Next round of script cleaning
This commit is contained in:
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76af291686
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@ -27,46 +27,66 @@ library(bupaverse)
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#--------------- (1) Read data ---------------
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dat <- read.table("results/haum/event_logfiles_glossar_2023-12-28_09-49-43.csv",
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sep = ";", header = TRUE,
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colClasses = c("POSIXct", "character", "integer",
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"integer", "numeric", "integer",
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"character", "character", "character",
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"character", "POSIXct", "POSIXct",
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"numeric", "numeric", "numeric",
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"integer", "character",
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rep("numeric", 11), "integer",
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"character", "character", "logical",
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"logical", "logical", "character",
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"character"))
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datlogs <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.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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dat$date <- NULL
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# TODO: Remove, after rerunning preprocessing
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datlogs$event <- factor(datlogs$event, levels = c("move", "flipCard",
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"openTopic",
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"openPopup"))
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dat$event <- factor(dat$event, levels = c("move", "flipCard", "openTopic", "openPopup"))
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datraw <- read.table("results/haum/raw_logfiles_2024-01-18_09-58-52.csv",
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sep = ";", header = TRUE)
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# Add weekdays to data frame
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dat$weekdays <- factor(weekdays(dat$date.start),
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datlogs$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$date.start))
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### Topics per item
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print(xtabs( ~ item + topic, datlogs), zero = "-")
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lattice::dotplot(xtabs( ~ item + topic, datlogs), auto.key = TRUE)
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mat <- t(as.matrix(xtabs( ~ item + topic, datlogs)))
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mat[mat == 0] <- NA
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image(mat, axes = F, col = rainbow(100))
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heatmap(t(mat))
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datlogs$start <- datlogs$date.start
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datlogs$complete <- datlogs$date.stop
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names(dat)[names(dat) %in% c("date.start", "date.stop")] <- c("start", "complete")
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dat$trail <- dat$trace
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dat$trace <- NULL
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# --> needs to be changed since "trace" is an inbuilt variable name in
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# bupar
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#--------------- (2) Descriptives ---------------
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# How many events per topic, per trace, ...
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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( ~ artwork + event, dat)
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tab <- xtabs( ~ artwork + event, datlogs)
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addmargins(tab)
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proportions(tab, margin = "artwork")
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@ -78,35 +98,35 @@ pdf("../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 = "artwork")), las = 2, col = cc,
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legend.text = levels(dat$event), args.legend = list(x = "bottomleft", bg = "white"))
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legend.text = levels(datlogs$event), args.legend = list(x = "bottomleft", bg = "white"))
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dev.off()
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#barchart(proportions(tab, margin = "artwork"), las = 2)
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# Proportion of events
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proportions(xtabs( ~ event, dat))
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# Mean proportion of event per trace
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colMeans(proportions(xtabs( ~ trail + event, dat), margin = "trail"))
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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 artwork
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colMeans(proportions(tab, margin = "artwork"))
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# Proportion of unclosed events
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nrow(dat[is.na(dat$complete), ])
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nrow(dat[is.na(dat$complete), ]) / nrow(dat)
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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(dat$fileId.start != dat$fileId.stop, na.rm = TRUE)
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sum(dat$fileId.start != dat$fileId.stop, na.rm = TRUE) / nrow(dat)
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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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alog <- activitylog(dat,
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case_id = "trail",
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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 = "artwork",
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@ -128,8 +148,8 @@ alog_no_move <- alog[alog$event != "move", ]
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pdf("../figures/traceexplore_trace-event.pdf", height = 8, width = 12, pointsize = 10)
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set.seed(1447)
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processmapR::trace_explorer(alog_no_move[alog_no_move$trail %in%
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sample(unique(alog_no_move$trail), 400),],
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processmapR::trace_explorer(alog_no_move[alog_no_move$path %in%
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sample(unique(alog_no_move$path), 400),],
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coverage = 1, type = "frequent",
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abbreviate = T)
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dev.off()
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@ -157,28 +177,28 @@ aggregate(relative_resource ~ artwork, ra, sum)
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# Do interaction patterns for events per trace look different for different
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# artworks?
