Adjusted descriptives to new data set
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@ -1,107 +1,130 @@
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code/")
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library(lattice)
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#library(mtt)
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devtools::load_all("../../../../software/mtt")
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# Achims colors (used by lattice)
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#cc <- palette.colors(palette = "Okabe-Ito")
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#plot(1:10, col = cc, pch = 16, cex = 2)
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# Read data
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datlogs <- read.table("results/haum/event_logfiles_metadata_2023-09-23_01-31-30.csv",
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datlogs <- read.table("results/haum/event_logfiles_2024-01-02_19-44-50.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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datlogs$date <- as.Date(datlogs$date)
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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?
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datlogs$event <- factor(datlogs$event, levels = c("move", "flipCard",
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"openTopic",
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"openPopup"))
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counts_artwork <- table(datlogs$artwork)
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barchart(counts_artwork)
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### Number of log files
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length(unique(datlogs$fileId.start))
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length(unique(datlogs$fileId.stop))
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artworks <- unique(datlogs$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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length(unique(c(datlogs$fileId.start, datlogs$fileId.stop)))
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# 22803
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### Number of activities
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nrow(datlogs)
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table(datlogs$event)
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proportions(table(datlogs$event))
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proportions(table(datlogs$event[datlogs$event != "move"]))
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### Time range
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range(as.Date(datlogs$date.start))
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### Topics per item
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print(xtabs( ~ item + topic, datlogs), zero = "-")
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lattice::dotplot(xtabs( ~ item + topic, datlogs), auto.key = TRUE)
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### Which item gets touched most often?
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counts_item <- table(datlogs$item)
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barchart(counts_item)
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items <- unique(datlogs$item)
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#items <- items[!items %in% c("504", "505")]
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datart <- extract_artworks(items,
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paste0(items, ".xml"),
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"../data/haum/ContentEyevisit/eyevisit_cards_light/")
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datart <- datart[order(datart$artwork), ]
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names(counts_artwork) <- datart$title
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names(counts_item) <- datart$title
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pdf("../figures/counts_artwork.pdf", width = 20, height = 10, pointsize = 10)
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pdf("../figures/counts_item.pdf", width = 20, height = 10, pointsize = 10)
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par(mai = c(5, .6, .1, .1))
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tmp <- barplot(counts_artwork, las = 2, ylim = c(0, 60000),
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border = "white", col = "#0072B2")
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text(tmp, counts_artwork + 1000, c(datart$artwork, "504", "505"))
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tmp <- barplot(counts_item, las = 2, ylim = c(0, 60000),
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border = "white", col = "#3CB4DC")
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text(tmp, counts_item + 1000, datart$artwork)
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dev.off()
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### Which artwork gets touched most often first?
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### Which item 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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counts_case <- table(datcase$item)
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names(counts_case) <- datart$title
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tmp <- barplot(counts_case, las = 2, border = "white")
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text(tmp, counts_case + 100, c(datart$artwork, "504", "505"))
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text(tmp, counts_case + 100, datart$item)
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counts <- rbind(counts_artwork, counts_case)
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counts <- rbind(counts_item, 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 <- aggregate(duration ~ event + item, 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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bwplot(ds ~ as.factor(event) | artwork, datagg)
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xyplot(ds ~ as.factor(event), datagg, groups = artwork)
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bwplot(ds ~ event, datagg)
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bwplot(ds ~ event | item, datagg)
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xyplot(ds ~ event, datagg, groups = item)
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# without aggregation
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bwplot(duration ~ as.factor(event), datlogs)
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bwplot(duration ~ event, datlogs)
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# in min
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set.seed(1027)
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pdf("../figures/duration.pdf", width = 5, height = 5, pointsize = 10)
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bwplot(I(duration/1000/60) ~ as.factor(event), datlogs[sample(nrow(datlogs), 100000), ],
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bwplot(I(duration/1000/60) ~ event, datlogs[sample(nrow(datlogs), 100000), ],
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ylab = "Duration in min")
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dev.off()
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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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### Move events
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datmove <- aggregate(cbind(duration, scaleSize, rotationDegree, x.start,
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y.start, x.stop, y.stop) ~ artwork, datlogs,
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datmove <- aggregate(cbind(duration, scaleSize, rotationDegree, distance, x.start,
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y.start, x.stop, y.stop) ~ item, datlogs,
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mean)
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hist(log(datlogs$scaleSize))
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# --> better interpretable on logscale
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plot(y.start ~ x.start, datmove, pch = 16, col = "gray")
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points(y.start ~ x.start, datmove, col = "#0072B2", cex = log(datmove$scaleSize))
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points(y.start ~ x.start, datmove, col = "#009E73", cex = datmove$rotationDegree)
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points(y.start ~ x.start, datmove, col = "#3CB4DC", cex = datmove$scaleSize)
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plot(y.start ~ x.start, datmove, type = "n", xlab = "x", ylab = "y",
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xlim = c(0, 3840), ylim = c(0, 2160))
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with(datmove, text(x.start, y.start, artwork, col = "gray", cex = 1.5))
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with(datmove, text(x.start, y.start, item, col = "gray", cex = 1.5))
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with(datmove,
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arrows(x.start, y.start, x.stop, y.stop, length = 0.07, lwd = 2)
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)
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abline(v = c(0, 3840), h = c(0, 2160), col = "#0072B2", lwd = 2)
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abline(v = c(0, 3840), h = c(0, 2160), col = "#3CB4DC", lwd = 2)
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datscale <- aggregate(scaleSize ~ artwork, datlogs, max)
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datscale <- aggregate(scaleSize ~ item, datlogs, max)
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plot(y.start ~ x.start, datmove, pch = 16, col = "gray")
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points(y.start ~ x.start, datmove, col = "#0072B2", cex = log(datscale$scaleSize))
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points(y.start ~ x.start, datmove, col = "#3CB4DC", cex = datscale$scaleSize)
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plot(y.start ~ x.start, datmove, type = "n", xlab = "x", ylab = "y",
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xlim = c(0, 3840), ylim = c(0, 2160))
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#with(datmove, text(x.stop, y.stop, artwork))
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with(datmove, text(x.start, y.start, artwork))
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#with(datmove, text(x.stop, y.stop, item))
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with(datmove, text(x.start, y.start, item))
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### Are there certain areas of the table that are touched most often?
