)))$case
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datitem$nmove <- aggregate(event ~ item, dat, table)$event[,"move"]
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datitem$nflipCard <- aggregate(event ~ item, dat, table)$event[,"flipCard"]
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datitem$nopenTopic <- aggregate(event ~ item, dat, table)$event[,"openTopic"]
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datitem$nopenPopup <- aggregate(event ~ item, dat, table)$event[,"openPopup"]
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rm(datpath)
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#--------------- (2) Clustering ---------------
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df <- datitem[, c("precision", "generalizability", "nvariants", "duration",
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"distance", "scaleSize", "rotationDegree", "npaths",
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"ncases", "nmove", "nflipCard", "nopenTopic",
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"nopenPopup")] |>
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scale()
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dist_mat <- dist(df)
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heatmap(as.matrix(dist_mat))
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# Choosing best linkage method
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method <- c(average = "average", single = "single", complete = "complete",
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ward = "ward")
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hcs <- lapply(method, function(x) cluster::agnes(dist_mat, method = x))
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acs <- sapply(hcs, function(x) x$ac)
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# Dendograms
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par(mfrow=c(4,2))
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for (hc in hcs) plot(hc, main = "")
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hc <- hcs$ward
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factoextra::fviz_nbclust(df, FUNcluster = factoextra::hcut, method = "wss")
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factoextra::fviz_nbclust(df, FUNcluster = factoextra::hcut, method = "silhouette")
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gap_stat <- cluster::clusGap(df, FUNcluster = factoextra::hcut,
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hc_func = "agnes", hc_method = "ward",
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K.max = 15)
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factoextra::fviz_gap_stat(gap_stat)
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k <- 6 # number of clusters
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mycols <- c("#434F4F", "#78004B", "#FF6900", "#3CB4DC", "#91C86E", "Black")
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cluster <- cutree(hc, k = k)
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factoextra::fviz_dend(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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rect = TRUE,
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main = "",
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ylab = ""
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#ggtheme = ggplot2::theme_bw()
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)
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factoextra::fviz_cluster(list(data = df, cluster = cluster),
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palette = mycols,
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ellipse.type = "convex",
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repel = TRUE,
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show.clust.cent = FALSE,
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main = "",
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ggtheme = ggplot2::theme_bw())
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aggregate(cbind(precision, generalizability, nvariants, duration, distance,
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scaleSize , rotationDegree, npaths, ncases, nmove,
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nflipCard, nopenTopic, nopenPopup) ~ cluster, datitem,
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mean)
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aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
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ncases, nmove, nflipCard, nopenTopic, nopenPopup) ~ cluster,
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datitem, max)
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item <- sprintf("%03d", as.numeric(gsub("item_([0-9]{3})", "\\1",
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row.names(datitem))))
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res <- merge(dat, data.frame(item, cluster), by = "item", all.x = TRUE)
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res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
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# DFGs for clusters
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res$start <- res$date.start
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res$complete <- res$date.stop
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for (clst in sort(unique(res$cluster))) {
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alog <- bupaR::activitylog(res[res$cluster == clst, ],
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case_id = "path",
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activity_id = "event",
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resource_id = "item",
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timestamps = c("start", "complete"))
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processmapR::process_map(alog,
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type_nodes = processmapR::frequency("relative", color_scale = "Greys"),
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sec_nodes = processmapR::frequency("absolute"),
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type_edges = processmapR::frequency("relative", color_edges = "#FF6900"),
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sec_edges = processmapR::frequency("absolute"),
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rankdir = "LR")
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}
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# Look at clusters
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par(mfrow = c(2,2))
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vioplot::vioplot(duration ~ cluster, res)
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vioplot::vioplot(distance ~ cluster, res)
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vioplot::vioplot(scaleSize ~ cluster, res)
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vioplot::vioplot(rotationDegree ~ cluster, res)
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write.table(res,
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file = "results/eventlogs_pre-corona_item-clusters.csv",
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sep = ";",
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quote = FALSE,
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row.names = FALSE)
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# Save data for plots and tables
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save(hc, k, res, dist_mat, datitem, df,
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file = "../thesis/results/clustering_items.RData")
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