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which.max(table(dat$artwork))
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which.min(table(dat$artwork))
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which.min(table(dat$artwork)[-c(71,72)])
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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$artwork)[-c(71,72)])
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alog080 <- activitylog(dat[dat$artwork == "080",],
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case_id = "trail",
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alog080 <- activitylog(datlogs[datlogs$artwork == "080",],
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case_id = "path",
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activity_id = "event",
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resource_id = "artwork",
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timestamps = c("start", "complete"))
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map_as_pdf(alog080, file = "../figures/pm_trace-event_080.pdf")
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alog087 <- activitylog(dat[dat$artwork == "087",],
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case_id = "trail",
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alog087 <- activitylog(datlogs[datlogs$artwork == "087",],
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case_id = "path",
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activity_id = "event",
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resource_id = "artwork",
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timestamps = c("start", "complete"))
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map_as_pdf(alog087, file = "../figures/pm_trace-event_087.pdf")
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alog504 <- activitylog(dat[dat$artwork == "504",],
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case_id = "trail",
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alog504 <- activitylog(datlogs[datlogs$artwork == "504",],
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case_id = "path",
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activity_id = "event",
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resource_id = "artwork",
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timestamps = c("start", "complete"))
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@ -194,10 +214,10 @@ map_as_pdf(alog504, file = "../figures/pm_trace-event_504.pdf")
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# ... weekdays for "normal" and school vacation days?
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# ... pre and post corona?
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alog <- activitylog(dat,
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alog <- activitylog(datlogs,
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event.pdf")
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@ -206,38 +226,38 @@ alog_no_move <- alog[alog$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$trail %in%
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sample(unique(alog_no_move$trail), 300),],
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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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map_as_pdf(alog080, file = "../figures/pm_case-event_080.pdf")
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alog087 <- activitylog(dat[dat$artwork == "087",],
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alog087 <- activitylog(datlogs[datlogs$artwork == "087",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog087, file = "../figures/pm_case-event_087.pdf")
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### Mornings and afternoons
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dat$tod <- ifelse(lubridate::hour(dat$start) > 13, "afternoon", "morning")
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datlogs$tod <- ifelse(lubridate::hour(datlogs$start) > 13, "afternoon", "morning")
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alog <- activitylog(dat[dat$tod == "morning",],
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alog <- activitylog(datlogs[datlogs$tod == "morning",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_morning.pdf")
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alog <- activitylog(dat[dat$tod == "afternoon",],
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alog <- activitylog(datlogs[datlogs$tod == "afternoon",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_afternoon.pdf")
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@ -246,7 +266,7 @@ map_as_pdf(alog, file = "../figures/pm_case-event_afternoon.pdf")
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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, dat), margin = "tod"), #col = cc[1:2],
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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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@ -254,20 +274,20 @@ dev.off()
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### Weekdays and weekends
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dat$wd <- ifelse(dat$weekdays %in% c("Saturday", "Sunday"), "weekend", "weekday")
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datlogs$wd <- ifelse(datlogs$weekdays %in% c("Saturday", "Sunday"), "weekend", "weekday")
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alog <- activitylog(dat[dat$wd == "weekend",],
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alog <- activitylog(datlogs[datlogs$wd == "weekend",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_weekend.pdf")
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alog <- activitylog(dat[dat$wd == "weekday",],
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alog <- activitylog(datlogs[datlogs$wd == "weekday",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_weekday.pdf")
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@ -276,7 +296,7 @@ map_as_pdf(alog, file = "../figures/pm_case-event_weekday.pdf")
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pdf("../figures/bp_wd.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( ~ wd + artwork, dat), margin = "wd"),
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barplot(proportions(xtabs( ~ wd + artwork, datlogs), margin = "wd"),
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las = 2, beside = TRUE, legend = c("weekday", "weekend"),
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args.legend = list(x = "topleft"))
