371 lines
12 KiB
R
371 lines
12 KiB
R
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# 00_current_analysis.R
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#
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# content: (1) Read event log data
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# (2) Descriptives
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# (3) Process Mining
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#
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# input: ../data/haum/event_logfiles_glossar_2023-10-29_10-26-42.csv
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# output:
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#
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# last mod: 2023-11-02, NW
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code")
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#library(mtt)
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devtools::load_all("../../../../software/mtt")
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library(lattice)
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library(bupaverse)
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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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# What about popups?
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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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dat <- read.table("../data/haum/event_logfiles_glossar_2023-10-29_10-26-42.csv",
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sep = ";", header = TRUE)
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dat$date <- as.POSIXct(dat$date)
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dat$date.start <- as.POSIXct(dat$date.start)
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dat$date.stop <- as.POSIXct(dat$date.stop)
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dat$artwork <- sprintf("%03d", dat$artwork)
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dat$event <- factor(dat$event, levels = c("move", "flipCard", "openTopic", "openPopup"))
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# Add weekdays to data frame
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dat$weekdays <- factor(weekdays(dat$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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names(dat)[names(dat) %in% c("date.start", "date.stop")] <- c("start", "complete")
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#--------------- (2) Descriptives ---------------
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# How many events per topic, per trace, ...
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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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addmargins(tab)
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proportions(tab, margin = "artwork")
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proportions(tab, margin = "event")
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barplot(t(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( ~ trace + event, dat), margin = "trace"))
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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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#--------------- (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 = "trace",
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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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timestamps = c("start", "complete"))
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# process_map(alog, frequency("relative"))
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map_as_pdf(alog, file = "../figures/pm_trace-event.pdf")
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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(dat$artwork))
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which.min(table(dat$artwork))
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which.min(table(dat$artwork)[-c(71,72)])
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alog080 <- activitylog(dat[dat$artwork == "080",],
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case_id = "trace",
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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$artwork == "087",],
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case_id = "trace",
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activity_id = "event",
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resource_id = "artwork",
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timestamps = c("start", "complete"))
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map_as_pdf(alog087, file = "../figures/pm_trace-event_087.pdf")
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alog504 <- activitylog(dat[dat$artwork == "504",],
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case_id = "trace",
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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(alog504, file = "../figures/pm_trace-event_504.pdf")
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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(dat,
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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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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alog080 <- activitylog(dat[dat$artwork == "080",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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timestamps = c("start", "complete"))
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map_as_pdf(alog080, file = "../figures/pm_case-event_080.pdf")
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alog087 <- activitylog(dat[dat$artwork == "087",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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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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alog <- activitylog(dat[dat$tod == "morning",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_morning.pdf")
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alog <- activitylog(dat[dat$tod == "afternoon",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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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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### Weekdays and weekends
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dat$wd <- ifelse(dat$weekdays %in% c("Saturday", "Sunday"), "weekend", "weekday")
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alog <- activitylog(dat[dat$wd == "weekend",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_weekend.pdf")
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alog <- activitylog(dat[dat$wd == "weekday",],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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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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### 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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alog <- activitylog(dat[which(dat$wds == "school"),],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_school.pdf")
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alog <- activitylog(dat[which(dat$wds == "vacation"),],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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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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### Pre and post Corona
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dat$corona <- ifelse(dat$date < "2020-03-14", "pre", "post")
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alog <- activitylog(dat[which(dat$corona == "pre"),],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-event_pre-corona.pdf")
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alog <- activitylog(dat[which(dat$corona == "post"),],
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case_id = "case",
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activity_id = "event",
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resource_id = "trace",
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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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#--------------- (3.4) Artwork sequences ---------------
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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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case_id = "case",
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activity_id = "artwork",
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resource_id = "trace",
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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$artwork == "080", "case"]))
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# 12615
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case080 <- unique(dat[dat$artwork == "080", "case"])
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tmp <- dat[dat$case %in% case080, ]
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table(tmp$artwork)
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# --> all :)
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# select the ones most often (I am aiming for 10...)
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barplot(table(tmp$artwork))
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abline(h = 12000, col = "red")
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which(table(tmp$artwork) > 12000)
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often080 <- names(which(table(tmp$artwork) > 12000))
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alog <- activitylog(dat[dat$artwork %in% often080, ],
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case_id = "case",
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activity_id = "artwork",
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resource_id = "trace",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = "../figures/pm_case-artwork_often080.pdf")
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#--------------- (3.5) Topics ---------------
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# Are there certain topics that people are interested in more than others?
