760 lines
25 KiB
R
760 lines
25 KiB
R
# 02_descriptives.R
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#
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# content: (1) Read data
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# (2) Descriptives
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# (3) Process Mining
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# (3.1) Check data quality
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# (3.2) Interactions for different artworks
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# (3.3) Patterns of cases
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# (3.4) Artwork sequences
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# (3.5) Topics
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#
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# results/haum/raw_logfiles_2024-02-21_16-07-33.csv
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# output:
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#
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# last mod: 2024-03-13
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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library(lattice)
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library(bupaverse)
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#library(mtt)
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devtools::load_all("../../../../../software/mtt")
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# Overall Research Question: How do museum visitors interact with the
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# artworks presented on the MTT?
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# Distribution of bursts
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# Can this be visualized in a nice way?
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#--------------- (1) Read data ---------------
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datlogs <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.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$event <- factor(datlogs$event, levels = c("move", "flipCard",
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"openTopic",
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"openPopup"))
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datraw <- read.table("results/haum/raw_logfiles_2024-02-21_16-07-33.csv",
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sep = ";", header = TRUE)
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# Add weekdays to data frame
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datlogs$weekdays <- factor(weekdays(datlogs$date.start),
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levels = c("Montag", "Dienstag", "Mittwoch",
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"Donnerstag", "Freitag", "Samstag",
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"Sonntag"),
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labels = c("Monday", "Tuesday", "Wednesday",
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"Thursday", "Friday", "Saturday",
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"Sunday"))
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### Number of log files
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length(unique(datraw$fileId))
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# 39767
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length(unique(c(datlogs$fileId.start, datlogs$fileId.stop)))
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# 22789
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### Number of activities
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nrow(datlogs)
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table(datlogs$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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mat <- t(as.matrix(xtabs( ~ item + topic, datlogs)))
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mat[mat == 0] <- NA
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image(mat, axes = F, col = rainbow(100))
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#--------------- (2) Descriptives ---------------
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### Which item gets touched most often?
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counts_item <- table(datlogs$item)
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lattice::barchart(counts_item)
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items <- unique(datlogs$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$artwork), ]
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names(counts_item) <- datart$title
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pdf("results/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_item, las = 2, ylim = c(0, 60000),
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border = NA, col = "#434F4F")
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text(tmp, counts_item + 1000, datart$artwork)
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dev.off()
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### Which item gets touched most often first?
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datcase <- datlogs[!duplicated(datlogs$case), ]
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counts_case <- table(datcase$item)
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names(counts_case) <- datart$title
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tmp <- barplot(counts_case, las = 2, border = "white")
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text(tmp, counts_case + 100, datart$item)
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counts <- rbind(counts_item, counts_case)
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pdf("results/figures/counts_item_firsttouch.pdf",
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width = 20, height = 10, pointsize = 10)
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par(mai = c(5, .6, .1, .1))
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tmp <- barplot(counts, las = 2, border = NA, col = c("#434F4F", "#FF6900"), ylim = c(0, 65000))
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text(tmp, counts_item + counts_case + 1000, datart$artwork)
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legend("topleft", c("Total interactions", "First interactions"),
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col = c("#434F4F", "#FF6900"), pch = 15, bty = "n")
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dev.off()
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### Which teasers seem to work well?