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@ -284,21 +304,21 @@ dev.off()
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### Weekdays vs. school vacation weekdays
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dat$wds <- ifelse(!is.na(dat$vacation), "vacation", "school")
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dat$wds[dat$wd == "weekend"] <- NA
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datlogs$wds <- ifelse(!is.na(datlogs$vacation), "vacation", "school")
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datlogs$wds[datlogs$wd == "weekend"] <- NA
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alog <- activitylog(dat[which(dat$wds == "school"),],
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alog <- activitylog(datlogs[which(datlogs$wds == "school"),],
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_school.pdf")
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alog <- activitylog(dat[which(dat$wds == "vacation"),],
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alog <- activitylog(datlogs[which(datlogs$wds == "vacation"),],
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_vacation.pdf")
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@ -307,8 +327,8 @@ map_as_pdf(alog, file = "../figures/pm_case-event_vacation.pdf")
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pdf("../figures/bp_wds.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(xtabs( ~ wds + artwork, dat), las = 2, beside = TRUE,
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barplot(proportions(xtabs( ~ wds + artwork, dat), margin = "wds"),
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#barplot(xtabs( ~ wds + artwork, datlogs), las = 2, beside = TRUE,
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barplot(proportions(xtabs( ~ wds + artwork, datlogs), margin = "wds"),
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las = 2, beside = TRUE,
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legend = c("school", "vacation"), args.legend = list(x = "topleft"))
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@ -316,20 +336,20 @@ dev.off()
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### Pre and post Corona
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dat$corona <- ifelse(dat$date < "2020-03-14", "pre", "post")
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datlogs$corona <- ifelse(datlogs$date < "2020-03-14", "pre", "post")
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alog <- activitylog(dat[which(dat$corona == "pre"),],
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alog <- activitylog(datlogs[which(datlogs$corona == "pre"),],
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_pre-corona.pdf")
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alog <- activitylog(dat[which(dat$corona == "post"),],
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alog <- activitylog(datlogs[which(datlogs$corona == "post"),],
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case_id = "case",
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activity_id = "event",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_post-corona.pdf")
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@ -338,7 +358,7 @@ map_as_pdf(alog, file = "../figures/pm_case-event_post-corona.pdf")
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pdf("../figures/bp_corona.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( ~ corona + artwork, dat), margin = "corona"),
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barplot(proportions(xtabs( ~ corona + artwork, datlogs), margin = "corona"),
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las = 2, beside = TRUE,
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legend = c("post", "pre"), args.legend = list(x = "topleft"))
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@ -348,19 +368,19 @@ dev.off()
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# Order in which artworks are looked at
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nart <- 5 # select 5 artworks randomly
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alog <- activitylog(dat,#[dat$artwork %in% sample(unique(dat$artwork), nart), ],
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alog <- activitylog(datlogs,#[datlogs$artwork %in% sample(unique(datlogs$artwork), nart), ],
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case_id = "case",
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activity_id = "artwork",
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resource_id = "trail",
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resource_id = "path",
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timestamps = c("start", "complete"))
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#map <- process_map(alog, frequency("relative"))
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## select cases with Vermeer
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length(unique(dat[dat$artwork == "080", "case"]))
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length(unique(datlogs[datlogs$artwork == "080", "case"]))
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# 12615
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case080 <- unique(dat[dat$artwork == "080", "case"])
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tmp <- dat[dat$case %in% case080, ]
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case080 <- unique(datlogs[datlogs$artwork == "080", "case"])
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tmp <- datlogs[datlogs$case %in% case080, ]
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table(tmp$artwork)
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# --> all :)
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@ -371,10 +391,10 @@ which(table(tmp$artwork) > 14000)
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often080 <- names(which(table(tmp$artwork) > 14000))
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alog <- activitylog(dat[dat$artwork %in% often080, ],
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alog <- activitylog(datlogs[datlogs$artwork %in% often080, ],
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case_id = "case",
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activity_id = "artwork",
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resource_id = "trail",
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resource_id = "path",
|
||||
timestamps = c("start", "complete"))
|
||||
|
||||
map_as_pdf(alog, file = "../figures/pm_case-artwork_often080.pdf")
|
||||
@ -393,43 +413,43 @@ dev.off()