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@ -172,6 +195,8 @@ heatmap(tab.stop, Rowv = NA, Colv = NA, col = my_colors(10))
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### How many visitors per day
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datlogs$date <- as.Date(datlogs$date.start)
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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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@ -180,20 +205,21 @@ plot(datint, type = "h")
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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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# Paths per day
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datpath <- aggregate(path ~ date, datlogs, function(x) length(unique(x)))
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plot(datpath, type = "h")
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plot(trace ~ date, dattrace, type = "h", col = "#0072B2")
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plot(path ~ date, datpath, type = "h", col = "#3CB4DC")
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points(case ~ date, datcase, type = "h")
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pdf("../figures/cases_per_day.pdf", width = 9, height = 5, pointsize = 10)
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pdf("../figures/cases_per_day2.pdf", width = 9, height = 5, pointsize = 10)
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par(mai = c(.6, .6, .1, .1), mgp = c(2.4, 1, 0))
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plot(case ~ date, datcase, type = "h", col = "#0072B2")
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abline(v = datcase$date[c(1027, 1039)], col = "#D55E00", lty = 2)
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text(datcase$date[1037]-250, 150, paste0("Corona gap from ",
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datcase$date[1027], " to ",
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datcase$date[1039]), col = "#D55E00")
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plot(case ~ date, datcase, type = "h", col = "#3CB4DC")
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abline(v = datcase$date[datcase$date %in% c("2020-03-13", "2022-10-25")],
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col = "#FF6900", lty = 2)
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text(datcase$date[datcase$date == "2020-03-13"]+470, 80,
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"Corona gap from 2020-03-13 to 2022-10-25",
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col = "#D55E00")
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dev.off()
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@ -209,10 +235,11 @@ foodweb(where = "package:mtt")
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pdf("../figures/fun_depend_mtt.pdf", width = 8, height = 4, pointsize = 10)
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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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"extract_topics"),
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#expand.ybox = 1.8, #cex = .6,
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#border = TRUE,
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#boxcolor = "gray",
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color.lines = FALSE,
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lwd = 2, mai = c(0, 0, 0, 0))
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dev.off()
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@ -222,7 +249,7 @@ dev.off()
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pdf("../figures/timeMs.pdf", width = 9, height = 6, pointsize = 10)
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#par(mai = c(.6, .6, .1, .1), mgp = c(2.4, 1, 0))
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#plot(timeMs.start ~ as.factor(fileId), datlogs[1:2000,], xlab = "fileId")
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bwplot(timeMs.start ~ as.factor(fileId), datlogs[1:2000,], xlab = "",
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bwplot(timeMs.start ~ as.factor(fileId.start), datlogs[1:2000,], xlab = "",
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scales = list(x = list(rot = 90), y = list(rot = 90)))
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dev.off()
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@ -234,18 +261,18 @@ pdf("../figures/xycoord.pdf", width = 5, height = 5, pointsize = 10)
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par(mai = c(.6, .6, .1, .1), mgp = c(2.4, 1, 0))
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#par(mfrow = c(1, 2))
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plot(y.start ~ x.start, datlogs[sample(nrow(datlogs), 10000), ])
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abline(v = c(0, 3840), h = c(0, 2160), col = "#0072B2", lwd = 2)
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abline(v = c(0, 3840), h = c(0, 2160), col = "#3CB4DC", lwd = 2)
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#plot(y.stop ~ x.stop, datlogs)
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#abline(v = c(0, 3840), h = c(0, 2160), col = "#0072B2", lwd = 2)
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#abline(v = c(0, 3840), h = c(0, 2160), col = "#3CB4DC", lwd = 2)
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legend("bottomleft", "Random sample of 10,000", bg = "white")
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legend("topleft", "4K-Display: 3840 x 2160", bg = "white")
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dev.off()
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## moves
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dat001 <- datlogs[which(datlogs$artwork == "001"), ]
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dat001 <- datlogs[which(datlogs$item == "001"), ]
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index <- as.numeric(as.factor(dat001$trace))
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index <- as.numeric(as.factor(dat001$path))
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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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@ -263,8 +290,8 @@ points(y.start ~ x.start, dat001, xlab = "x", ylab = "y",
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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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dat1 <- datlogs[!duplicated(datlogs$item), ]
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dat1 <- dat1[order(dat1$item), ]
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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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