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barplot(table(datlogs$topic), las = 2)
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### Dwell times/duration
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datagg <- aggregate(duration ~ event + item, datlogs, mean)
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datagg$ds <- datagg$duration / 1000 # in secs
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lattice::bwplot(ds ~ event, datagg)
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# without aggregation
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lattice::bwplot(duration / 1000 / 60 ~ event, datlogs)
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# in min
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set.seed(1027)
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pdf("results/figures/duration.pdf", width = 5, height = 5, pointsize = 10)
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lattice::bwplot(I(duration/1000/60) ~ event, datlogs[sample(nrow(datlogs), 100000), ],
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ylab = "Duration in min")
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dev.off()
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### Move events
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datmove <- aggregate(cbind(duration, scaleSize, rotationDegree, distance, x.start,
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y.start, x.stop, y.stop) ~ item, datlogs,
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mean)
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hist(log(datlogs$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 = "#3CB4DC", cex = datmove$scaleSize)
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plot(y.start ~ x.start, datmove, type = "n", xlab = "x", ylab = "y",
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xlim = c(0, 3840), ylim = c(0, 2160))
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with(datmove, text(x.start, y.start, item, col = "gray", cex = 1.5))
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with(datmove,
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arrows(x.start, y.start, x.stop, y.stop, length = 0.07, lwd = 2)
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)
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abline(v = c(0, 3840), h = c(0, 2160), col = "#3CB4DC", lwd = 2)
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datscale <- aggregate(scaleSize ~ item, datlogs, max)
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plot(y.start ~ x.start, datmove, pch = 16, col = "gray")
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points(y.start ~ x.start, datmove, col = "#3CB4DC", cex = datscale$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, 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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# 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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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 <- 100 # 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) <- NULL
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rownames(tab.start) <- NULL
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pdf("results/figures/heatmap_start.pdf", width = 5, height = 5, pointsize = 10)
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heatmap(tab.start, Rowv = NA, Colv = NA)
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dev.off()
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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) <- NULL
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rownames(tab.stop) <- NULL
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pdf("results/figures/heatmap_stop.pdf", width = 5, height = 5, pointsize = 10)
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heatmap(tab.stop, Rowv = NA, Colv = NA)
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dev.off()
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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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# 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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# 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(path ~ date, datpath, type = "h", col = "#3CB4DC")
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points(case ~ date, datcase, type = "h")
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pdf("results/figures/cases_per_day.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 = "#434F4F")
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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 = "#FF6900")
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dev.off()
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### Other stuff
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library(mvbutils)
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foodweb(where = "package:mtt")
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pdf("results/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"),
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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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## weird behavior of timeMs
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pdf("results/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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lattice::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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## x,y-coordinates out of range
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set.seed(1522)
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pdf("results/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 = "#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 = "#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$item == "001"), ]
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index <- as.numeric(as.factor(dat001$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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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$scaleSize,
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col = "blue")
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cc <- sample(colors(), 70)
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dat1 <- datlogs[!duplicated(datlogs$item), ]
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dat1 <- dat1[order(dat1$item), ]
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plot(y.start ~ x.start, dat1, type = "n", xlim = c(-100, 4500), ylim = c(-100, 2500))
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abline(h = c(0, 2160), v = c(0, 3840), col = "lightgray")
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with(dat1, points(x.start, y.start, col = cc, pch = 16))
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with(dat1, points(x.stop, y.stop, col = cc, pch = 16))
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with(dat1, arrows(x.start, y.start, x.stop, y.stop, length = .07, col = cc))
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# How many events per topic, per path, ...
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# How many popups per artwork?
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# Number of events per artwork
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tab <- xtabs( ~ item + event, datlogs)
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addmargins(tab)
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proportions(tab, margin = "item")
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proportions(tab, margin = "event")
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cc <- palette.colors(palette = "Okabe-Ito")[c(3,2,4,8)]
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pdf("results/figures/event-dist.pdf", height = 3.375, width = 12, pointsize = 10)
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par(mai = c(.4,.4,.1,.1), mgp = c(2.4, 1, 0))