|
||||
# Are there certain topics that people are interested in more than others?
|
||||
# Do these topic distributions differ for comparable artworks?
|
||||
|
||||
alog <- activitylog(dat[which(dat$event == "openTopic"),],
|
||||
alog <- activitylog(datlogs[which(datlogs$event == "openTopic"),],
|
||||
case_id = "case",
|
||||
activity_id = "topic",
|
||||
resource_id = "trail",
|
||||
resource_id = "path",
|
||||
timestamps = c("start", "complete"))
|
||||
|
||||
map_as_pdf(alog, file = "../figures/pm_case-topic.pdf")
|
||||
|
||||
# Order of topics for Vermeer
|
||||
# alog080 <- activitylog(dat[dat$artwork == "080",],
|
||||
# alog080 <- activitylog(datlogs[datlogs$artwork == "080",],
|
||||
# case_id = "case",
|
||||
# activity_id = "topic",
|
||||
# resource_id = "trail",
|
||||
# resource_id = "path",
|
||||
# timestamps = c("start", "complete"))
|
||||
#
|
||||
# map_as_pdf(alog080, file = "../figures/pm_case-topic_080.pdf")
|
||||
#
|
||||
#
|
||||
# alog080 <- activitylog(dat[dat$artwork == "080",],
|
||||
# alog080 <- activitylog(datlogs[datlogs$artwork == "080",],
|
||||
# case_id = "case",
|
||||
# activity_id = "topicFile",
|
||||
# resource_id = "trail",
|
||||
# resource_id = "path",
|
||||
# timestamps = c("start", "complete"))
|
||||
#
|
||||
# #process_map(alog080, frequency("relative"))
|
||||
#
|
||||
# # Comparable artwork
|
||||
# alog083 <- activitylog(dat[dat$artwork == "083",],
|
||||
# alog083 <- activitylog(datlogs[datlogs$artwork == "083",],
|
||||
# case_id = "case",
|
||||
# activity_id = "topic",
|
||||
# resource_id = "trail",
|
||||
# resource_id = "path",
|
||||
# timestamps = c("start", "complete"))
|
||||
#
|
||||
# map_as_pdf(alog083, file = "../figures/pm_case-topic_083.pdf")
|
||||
|
||||
# artworks that have the same topics than Vermeer
|
||||
which(rowSums(xtabs( ~ artwork + topic, dat[dat$topic %in%
|
||||
which(rowSums(xtabs( ~ artwork + topic, datlogs[datlogs$topic %in%
|
||||
c("artist", "details", "extra info", "komposition",
|
||||
"licht und farbe", "thema"), ]) != 0) == 6)
|
||||
|
||||
@ -437,10 +457,10 @@ which(rowSums(xtabs( ~ artwork + topic, dat[dat$topic %in%
|
||||
|
||||
for (art in c("037", "046", "062", "080", "083", "109")) {
|
||||
|
||||
alog <- activitylog(dat[dat$event == "openTopic" & dat$artwork == art,],
|
||||
alog <- activitylog(datlogs[datlogs$event == "openTopic" & datlogs$artwork == art,],
|
||||
case_id = "case",
|
||||
activity_id = "topic",
|
||||
resource_id = "trail",
|
||||
resource_id = "path",
|
||||
timestamps = c("start", "complete"))
|
||||
|
||||
map_as_pdf(alog, file = paste0("../figures/pm_case-topic_", art, ".pdf"))
|
||||
@ -467,11 +487,11 @@ c("Kultur", "Kultur", "Graphik", "Gemälde", "Gemälde", "Gemälde",
|
||||
|
||||
|
||||
# BURSTS
|
||||
which.max(table(dat$date))
|
||||
tmp <- dat[dat$date == "2017-02-12", ]
|
||||
which.max(table(datlogs$date))
|
||||
tmp <- datlogs[datlogs$date == "2017-02-12", ]
|
||||
|
||||
# number of traces per case on 2017-02-12
|
||||
rowSums(xtabs( ~ case + trail, tmp) != 0)
|
||||
rowSums(xtabs( ~ case + path, tmp) != 0)
|
||||
|
||||
range(tmp$start)
|
||||
hours <- lubridate::hour(tmp$start)
|
||||
@ -481,20 +501,20 @@ xtabs( ~ case + hours, tmp)
|
||||
colSums(xtabs( ~ case + hours, tmp) != 0)
|
||||
barplot(colSums(xtabs( ~ case + hours, tmp) != 0))
|
||||
|
||||
aggregate(trail ~ case + hours, tmp, length)
|
||||
aggregate(path ~ case + hours, tmp, length)
|
||||
|
||||
|
||||
|
||||
|
||||
tmp <- aggregate(trail ~ case, dat, length)
|
||||
tmp$date <- as.Date(dat[!duplicated(dat$case), "start"])
|
||||
tmp$time <- lubridate::hour(dat[!duplicated(dat$case), "start"])
|
||||
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$trail > 200, ]
|
||||
tmp[tmp$path > 200, ]
|
||||
|
||||
plot(trail ~ time, tmp, cex = 2, col = rgb(0,0,0,.3))
|
||||
plot(path ~ time, tmp, cex = 2, col = rgb(0,0,0,.3))
|
||||
|
||||
lattice::barchart(trail ~ time, tmp, horizontal=F)
|
||||
lattice::barchart(path ~ time, tmp, horizontal=F)
