170 lines
5.5 KiB
R
170 lines
5.5 KiB
R
# 10_user-navigation.R
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#
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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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#
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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: 2024-03-15
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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#--------------- (1) Load data ---------------
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load("results/haum/dataframes_case_2019.RData")
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#--------------- (2) Clustering ---------------
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df <- dattree[, c("PropItems", "SearchInfo", "PropMoves", "AvDurItemNorm",
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"Pattern", "InfocardOnly")]
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summary(df)
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#dist_mat <- cluster::daisy(df, metric = "euclidean")
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dist_mat <- cluster::daisy(df, metric = "gower")
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# "Flatten" with MDS
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# coor_3d <- as.data.frame(cmdscale(dist_mat, k = 3))
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# coor_3d <- prcomp(df)$x[, 1:3]
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coor_3d <- smacof::mds(dist_mat, ndim = 3, type = "ordinal")$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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# pm <- cluster::pam(dist_mat, k = k)
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# cluster <- pm$clustering
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# --> Does not look as good as the hierarchical clustring
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method <- c(average = "average", single = "single", complete = "complete",
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ward = "ward")
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hcs <- pbapply::pblapply(method, function(x) cluster::agnes(dist_mat, method = x))
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acs <- pbapply::pbsapply(hcs, function(x) x$ac)
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# average single complete ward
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# 0.9881224 0.9725661 0.9937669 0.9994267
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hc <- hcs$ward
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#hc <- cluster::agnes(dist_mat, method = "ward")
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k <- 5
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mycols <- c("#3CB4DC", "#FF6900", "#78004B", "#91C86E", "#434F4F")
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cluster <- cutree(as.hclust(hc), k = k)
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table(cluster)
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plot(coor_2d, col = mycols[cluster], pch = 16)
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#legend("topleft", paste("Cl", 1:5), col = mycols, pch = 21)
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legend("topleft", c("Scanning", "Exploring", "Flitting", "Searching", "Info"),
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col = mycols, bty = "n", pch = 16)
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rgl::plot3d(coor_3d, col = mycols[cluster])
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print(ftable(xtabs( ~ InfocardOnly + Pattern + cluster, dattree)), zero = "-")
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aggregate(. ~ cluster, df, mean)
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aggregate(cbind(duration, distance, scaleSize, rotationDegree, length, nitems,
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nmove, nflipCard, nopenTopic, nopenPopup) ~ cluster, datcase,
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mean)
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### Look at selected cases ###########################################
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tmp <- dat
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tmp$start <- tmp$date.start
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tmp$complete <- tmp$date.stop
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# Examples:
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## Scholar: 29679
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## Star: 24456
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## Dispersion: 26000
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## only info cards: 24299
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## not only info cards: 24013
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#head(dattree[dattree$Pattern == "Dispersion" & datcase$nitems == 10, ])
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#head(dattree[dattree$InfocardOnly == "yes" & datcase$nitems == 3, ])
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alog <- bupaR::activitylog(tmp[tmp$case == 24013, ],
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case_id = "case",
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activity_id = "item",
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resource_id = "path",
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timestamps = c("start", "complete"))
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processmapR::process_map(alog)
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rm(tmp)
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######################################################################
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res <- merge(dat, data.frame(case = dattree$case, cluster),
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by = "case", all.x = TRUE)
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res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
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xtabs( ~ item + cluster, res)
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aggregate(event ~ cluster, res, table)
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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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aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, mean)
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aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, median)
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write.table(res,
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file = "results/haum/eventlogs_2019_case-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(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, dattree,
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file = "../../thesis/figures/data/clustering_cases.RData")
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#--------------- (3) Fit tree ---------------
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c1 <- 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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plot(partykit::as.party(c1), tp_args = list(fill = mycols, col = mycols))
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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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"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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alpha = 0.001)
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plot(c2, tp_args = list(fill = mycols, col = mycols))
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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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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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