262 lines
8.4 KiB
R
262 lines
8.4 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/haum/event_logfiles_2024-02-21_16-07-33.csv
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# results/haum/pn_infos_items.csv
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# output: results/haum/event_logfiles_pre-corona_with-clusters.csv
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#
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# last mod: 2024-02-23
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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library(bupaverse)
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library(factoextra)
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#--------------- (1) Read data ---------------
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#--------------- (1.1) Read log event data ---------------
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dat0 <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
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colClasses = c("character", "character", "POSIXct",
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"POSIXct", "character", "integer",
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"numeric", "character", "character",
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rep("numeric", 3), "character",
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"character", rep("numeric", 11),
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"character", "character"),
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sep = ";", header = TRUE)
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dat0$event <- factor(dat0$event, levels = c("move", "flipCard", "openTopic",
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"openPopup"))
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# TODO: Maybe look at this with complete data?
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# Select data pre Corona
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dat <- dat0[as.Date(dat0$date.start) < "2020-03-13", ]
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dat <- dat[dat$path != 106098, ]
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#--------------- (1.2) Read infos for PM for items ---------------
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datitem <- read.table("results/haum/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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dat_split <- split(dat, ~ path)
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time_minmax <- function(subdata) {
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subdata$min_time <- min(subdata$timeMs.start)
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if (all(is.na(subdata$timeMs.stop))) {
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subdata$max_time <- NA
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} else {
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subdata$max_time <- max(subdata$timeMs.stop, na.rm = TRUE)
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}
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subdata
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}
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dat_list <- pbapply::pblapply(dat_split, time_minmax)
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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$duration_path <- datpath$max_time - datpath$min_time
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# average duration per path
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datitem$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)))$case
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datitem$nmoves <- 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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#--------------- (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", "nmoves", "nopenTopic", "nopenPopup")] |>
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scale()
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mat <- dist(df)
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heatmap(as.matrix(mat))
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# Choosing best linkage method
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h1 <- hclust(mat, method = "average")
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h2 <- hclust(mat, method = "complete")
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h3 <- hclust(mat, method = "ward.D")
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h4 <- hclust(mat, method = "ward.D2")
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h5 <- hclust(mat, method = "single")
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# Cophenetic Distances, for each linkage
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c1 <- cophenetic(h1)
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c2 <- cophenetic(h2)
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c3 <- cophenetic(h3)
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c4 <- cophenetic(h4)
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c5 <- cophenetic(h5)
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# Correlations
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cor(mat, c1)
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cor(mat, c2)
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cor(mat, c3)
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cor(mat, c4)
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cor(mat, c5)
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# https://en.wikipedia.org/wiki/Cophenetic_correlation
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# https://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
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# Dendograms
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par(mfrow=c(3,2))
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plot(h1, main = "Average Linkage")
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plot(h2, main = "Complete Linkage")
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plot(h3, main = "Ward Linkage")
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plot(h4, main = "Ward 2 Linkage")
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plot(h5, main = "Single Linkage")
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hc <- h1
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# Note that ‘agnes(*, method="ward")’ corresponds to ‘hclust(*, "ward.D2")’
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k <- 4 # number of clusters
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grp <- cutree(hc, k = k)
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datitem$grp <- grp
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fviz_dend(hc, k = k,
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cex = 0.5,
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k_colors = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
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"#000000", "gold", "#434F4F"),
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#type = "phylogenic",
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rect = TRUE
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)
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plot(hc)
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rect.hclust(hc, k=8, border="red")
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rect.hclust(hc, k=7, border="blue")
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rect.hclust(hc, k=6, border="green")
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p <- fviz_cluster(list(data = df, cluster = grp),
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palette = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
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"#000000", "#434F4F", "gold"),
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ellipse.type = "convex",
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repel = TRUE,
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show.clust.cent = FALSE, ggtheme = theme_bw())
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p
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aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
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ncases, nmoves, nflipCard, nopenTopic, nopenPopup) ~ grp,
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datitem, median)
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# Something like a scree plot (??)
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plot(rev(seq_along(hc$height)), hc$height, type = "l")
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points(rev(seq_along(hc$height)), hc$height, pch = 16, cex = .5)
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datitem$item <- sprintf("%03d",
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as.numeric(gsub("item_([0-9]{3})", "\\1", row.names(datitem))))
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res <- merge(dat, datitem[, c("item", "grp")], by = "item", all.x = TRUE)
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res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
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# Look at clusters
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par(mfrow = c(2,2))
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vioplot::vioplot(duration ~ grp, res)
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vioplot::vioplot(distance ~ grp, res)
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vioplot::vioplot(scaleSize ~ grp, res)
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vioplot::vioplot(rotationDegree ~ grp, res)
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write.table(res,
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file = "results/haum/event_logfiles_pre-corona_with-clusters.csv",
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sep = ";",
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quote = FALSE,
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row.names = FALSE)
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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 (cluster in sort(unique(res$grp))) {
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alog <- activitylog(res[res$grp == cluster, ],
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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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dfg <- process_map(alog,
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type_nodes = frequency("relative", color_scale = "Greys"),
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sec_nodes = frequency("absolute"),
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type_edges = frequency("relative", color_edges = "#FF6900"),
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sec_edges = frequency("absolute"),
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rankdir = "LR",
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render = FALSE)
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export_map(dfg,
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file_name = paste0("results/processmaps/dfg_cluster", cluster, "_R.pdf"),
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file_type = "pdf",
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title = paste("DFG Cluster", cluster))
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}
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#--------------- (3) Visualization with pictures ---------------
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library(png)
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library(jpeg)
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library(grid)
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colors <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")
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pdf("results/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 10)
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#png("results/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 10, res = 300)
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par(mai = c(.4,.4,.1,.1), mgp = c(2.4, 1, 0))
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plot(y ~ x, p$data, type = "n", ylim = c(-3.5, 2.8), xlim = c(-5, 10),
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xlab = "", ylab = "")
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for (item in sprintf("%03d", as.numeric(rownames(p$data)))) {
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if (item == "125") {
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pic <- readJPEG(paste0("../data/haum/ContentEyevisit/eyevisit_cards_light/",
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item, "/", item, ".jpg"))
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} else {
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pic <- readPNG(paste0("../data/haum/ContentEyevisit/eyevisit_cards_light/",
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item, "/", item, ".png"))
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}
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img <- as.raster(pic[,,1:3])
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x <- p$data$x[sprintf("%03d", as.numeric(rownames(p$data))) == item]
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y <- p$data$y[sprintf("%03d", as.numeric(rownames(p$data))) == item]
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points(x, y,
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col = colors[p$data$cluster[sprintf("%03d", as.numeric(rownames(p$data))) == item]],
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cex = 9,
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pch = 15)
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rasterImage(img,
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xleft = x - .45,
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xright = x + .45,
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ybottom = y - .2,
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ytop = y + .2)
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}
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legend("topright", paste("Cluster", 1:k), col = colors, pch = 15, bty = "n")
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dev.off()
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