|
||||
|
||||
|
||||
|
||||
@ -1,8 +1,6 @@
|
||||
import pm4py
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
from python_helpers import eval_pm, pn_infos_miner
|
||||
|
||||
###### Load data and create event logs ######
|
||||
@ -1,5 +1,3 @@
|
||||
# TODO: Clean me up! I am a mix of useful and useless!!!
|
||||
|
||||
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
|
||||
|
||||
#--------------- (1) Look at broken trace ---------------
|
||||
@ -1,301 +0,0 @@
|
||||
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code/")
|
||||
|
||||
library(lattice)
|
||||
#library(mtt)
|
||||
devtools::load_all("../../../../software/mtt")
|
||||
|
||||
# Achims colors (used by lattice)
|
||||
#cc <- palette.colors(palette = "Okabe-Ito")
|
||||
#plot(1:10, col = cc, pch = 16, cex = 2)
|
||||
|
||||
# Read data
|
||||
|
||||
datlogs <- read.table("results/haum/event_logfiles_2024-01-02_19-44-50.csv",
|
||||
colClasses = c("character", "character", "POSIXct",
|
||||
"POSIXct", "character", "integer",
|
||||
"numeric", "character", "character",
|
||||
rep("numeric", 3), "character",
|
||||
"character", rep("numeric", 11),
|
||||
"character", "character"),
|
||||
sep = ";", header = TRUE)
|
||||
|
||||
datlogs$event <- factor(datlogs$event, levels = c("move", "flipCard",
|
||||
"openTopic",
|
||||
"openPopup"))
|
||||
|
||||
### Number of log files
|
||||
length(unique(datlogs$fileId.start))
|
||||
length(unique(datlogs$fileId.stop))
|
||||
|
||||
length(unique(c(datlogs$fileId.start, datlogs$fileId.stop)))
|
||||
# 22803
|
||||
|
||||
### Number of activities
|
||||
nrow(datlogs)
|
||||
table(datlogs$event)
|
||||
proportions(table(datlogs$event))
|
||||
proportions(table(datlogs$event[datlogs$event != "move"]))
|
||||
|
||||
### Time range
|
||||
range(as.Date(datlogs$date.start))
|
||||
|
||||
### Topics per item
|
||||
print(xtabs( ~ item + topic, datlogs), zero = "-")
|
||||
lattice::dotplot(xtabs( ~ item + topic, datlogs), auto.key = TRUE)
|
||||
|
||||
### Which item gets touched most often?
|
||||
|
||||
counts_item <- table(datlogs$item)
|
||||
barchart(counts_item)
|
||||
|
||||
items <- unique(datlogs$item)
|
||||
#items <- items[!items %in% c("504", "505")]
|
||||
datart <- extract_artworks(items,
|
||||
paste0(items, ".xml"),
|
||||
"../data/haum/ContentEyevisit/eyevisit_cards_light/")
|
||||
datart <- datart[order(datart$artwork), ]
|
||||
names(counts_item) <- datart$title
|
||||
|
||||
pdf("../figures/counts_item.pdf", width = 20, height = 10, pointsize = 10)
|
||||
par(mai = c(5, .6, .1, .1))
|
||||
tmp <- barplot(counts_item, las = 2, ylim = c(0, 60000),
|
||||
border = "white", col = "#3CB4DC")
|
||||
text(tmp, counts_item + 1000, datart$artwork)
|
||||
dev.off()
