159 lines
5.5 KiB
R
159 lines
5.5 KiB
R
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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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_2024-01-18_09-58-52.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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# 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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datitem$duration <- aggregate(duration ~ item, dat, mean)$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$ntopics <- aggregate(topic ~ item, dat, function(x) length(unique(x)))$topic
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datitem$mostfreq_num <- as.numeric(gsub(".*: (.*)}", "\\1", datitem$mostfreq))
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#--------------- (2) Clustering ---------------
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df <- datitem[, c("precision", "generalizability", "nvariants",
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"mostfreq_num", "duration", "distance", "scaleSize",
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"rotationDegree", "npaths", "ncases", "ntopics")] |>
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scale()
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mat <- dist(df)
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hc <- hclust(mat, method = "ward.D2")
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library(factoextra)
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fviz_dend(hc, cex = 0.5)
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datitem$grp <- cutree(hc, k = 6)
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fviz_dend(hc, k = 6,
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cex = 0.5,
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k_colors = c("#78004B", "#000000", "#3CB4DC", "#91C86E",
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"#FF6900", "#434F4F"),
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#type = "phylogenic",
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rect = TRUE
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)
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p <- fviz_cluster(list(data = df, cluster = grp),
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palette = c("#78004B", "#000000", "#3CB4DC", "#91C86E",
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"#FF6900", "#434F4F"),
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ellipse.type = "convex",
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repel = TRUE,
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show.clust.cent = FALSE, ggtheme = theme_bw())
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p
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aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
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ncases, ntopics) ~ grp, datitem, mean)
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datitem$item <- 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$timeMs.start), ]
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write.table(res,
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file = "results/haum/event_logfiles_pre-corona_with-clusters.csv",
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sep = ";",
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quote = FALSE,
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row.names = FALSE)
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library(bupaverse)
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res$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"),
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sec_nodes = frequency("absolute"),
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type_edges = frequency("relative"),
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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", "#000000", "#3CB4DC", "#91C86E", "#FF6900",
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"#434F4F")
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#pdf("results/haum/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 10)
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png("results/haum/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 10, res = 300)
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par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
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plot(y ~ x, p$data, type = "n", ylim = c(-3.2, 3), xlim = c(-4.7, 6.4))
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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 - .4,
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xright = x + .4,
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ybottom = y - .2,
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ytop = y + .2)
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}
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dev.off()
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