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barplot(t(proportions(tab, margin = "item")), las = 2, col = c("#78004B", "#3CB4DC", "#91C86E", "#FF6900"),
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legend.text = levels(datlogs$event), args.legend = list(x = "bottomleft", bg = "white"))
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dev.off()
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#barchart(proportions(tab, margin = "item"), las = 2)
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# Proportion of events
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proportions(xtabs( ~ event, datlogs))
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# Mean proportion of event per path
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colMeans(proportions(xtabs( ~ path + event, datlogs), margin = "path"))
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# Mean proportion of event per item
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colMeans(proportions(tab, margin = "item"))
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# Proportion of unclosed events
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nrow(datlogs[is.na(datlogs$complete), ])
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nrow(datlogs[is.na(datlogs$complete), ]) / nrow(datlogs)
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# Proportion of events spanning more than one log file
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sum(datlogs$fileId.start != datlogs$fileId.stop, na.rm = TRUE)
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sum(datlogs$fileId.start != datlogs$fileId.stop, na.rm = TRUE) / nrow(datlogs)
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#--------------- (3) Process Mining ---------------
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#--------------- (3.1) Check data quality ---------------
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datlogs$start <- datlogs$date.start
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datlogs$complete <- datlogs$date.stop
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alog <- activitylog(datlogs,
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case_id = "path",
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activity_id = "event",
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#resource_id = "case",
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resource_id = "item",
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timestamps = c("start", "complete"))
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process_map(alog, frequency("relative"))
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alogf <- edeaR::filter_trace_frequency(alog, percentage = 0.9)
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processmapR::process_map(alogf, # alog,
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type_nodes = processmapR::frequency("absolute"),
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sec_nodes = processmapR::frequency("relative"),
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type_edges = processmapR::frequency("absolute"),
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sec_edges = processmapR::frequency("relative"),
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rankdir = "TB")
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alog_no_move <- alog[alog$event != "move", ]
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pdf("../figures/traceexplore_trace-event.pdf", height = 8, width = 12, pointsize = 10)
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set.seed(1447)
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processmapR::trace_explorer(alog_no_move[alog_no_move$path %in%
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sample(unique(alog_no_move$path), 400),],
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coverage = 1, type = "frequent",
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abbreviate = T)
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dev.off()
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pdf("../figures/ra_trace-event.pdf", height = 8, width = 12, pointsize = 10)
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ra_no_move <- edeaR::resource_frequency(alog_no_move, "resource-activity")
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levels(ra_no_move$event) <- c("flipCard", "flipCard", "openTopic", "openPopup")
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plot(ra_no_move)
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dev.off()
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ra <- edeaR::resource_frequency(alog, "resource-activity")
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plot(ra)
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heatmap(xtabs(relative_activity ~ artwork + event, ra))
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heatmap(xtabs(relative_resource ~ artwork + event, ra_no_move))
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heatmap(xtabs(relative_activity ~ artwork + event, ra_no_move))
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aggregate(relative_activity ~ event, ra, sum)
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aggregate(relative_resource ~ artwork, ra, sum)
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#--------------- (3.2) Interactions for different artworks ---------------
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# Do interaction patterns for events per trace look different for different
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# artworks?
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which.max(table(datlogs$artwork))
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which.min(table(datlogs$artwork))
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which.min(table(datlogs$artwork)[-c(71,72)])
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alog080 <- activitylog(datlogs[datlogs$artwork == "080",],
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case_id = "path",
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activity_id = "event",
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resource_id = "artwork",
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timestamps = c("start", "complete"))
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process_map(alog80, frequency("relative"))
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alog087 <- activitylog(datlogs[datlogs$artwork == "087",],
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case_id = "path",
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activity_id = "event",
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resource_id = "artwork",
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timestamps = c("start", "complete"))
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process_map(alog087, frequency("relative"))
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alog504 <- activitylog(datlogs[datlogs$artwork == "504",],
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case_id = "path",
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activity_id = "event",
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resource_id = "artwork",
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timestamps = c("start", "complete"))
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process_map(alog504, frequency("relative"))
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#--------------- (3.3) Patterns of cases ---------------
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# What kind of patterns do we have? Are their typical sequences for cases?
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# Do case patterns look different for ...
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# ... mornings and afternoons?
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# ... weekdays and weekends?
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# ... weekdays for "normal" and school vacation days?
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# ... pre and post corona?