|
||||
|
||||
### Which item gets touched most often first?
|
||||
|
||||
datcase <- datlogs[!duplicated(datlogs$case), ]
|
||||
counts_case <- table(datcase$item)
|
||||
names(counts_case) <- datart$title
|
||||
tmp <- barplot(counts_case, las = 2, border = "white")
|
||||
text(tmp, counts_case + 100, datart$item)
|
||||
|
||||
counts <- rbind(counts_item, counts_case)
|
||||
barplot(counts, las = 2, border = "white", col = c("gray", "darkorange"))
|
||||
|
||||
### Which teasers seem to work well?
|
||||
barplot(table(datlogs$topic), las = 2)
|
||||
|
||||
### Dwell times/duration
|
||||
datagg <- aggregate(duration ~ event + item, datlogs, mean)
|
||||
datagg$ds <- datagg$duration / 1000
|
||||
|
||||
bwplot(ds ~ event, datagg)
|
||||
bwplot(ds ~ event | item, datagg)
|
||||
xyplot(ds ~ event, datagg, groups = item)
|
||||
|
||||
# without aggregation
|
||||
bwplot(duration ~ event, datlogs)
|
||||
# in min
|
||||
|
||||
set.seed(1027)
|
||||
|
||||
pdf("../figures/duration.pdf", width = 5, height = 5, pointsize = 10)
|
||||
bwplot(I(duration/1000/60) ~ event, datlogs[sample(nrow(datlogs), 100000), ],
|
||||
ylab = "Duration in min")
|
||||
dev.off()
|
||||
|
||||
### Move events
|
||||
|
||||
datmove <- aggregate(cbind(duration, scaleSize, rotationDegree, distance, x.start,
|
||||
y.start, x.stop, y.stop) ~ item, datlogs,
|
||||
mean)
|
||||
|
||||
hist(log(datlogs$scaleSize))
|
||||
# --> better interpretable on logscale
|
||||
|
||||
plot(y.start ~ x.start, datmove, pch = 16, col = "gray")
|
||||
points(y.start ~ x.start, datmove, col = "#3CB4DC", cex = datmove$scaleSize)
|
||||
|
||||
|
||||
plot(y.start ~ x.start, datmove, type = "n", xlab = "x", ylab = "y",
|
||||
xlim = c(0, 3840), ylim = c(0, 2160))
|
||||
with(datmove, text(x.start, y.start, item, col = "gray", cex = 1.5))
|
||||
with(datmove,
|
||||
arrows(x.start, y.start, x.stop, y.stop, length = 0.07, lwd = 2)
|
||||
)
|
||||
abline(v = c(0, 3840), h = c(0, 2160), col = "#3CB4DC", lwd = 2)
|
||||
|
||||
datscale <- aggregate(scaleSize ~ item, datlogs, max)
|
||||
plot(y.start ~ x.start, datmove, pch = 16, col = "gray")
|
||||
points(y.start ~ x.start, datmove, col = "#3CB4DC", cex = datscale$scaleSize)
|
||||
|
||||
plot(y.start ~ x.start, datmove, type = "n", xlab = "x", ylab = "y",
|
||||
xlim = c(0, 3840), ylim = c(0, 2160))
|
||||
#with(datmove, text(x.stop, y.stop, item))
|
||||
with(datmove, text(x.start, y.start, item))
|
||||
|
||||
|
||||
### Are there certain areas of the table that are touched most often?
|
||||
|
||||
# heatmap
|
||||
cuts <- 100
|
||||
|
||||
datlogs$x.start.cat <- cut(datlogs$x.start, cuts)
|
||||
datlogs$y.start.cat <- cut(datlogs$y.start, cuts)
|
||||
|
||||
tab <- xtabs( ~ x.start.cat + y.start.cat, datlogs)
|
||||
|
||||
colnames(tab) <- paste0("c", 1:cuts)
|
||||
rownames(tab) <- paste0("c", 1:cuts)
|
||||
|
||||
heatmap(tab, Rowv = NA, Colv = NA)
|
||||
|
||||
|
||||
library(ggplot2)
|
||||
|
||||
ggplot(as.data.frame(tab)) +
|
||||
geom_tile(aes(x = x.start.cat, y = y.start.cat, fill = Freq)) +
|
||||
scale_fill_gradient(low = "gray40", high = "orange")
|
||||
|
||||
dattrim <- datlogs[datlogs$x.start < 3840 &
|
||||
datlogs$x.start > 0 &
|
||||
datlogs$y.start < 2160 &
|
||||
datlogs$y.start > 0 &
|
||||
datlogs$x.stop < 3840 &
|
||||
datlogs$x.stop > 0 &
|
||||
datlogs$y.stop < 2160 &
|
||||
datlogs$y.stop > 0, ]
|
||||
|
||||
cuts <- 100 # 200, 100, 70, ...
