142 lines
3.8 KiB
R
142 lines
3.8 KiB
R
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# 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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# (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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#
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# last mod: 2024-03-08
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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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library(rpart)
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library(partykit)
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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[, -1]
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summary(df)
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# Look at collinearity
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cor_mat <- cor(df)
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diag(cor_mat) <- NA
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heatmap(cor_mat)
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#--------------- (2.2) Hierarchical clustering ---------------
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dist_mat <- cluster::daisy(df, metric = "gower")
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# "Flatten" with MDS
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coor_2d <- as.data.frame(cmdscale(dist_mat, k = 2))
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coor_3d <- as.data.frame(cmdscale(dist_mat, k = 3))
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# TODO: Better use MASS::isoMDS() since I am not using Euclidean distances?
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plot(coor_2d)
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rgl::plot3d(coor_3d)
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method <- c(average = "average", single = "single", complete = "complete",
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ward = "ward")
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method <- "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$ac)
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hc <- hcs$ward
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# Something like a scree plot (??)
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plot(rev(hc$height)[1:100], type = "b", pch = 16, cex = .5)
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k <- 4
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mycols <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")
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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])
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legend("topleft", paste("Cl", 1:4), col = mycols, pch = 21)
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rgl::plot3d(coor_3d, col = mycols[cluster])
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table(dattree[cluster == 1, "Pattern"])
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table(dattree[cluster == 2, "Pattern"])
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table(dattree[cluster == 3, "Pattern"])
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table(dattree[cluster == 4, "Pattern"])
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table(dattree[cluster == 1, "InfocardOnly"])
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table(dattree[cluster == 2, "InfocardOnly"])
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table(dattree[cluster == 3, "InfocardOnly"])
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table(dattree[cluster == 4, "InfocardOnly"])
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aggregate(. ~ cluster, df, mean)
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aggregate(cbind(duration, distance, scaleSize, rotationDegree, length,
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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$date.stop
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alog <- activitylog(tmp[tmp$case == 24016, ],
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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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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, datcase, dattree, coor_2d, coor_3d,
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file = "results/haum/tmp_user-navigation.RData")
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#--------------- (3) Fit tree ---------------
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c1 <- rpart(as.factor(cluster) ~ ., data = dattree[, -1], method = "class")
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plot(as.party(c1))
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# with conditional tree
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c2 <- ctree(as.factor(cluster) ~ ., data = dattree[, -1], alpha = 0)
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plot(c2)
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