201 lines
5.7 KiB
R
201 lines
5.7 KiB
R
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code/")
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library(lattice)
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# Read data
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datlogs <- read.table("../data/haum/event_logfiles_metadata_2023-09-23_01-31-30.csv",
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sep = ";", header = TRUE)
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datlogs$date <- as.Date(datlogs$date.start)
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datlogs$date.start <- as.POSIXct(datlogs$date.start)
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datlogs$date.stop <- as.POSIXct(datlogs$date.stop)
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datlogs$artwork <- sprintf("%03d", datlogs$artwork)
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### Which artwork gets touched most often/first?
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counts_artwork <- table(datlogs$artwork)
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barchart(counts_artwork)
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artworks <- unique(datlogs$artwork)
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artworks <- artworks[!artworks %in% c("504", "505")]
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datart <- extract_artworks(artworks,
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paste0(artworks, ".xml"),
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"../data/haum/ContentEyevisit/eyevisit_cards_light/")
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datart <- datart[order(datart$artwork), ]
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names(counts_artwork) <- datart$title
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pdf("../figures/counts_artwork.pdf", width = 20, height = 10, pointsize = 10)
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par(mai = c(5, .6, .1, .1))
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mtp <- barplot(counts_artwork, las = 2, ylim = c(0, 60000), border = "white")
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text(tmp, counts_artwork + 1000, c(datart$artwork, "504", "505"))
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dev.off()
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### Which artwork gets touched most often first?
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datcase <- datlogs[!duplicated(datlogs$case), ]
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counts_case <- table(datcase$artwork)
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names(counts_case) <- datart$title
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tmp <- barplot(counts_case, las = 2, border = "white")
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text(tmp, counts_case + 100, c(datart$artwork, "504", "505"))
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counts <- rbind(counts_artwork, counts_case)
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barplot(counts, las = 2, border = "white", col = c("gray", "darkorange"))
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### Which teasers seem to work well?
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barplot(table(datlogs$topic), las = 2)
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barplot(table(datlogs$topicFile), las = 2)
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### Dwell times/duration
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datagg <- aggregate(duration ~ event + artwork, datlogs, mean)
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datagg$ds <- datagg$duration / 1000
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bwplot(ds ~ as.factor(event), datagg)
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xyplot(ds ~ as.factor(event), datagg, groups = artwork)
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# without aggregation
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bwplot(duration ~ as.factor(event), datlogs)
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# in min
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bwplot(I(duration/1000/60) ~ as.factor(event), datlogs)
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datlogs$daydiff <- c(NA, diff(datlogs$date))
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plot(daydiff ~ date, datlogs, type = "b")
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### Are there certain areas of the table that are touched most often?
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# heatmap
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cuts <- 100
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datlogs$x.start.cat <- cut(datlogs$x.start, cuts)
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datlogs$y.start.cat <- cut(datlogs$y.start, cuts)
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tab <- xtabs( ~ x.start.cat + y.start.cat, datlogs)
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colnames(tab) <- paste0("c", 1:cuts)
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rownames(tab) <- paste0("c", 1:cuts)
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heatmap(tab, Rowv = NA, Colv = NA)
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library(ggplot2)
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ggplot(as.data.frame(tab)) +
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geom_tile(aes(x = x.start.cat, y = y.start.cat, fill = Freq)) +
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scale_fill_gradient(low = "gray40", high = "orange")
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dattrim <- datlogs[datlogs$x.start < 3840 &
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datlogs$x.start > 0 &
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datlogs$y.start < 2160 &
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datlogs$y.start > 0 &
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datlogs$x.stop < 3840 &
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datlogs$x.stop > 0 &
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datlogs$y.stop < 2160 &
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datlogs$y.stop > 0, ]
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cuts <- 200 # 100, 70, ...
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# start
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dattrim$x.start.cat <- cut(dattrim$x.start, cuts)
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dattrim$y.start.cat <- cut(dattrim$y.start, cuts)
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tab.start <- xtabs( ~ x.start.cat + y.start.cat, dattrim)
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colnames(tab.start) <- paste0("c", 1:cuts)
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rownames(tab.start) <- paste0("c", 1:cuts)
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heatmap(tab.start, Rowv = NA, Colv = NA)
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my_colors <- colorRampPalette(c("gray40", "orange"))
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heatmap(tab.start, Rowv = NA, Colv = NA, col = my_colors(1000))
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ggplot(as.data.frame(tab.start)) +
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geom_tile(aes(x = x.start.cat, y = y.start.cat, fill = Freq)) +
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scale_fill_gradient(low = "gray40", high = "orange")
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# stop
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dattrim$x.stop.cat <- cut(dattrim$x.stop, cuts)
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dattrim$y.stop.cat <- cut(dattrim$y.stop, cuts)
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tab.stop <- xtabs( ~ x.stop.cat + y.stop.cat, dattrim)
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colnames(tab.stop) <- paste0("c", 1:cuts)
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rownames(tab.stop) <- paste0("c", 1:cuts)
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heatmap(tab.stop, Rowv = NA, Colv = NA)
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heatmap(tab.stop, Rowv = NA, Colv = NA, col = my_colors(1000))
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### How many visitors per day
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# Interactions per day
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datint <- aggregate(case ~ date, datlogs, length)
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plot(datint, type = "h")
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# Cases per day
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datcase <- aggregate(case ~ date, datlogs, function(x) length(unique(x)))
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plot(datcase, type = "h")
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# Traces per day
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dattrace <- aggregate(trace ~ date, datlogs, function(x) length(unique(x)))
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plot(dattrace, type = "h")
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# function dependencies of mtt
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devtools::load_all("../../../../software/mtt")
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#library(mtt)
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library(mvbutils)
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foodweb(where = "package:mtt")
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foodweb(where = "package:mtt",
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prune = c("parse_logfiles", "create_eventlogs", "extract_artworks",
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"extract_topics", "add_topic"),
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expand.ybox = 1.8, #cex = .6,
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boxcolor = "gray", lwd = 2)
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### Other stuff
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dat001 <- datlogs[which(datlogs$artwork == "001"), ]
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index <- as.numeric(as.factor(dat001$trace))
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cc <- sample(colors(), 100)
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plot(y.start ~ x.start, dat001, type = "n", xlab = "x", ylab = "y",
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xlim = c(0, 3840), ylim = c(0, 2160))
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with(dat001[1:200,], arrows(x.start, y.start, x.stop, y.stop,
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length = .07, col = cc[index]))
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plot(y.start ~ x.start, dat001, xlab = "x", ylab = "y",
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xlim = c(0, 3840), ylim = c(0, 2160), pch = 16, col = "gray")
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points(y.start ~ x.start, dat001, xlab = "x", ylab = "y",
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xlim = c(0, 3840), ylim = c(0, 2160), cex = dat001$scaleSize,
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col = "blue")
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cc <- sample(colors(), 70)
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dat1 <- datlogs[!duplicated(datlogs$artwork), ]
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dat1 <- dat1[order(dat1$artwork), ]
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plot(y.start ~ x.start, dat1, type = "n", xlim = c(-100, 4500), ylim = c(-100, 2500))
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abline(h = c(0, 2160), v = c(0, 3840), col = "lightgray")
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with(dat1, points(x.start, y.start, col = cc, pch = 16))
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with(dat1, points(x.stop, y.stop, col = cc, pch = 16))
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with(dat1, arrows(x.start, y.start, x.stop, y.stop, length = .07, col = cc))
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