|
||||
|
||||
# start
|
||||
dattrim$x.start.cat <- cut(dattrim$x.start, cuts)
|
||||
dattrim$y.start.cat <- cut(dattrim$y.start, cuts)
|
||||
|
||||
tab.start <- xtabs( ~ x.start.cat + y.start.cat, dattrim)
|
||||
colnames(tab.start) <- NULL
|
||||
rownames(tab.start) <- NULL
|
||||
|
||||
pdf("../figures/heatmap_start.pdf", width = 5, height = 5, pointsize = 10)
|
||||
heatmap(tab.start, Rowv = NA, Colv = NA)
|
||||
dev.off()
|
||||
|
||||
|
||||
my_colors <- colorRampPalette(c("#009E73", "#E69F00"))
|
||||
heatmap(tab.start, Rowv = NA, Colv = NA, col = my_colors(1000))
|
||||
|
||||
ggplot(as.data.frame(tab.start)) +
|
||||
geom_tile(aes(x = x.start.cat, y = y.start.cat, fill = Freq)) +
|
||||
scale_fill_gradient(low = "#009E73", high = "#E69F00")
|
||||
|
||||
# stop
|
||||
dattrim$x.stop.cat <- cut(dattrim$x.stop, cuts)
|
||||
dattrim$y.stop.cat <- cut(dattrim$y.stop, cuts)
|
||||
tab.stop <- xtabs( ~ x.stop.cat + y.stop.cat, dattrim)
|
||||
colnames(tab.stop) <- NULL
|
||||
rownames(tab.stop) <- NULL
|
||||
|
||||
pdf("../figures/heatmap_stop.pdf", width = 5, height = 5, pointsize = 10)
|
||||
heatmap(tab.stop, Rowv = NA, Colv = NA)
|
||||
dev.off()
|
||||
|
||||
heatmap(tab.stop, Rowv = NA, Colv = NA, col = my_colors(10))
|
||||
|
||||
### How many visitors per day
|
||||
|
||||
datlogs$date <- as.Date(datlogs$date.start)
|
||||
|
||||
# Interactions per day
|
||||
datint <- aggregate(case ~ date, datlogs, length)
|
||||
plot(datint, type = "h")
|
||||
|
||||
# Cases per day
|
||||
datcase <- aggregate(case ~ date, datlogs, function(x) length(unique(x)))
|
||||
plot(datcase, type = "h")
|
||||
|
||||
# Paths per day
|
||||
datpath <- aggregate(path ~ date, datlogs, function(x) length(unique(x)))
|
||||
plot(datpath, type = "h")
|
||||
|
||||
plot(path ~ date, datpath, type = "h", col = "#3CB4DC")
|
||||
points(case ~ date, datcase, type = "h")
|
||||
|
||||
pdf("../figures/cases_per_day2.pdf", width = 9, height = 5, pointsize = 10)
|
||||
par(mai = c(.6, .6, .1, .1), mgp = c(2.4, 1, 0))
|
||||
plot(case ~ date, datcase, type = "h", col = "#3CB4DC")
|
||||
abline(v = datcase$date[datcase$date %in% c("2020-03-13", "2022-10-25")],
|
||||
col = "#FF6900", lty = 2)
|
||||
text(datcase$date[datcase$date == "2020-03-13"]+470, 80,
|
||||
"Corona gap from 2020-03-13 to 2022-10-25",
|
||||
col = "#D55E00")
|
||||
dev.off()
|
||||
|
||||
|
||||
### Other stuff
|
||||
|
||||
## function dependencies of mtt
|
||||
devtools::load_all("../../../../software/mtt")
|
||||
#library(mtt)
|
||||
|
||||
library(mvbutils)
|
||||
foodweb(where = "package:mtt")
|
||||
|
||||
pdf("../figures/fun_depend_mtt.pdf", width = 8, height = 4, pointsize = 10)
|