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alog <- activitylog(datlogs,
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case_id = "case",
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activity_id = "event",
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resource_id = "path",
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timestamps = c("start", "complete"))
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process_map(alog, frequency("relative"))
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alog_no_move <- alog[alog$event != "move", ]
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pdf("../figures/traceexplore_case-event.pdf", height = 8, width = 12, pointsize = 10)
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set.seed(1050)
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processmapR::trace_explorer(alog_no_move[alog_no_move$path %in%
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sample(unique(alog_no_move$path), 300),],
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coverage = 1, type = "frequent",
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abbreviate = T)
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dev.off()
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process_map(alog080, frequency("relative"))
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alog087 <- activitylog(datlogs[datlogs$artwork == "087",],
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case_id = "case",
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activity_id = "event",
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resource_id = "path",
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timestamps = c("start", "complete"))
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process_map(alog087, frequency("relative"))
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### Mornings and afternoons
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datlogs$tod <- ifelse(lubridate::hour(datlogs$start) > 13, "afternoon", "morning")
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alog <- activitylog(datlogs[datlogs$tod == "morning",],
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case_id = "case",
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activity_id = "event",
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resource_id = "path",
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timestamps = c("start", "complete"))
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|
|
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process_map(alog, frequency("relative"))
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alog <- activitylog(datlogs[datlogs$tod == "afternoon",],
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case_id = "case",
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activity_id = "event",
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|
resource_id = "path",
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timestamps = c("start", "complete"))
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process_map(alog, frequency("relative"))
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# Are the same artworks looked at?
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|
pdf("../figures/bp_tod.pdf", height = 3.375, width = 12, pointsize = 10)
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par(mai = c(.5,.6,.1,.1), mgp = c(2.4, 1, 0))
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barplot(proportions(xtabs( ~ tod + artwork, datlogs), margin = "tod"), #col = cc[1:2],
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las = 2, beside = TRUE, legend = c("afternoon", "morning"),
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args.legend = list(x = "topleft"))
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|
|
|
dev.off()
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|
|
|
### Weekdays and weekends
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|
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datlogs$wd <- ifelse(datlogs$weekdays %in% c("Saturday", "Sunday"), "weekend", "weekday")
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alog <- activitylog(datlogs[datlogs$wd == "weekend",],
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case_id = "case",
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activity_id = "event",
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resource_id = "path",
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|
timestamps = c("start", "complete"))
|
|
|
|
process_map(alog, frequency("relative"))
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alog <- activitylog(datlogs[datlogs$wd == "weekday",],
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case_id = "case",
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|
activity_id = "event",
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|
resource_id = "path",
|
|
timestamps = c("start", "complete"))
|
|
|
|
process_map(alog, frequency("relative"))
|
|
|
|
# Are the same artworks looked at?
|
|
pdf("../figures/bp_wd.pdf", height = 3.375, width = 12, pointsize = 10)
|
|
par(mai = c(.5,.6,.1,.1), mgp = c(2.4, 1, 0))
|
|
|
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barplot(proportions(xtabs( ~ wd + artwork, datlogs), margin = "wd"),
|
|
las = 2, beside = TRUE, legend = c("weekday", "weekend"),
|
|
args.legend = list(x = "topleft"))
|
|
|
|
dev.off()
|
|
|
|
### Weekdays vs. school vacation weekdays
|
|
|
|
datlogs$wds <- ifelse(!is.na(datlogs$vacation), "vacation", "school")
|
|
datlogs$wds[datlogs$wd == "weekend"] <- NA
|
|
|
|
alog <- activitylog(datlogs[which(datlogs$wds == "school"),],
|
|
case_id = "case",
|
|
activity_id = "event",
|
|
resource_id = "path",
|
|
timestamps = c("start", "complete"))
|
|
|
|
process_map(alog, frequency("relative"))
|
|
|
|
alog <- activitylog(datlogs[which(datlogs$wds == "vacation"),],
|
|
case_id = "case",
|
|
activity_id = "event",
|
|
resource_id = "path",
|
|
timestamps = c("start", "complete"))
|
|
|
|
process_map(alog, frequency("relative"))