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# Do these topic distributions differ for comparable artworks?
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# Order of topics for Vermeer
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# alog080 <- activitylog(dat[dat$artwork == "080",],
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# case_id = "case",
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# activity_id = "topic",
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# resource_id = "trace",
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# timestamps = c("start", "complete"))
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#
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# map_as_pdf(alog080, file = "../figures/pm_case-topic_080.pdf")
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#
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#
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# alog080 <- activitylog(dat[dat$artwork == "080",],
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# case_id = "case",
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# activity_id = "topicFile",
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# resource_id = "trace",
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# timestamps = c("start", "complete"))
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#
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# #process_map(alog080, frequency("relative"))
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#
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# # Comparable artwork
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# alog083 <- activitylog(dat[dat$artwork == "083",],
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# case_id = "case",
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# activity_id = "topic",
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# resource_id = "trace",
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# timestamps = c("start", "complete"))
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#
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# map_as_pdf(alog083, file = "../figures/pm_case-topic_083.pdf")
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# artworks that have the same topics than Vermeer
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which(rowSums(xtabs( ~ artwork + topic, dat[dat$topic %in%
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c("artist", "details", "extra info", "komposition",
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"licht und farbe", "thema"), ]) != 0) == 6)
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#037 046 062 080 083 109
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for (art in c("037", "046", "062", "080", "083", "109")) {
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alog <- activitylog(dat[dat$artwork == art,],
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case_id = "case",
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activity_id = "topic",
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resource_id = "trace",
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timestamps = c("start", "complete"))
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map_as_pdf(alog, file = paste0("../figures/pm_case-topic_", art, ".pdf"))
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}
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# Angewandte Kunst, Graphik, Gemälde, Kultur
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c("Kultur", "Kultur", "Graphik", "Gemälde", "Gemälde", "Gemälde",
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"Gemälde", "Gemälde", "Graphik", "Gemälde", "Angewandte Kunst", "",
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"Gemälde", "Angewandte Kunst", "", "", "Graphik", "Angewandte Kunst",
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"Angewandte Kunst", "Gemälde", "Angewandte Kunst", "Gemälde", "",
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"Gemälde", "Gemälde", "Gemälde", "Graphik", "Gemälde", "Gemälde",
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"Gemälde", "", "Angewandte Kunst", "Angewandte Kunst", "Gemälde",
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"Graphik", "Gemälde", "Gemälde", "Gemälde", "Gemälde",
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"Angewandte Kunst", "Gemälde", "Gemälde", "Gemälde", "Kultur", "Kultur",
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"Gemälde", "Kultur", "", "Gemälde", "", "Graphik", "Kultur", "Gemälde",
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"", "Kultur", "Gemälde", "Kultur", "Gemälde", "Gemälde", "Gemälde",
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"Kultur", "Kultur", "Kultur", "Kultur", "Kultur", "Kultur",
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"Angewandte Kunst", "Info", "Info", "Info", "Kultur", "Kultur")
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# BURSTS
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which.max(table(dat$date))
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tmp <- dat[dat$date == "2017-02-12", ]
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# number of traces per case on 2017-02-12
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rowSums(xtabs( ~ case + trace, tmp) != 0)
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range(tmp$start)
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startcut <- cut(tmp$start, 7)
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xtabs( ~ case + startcut, tmp)
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||
|
|
|
||
|
|
# distribution of cases over the day
|
||
|
|
colSums(xtabs( ~ case + startcut, tmp) != 0)
|
||
|
|
barplot(colSums(xtabs( ~ case + startcut, tmp) != 0))
|
||
|
|
|
||
|
|
aggregate(trace ~ case + startcut, tmp, length)
|
||
|
|
|
||
|
|
|
||
|
|
###########################################################################
|
||
|
|
# HELPER
|
||
|
|
|
||
|
|
map_as_pdf <- function(alog, file, type = frequency("relative")) {
|
||
|
|
map <- process_map(alog, type = type)
|
||
|
|
g <- DiagrammeR::grViz(map$x$diagram) |> DiagrammeRsvg::export_svg() |> charToRaw()
|
||
|
|
rsvg::rsvg_pdf(g, file)
|
||
|
|
}
|
||
|
|
|