||||
foodweb(where = "package:mtt",
|
||||
prune = c("parse_logfiles", "create_eventlogs", "extract_artworks",
|
||||
"extract_topics"),
|
||||
#expand.ybox = 1.8, #cex = .6,
|
||||
#border = TRUE,
|
||||
#boxcolor = "gray",
|
||||
color.lines = FALSE,
|
||||
lwd = 2, mai = c(0, 0, 0, 0))
|
||||
dev.off()
|
||||
|
||||
|
||||
## weird behavior of timeMs
|
||||
|
||||
pdf("../figures/timeMs.pdf", width = 9, height = 6, pointsize = 10)
|
||||
#par(mai = c(.6, .6, .1, .1), mgp = c(2.4, 1, 0))
|
||||
#plot(timeMs.start ~ as.factor(fileId), datlogs[1:2000,], xlab = "fileId")
|
||||
bwplot(timeMs.start ~ as.factor(fileId.start), datlogs[1:2000,], xlab = "",
|
||||
scales = list(x = list(rot = 90), y = list(rot = 90)))
|
||||
dev.off()
|
||||
|
||||
## x,y-coordinates out of range
|
||||
|
||||
set.seed(1522)
|
||||
|
||||
pdf("../figures/xycoord.pdf", width = 5, height = 5, pointsize = 10)
|
||||
par(mai = c(.6, .6, .1, .1), mgp = c(2.4, 1, 0))
|
||||
#par(mfrow = c(1, 2))
|
||||
plot(y.start ~ x.start, datlogs[sample(nrow(datlogs), 10000), ])
|
||||
abline(v = c(0, 3840), h = c(0, 2160), col = "#3CB4DC", lwd = 2)
|
||||
#plot(y.stop ~ x.stop, datlogs)
|
||||
#abline(v = c(0, 3840), h = c(0, 2160), col = "#3CB4DC", lwd = 2)
|
||||
legend("bottomleft", "Random sample of 10,000", bg = "white")
|
||||
legend("topleft", "4K-Display: 3840 x 2160", bg = "white")
|
||||
dev.off()
|
||||
|
||||
## moves
|
||||
|
||||
dat001 <- datlogs[which(datlogs$item == "001"), ]
|
||||
|
||||
index <- as.numeric(as.factor(dat001$path))
|
||||
cc <- sample(colors(), 100)
|
||||
|
||||
plot(y.start ~ x.start, dat001, type = "n", xlab = "x", ylab = "y",
|
||||
xlim = c(0, 3840), ylim = c(0, 2160))
|
||||
with(dat001[1:200,], arrows(x.start, y.start, x.stop, y.stop,
|
||||
length = .07, col = cc[index]))
|
||||
|
||||
plot(y.start ~ x.start, dat001, xlab = "x", ylab = "y",
|
||||
xlim = c(0, 3840), ylim = c(0, 2160), pch = 16, col = "gray")
|
||||
points(y.start ~ x.start, dat001, xlab = "x", ylab = "y",
|
||||
xlim = c(0, 3840), ylim = c(0, 2160), cex = dat001$scaleSize,
|
||||
col = "blue")
|
||||
|
||||
|
||||
|
||||
cc <- sample(colors(), 70)
|
||||
|
||||
dat1 <- datlogs[!duplicated(datlogs$item), ]
|
||||
dat1 <- dat1[order(dat1$item), ]
|
||||
|
||||
plot(y.start ~ x.start, dat1, type = "n", xlim = c(-100, 4500), ylim = c(-100, 2500))
|
||||
abline(h = c(0, 2160), v = c(0, 3840), col = "lightgray")
|
||||
with(dat1, points(x.start, y.start, col = cc, pch = 16))
|
||||
with(dat1, points(x.stop, y.stop, col = cc, pch = 16))
|
||||
with(dat1, arrows(x.start, y.start, x.stop, y.stop, length = .07, col = cc))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user