|
|
|
|
# Are the same artworks looked at?
|
|
pdf("../figures/bp_wds.pdf", height = 3.375, width = 12, pointsize = 10)
|
|
par(mai = c(.5,.6,.1,.1), mgp = c(2.4, 1, 0))
|
|
|
|
#barplot(xtabs( ~ wds + artwork, datlogs), las = 2, beside = TRUE,
|
|
barplot(proportions(xtabs( ~ wds + artwork, datlogs), margin = "wds"),
|
|
las = 2, beside = TRUE,
|
|
legend = c("school", "vacation"), args.legend = list(x = "topleft"))
|
|
|
|
dev.off()
|
|
|
|
### Pre and post Corona
|
|
|
|
datlogs$corona <- ifelse(datlogs$date < "2020-03-14", "pre", "post")
|
|
|
|
alog <- activitylog(datlogs[which(datlogs$corona == "pre"),],
|
|
case_id = "case",
|
|
activity_id = "event",
|
|
resource_id = "path",
|
|
timestamps = c("start", "complete"))
|
|
|
|
process_map(alog, frequency("relative"))
|
|
|
|
alog <- activitylog(datlogs[which(datlogs$corona == "post"),],
|
|
case_id = "case",
|
|
activity_id = "event",
|
|
resource_id = "path",
|
|
timestamps = c("start", "complete"))
|
|
|
|
process_map(alog, frequency("relative"))
|
|
|
|
# Are the same artworks looked at?
|
|
pdf("../figures/bp_corona.pdf", height = 3.375, width = 12, pointsize = 10)
|
|
par(mai = c(.5,.6,.1,.1), mgp = c(2.4, 1, 0))
|
|
|
|
barplot(proportions(xtabs( ~ corona + artwork, datlogs), margin = "corona"),
|
|
las = 2, beside = TRUE,
|
|
legend = c("post", "pre"), args.legend = list(x = "topleft"))
|
|
|
|
dev.off()
|
|
|
|
#--------------- (3.4) Artwork sequences ---------------
|
|
# Order in which artworks are looked at
|
|
|
|
nart <- 5 # select 5 artworks randomly
|
|
alog <- activitylog(datlogs,#[datlogs$artwork %in% sample(unique(datlogs$artwork), nart), ],
|
|
case_id = "case",
|
|
activity_id = "artwork",
|
|
resource_id = "path",
|
|
timestamps = c("start", "complete"))
|
|
|
|
#map <- process_map(alog, frequency("relative"))
|
|
|
|
## select cases with Vermeer
|
|
length(unique(datlogs[datlogs$artwork == "080", "case"]))
|
|
# 12615
|
|
case080 <- unique(datlogs[datlogs$artwork == "080", "case"])
|
|
tmp <- datlogs[datlogs$case %in% case080, ]
|
|
table(tmp$artwork)
|
|
# --> all :)
|
|
|
|
# select the ones most often (I am aiming for 10...)
|
|
barplot(table(tmp$artwork))
|
|
abline(h = 14000, col = "red")
|
|
which(table(tmp$artwork) > 14000)
|
|
|
|
often080 <- names(which(table(tmp$artwork) > 14000))
|
|
|
|
alog <- activitylog(datlogs[datlogs$artwork %in% often080, ],
|
|
case_id = "case",
|
|
activity_id = "artwork",
|
|
resource_id = "path",
|
|
timestamps = c("start", "complete"))
|
|
|
|
process_map(alog, frequency("relative"))
|
|
|
|
|
|
pdf("../figures/traceexplore_case-artwork_often080.pdf", height = 8, width = 12, pointsize = 10)
|
|
|
|
processmapR::trace_explorer(alog,
|
|
n_traces = 30, type = "frequent",
|
|
abbreviate = TRUE)
|
|
|
|
dev.off()
|
|
|
|
#--------------- (3.5) Topics ---------------
|
|
|
|
# Are there certain topics that people are interested in more than others?
|
|
# Do these topic distributions differ for comparable artworks?
|
|
|
|
alog <- activitylog(datlogs[which(datlogs$event == "openTopic"),],
|
|
case_id = "case",
|
|
activity_id = "topic",
|
|
resource_id = "path",
|
|
timestamps = c("start", "complete"))
|
|
|
|
process_map(alog, frequency("relative"))
|
|
|
|
# Order of topics for Vermeer
|
|
# alog080 <- activitylog(datlogs[datlogs$artwork == "080",],
|
|
# case_id = "case",
|
|
# activity_id = "topic",
|
|
# resource_id = "path",
|
|
# timestamps = c("start", "complete"))
|
|
#
|
|
# alog080 <- activitylog(datlogs[datlogs$artwork == "080",],
|
|
# case_id = "case",
|
|
# activity_id = "topicFile",
|
|
# resource_id = "path",
|
|
# timestamps = c("start", "complete"))
|
|
#
|
|
# #process_map(alog080, frequency("relative"))
|
|
#
|
|
# # Comparable artwork
|
|
# alog083 <- activitylog(datlogs[datlogs$artwork == "083",],
|
|
# case_id = "case",
|
|
# activity_id = "topic",
|
|
# resource_id = "path",
|
|
# timestamps = c("start", "complete"))
|
|
|
|
# artworks that have the same topics than Vermeer
|
|
which(rowSums(xtabs( ~ artwork + topic, datlogs[datlogs$topic %in%
|
|
c("artist", "details", "extra info", "komposition",
|
|
"licht und farbe", "thema"), ]) != 0) == 6)
|
|
|
|
#037 046 062 080 083 109
|
|
|
|
for (art in c("037", "046", "062", "080", "083", "109")) {
|
|
|
|
alog <- activitylog(datlogs[datlogs$event == "openTopic" & datlogs$artwork == art,],
|
|
case_id = "case",
|
|
activity_id = "topic",
|
|
resource_id = "path",
|
|
timestamps = c("start", "complete"))
|
|
|
|
process_map(alog, frequency("relative"))
|
|
}
|
|
|
|
|
|
|
|
# Angewandte Kunst, Graphik, Gemälde, Kultur
|
|
|
|
|
|
c("Kultur", "Kultur", "Graphik", "Gemälde", "Gemälde", "Gemälde",
|
|
"Gemälde", "Gemälde", "Graphik", "Gemälde", "Angewandte Kunst", "",
|
|
"Gemälde", "Angewandte Kunst", "", "", "Graphik", "Angewandte Kunst",
|
|
"Angewandte Kunst", "Gemälde", "Angewandte Kunst", "Gemälde", "",
|
|
"Gemälde", "Gemälde", "Gemälde", "Graphik", "Gemälde", "Gemälde",
|
|
"Gemälde", "", "Angewandte Kunst", "Angewandte Kunst", "Gemälde",
|
|
"Graphik", "Gemälde", "Gemälde", "Gemälde", "Gemälde",
|
|
"Angewandte Kunst", "Gemälde", "Gemälde", "Gemälde", "Kultur", "Kultur",
|
|
"Gemälde", "Kultur", "", "Gemälde", "", "Graphik", "Kultur", "Gemälde",
|
|
"", "Kultur", "Gemälde", "Kultur", "Gemälde", "Gemälde", "Gemälde",
|
|
"Kultur", "Kultur", "Kultur", "Kultur", "Kultur", "Kultur",
|
|
"Angewandte Kunst", "Info", "Info", "Info", "Kultur", "Kultur")
|
|
|
|
|
|
|
|
# BURSTS
|
|
which.max(table(datlogs$date))
|
|
tmp <- datlogs[datlogs$date == "2017-02-12", ]
|
|
|
|
# number of traces per case on 2017-02-12
|
|
rowSums(xtabs( ~ case + path, tmp) != 0)
|
|
|
|
range(tmp$start)
|
|
hours <- lubridate::hour(tmp$start)
|
|
xtabs( ~ case + hours, tmp)
|
|
|
|
# distribution of cases over the day
|
|
colSums(xtabs( ~ case + hours, tmp) != 0)
|
|
barplot(colSums(xtabs( ~ case + hours, tmp) != 0))
|
|
|
|
aggregate(path ~ case + hours, tmp, length)
|
|
|
|
|
|
|
|
|
|
tmp <- aggregate(path ~ case, datlogs, length)
|
|
tmp$date <- as.Date(datlogs[!duplicated(datlogs$case), "start"])
|
|
tmp$time <- lubridate::hour(datlogs[!duplicated(datlogs$case), "start"])
|
|
|
|
tmp[tmp$path > 200, ]
|
|
|
|
plot(path ~ time, tmp, cex = 2, col = rgb(0,0,0,.3))
|
|
|
|
lattice::barchart(path ~ time, tmp, horizontal=F)
|
|
|
|
|