# 02_descriptives.R
#
# content: (1) Read data
#          (2) Descriptives
#          (3) Process Mining
#           (3.1) Check data quality
#           (3.2) Interactions for different artworks
#           (3.3) Patterns of cases
#           (3.4) Artwork sequences
#           (3.5) Topics
#
# input:  results/event_logfiles_2024-02-21_16-07-33.csv
#         results/raw_logfiles_2024-02-21_16-07-33.csv
# output: results/figures/counts_item.pdf
#         results/figures/counts_item_firsttouch.pdf
#         results/figures/duration.pdf
#         results/figures/heatmap_start.pdf
#         results/figures/heatmap_stop.pdf
#         results/figures/cases_per_day.pdf
#         results/figures/timeMs.pdf
#         results/figures/xycoord.pdf
#         results/figures/event-dist.pdf
#         results/figures/traceexplore_trace-event.pdf
#         results/figures/ra_trace-event.pdf
#         results/figures/traceexplore_case-event.pdf
#         results/figures/bp_tod.pdf
#         results/figures/bp_wd.pdf
#         results/figures/bp_wds.pdf
#         results/figures/bp_corona.pdf
#         results/figures/traceexplore_case-artwork_often080.pdf
#
# last mod: 2024-03-22

# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")

# Overall Research Question: How do museum visitors interact with the
# artworks presented on the MTT?

# Distribution of bursts
# Can this be visualized in a nice way?

#--------------- (1) Read data ---------------

datlogs <- read.table("results/event_logfiles_2024-02-21_16-07-33.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"))

datraw <- read.table("results/raw_logfiles_2024-02-21_16-07-33.csv",
                     sep = ";", header = TRUE)

# Add weekdays to data frame
datlogs$weekdays <- factor(weekdays(datlogs$date.start),
                       levels = c("Montag", "Dienstag", "Mittwoch",
                                  "Donnerstag", "Freitag", "Samstag",
                                  "Sonntag"),
                       labels = c("Monday", "Tuesday", "Wednesday",
                                  "Thursday", "Friday", "Saturday",
                                  "Sunday"))

### Number of log files
length(unique(datraw$fileId))
# 39767
length(unique(c(datlogs$fileId.start, datlogs$fileId.stop)))
# 22789

### 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)

mat <- t(as.matrix(xtabs( ~ item + topic, datlogs)))
mat[mat == 0] <- NA
image(mat, axes = F, col = rainbow(100))

#--------------- (2) Descriptives ---------------

### Which item gets touched most often?

counts_item <- table(datlogs$item)
lattice::barchart(counts_item)

items <- unique(datlogs$item)
#items <- items[!items %in% c("504", "505")]
datart <- mtt::extract_artworks(items,
                           paste0(items, ".xml"),
                           "../data/haum/ContentEyevisit/eyevisit_cards_light/")
datart <- datart[order(datart$artwork), ]
names(counts_item) <- datart$title

pdf("results/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 = NA, col = "#434F4F")
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)

pdf("results/figures/counts_item_firsttouch.pdf",
    width = 20, height = 10, pointsize = 10)
par(mai = c(5, .6, .1, .1))

tmp <- barplot(counts, las = 2, border = NA, col = c("#434F4F", "#FF6900"), ylim = c(0, 65000))
text(tmp, counts_item + counts_case + 1000, datart$artwork)
legend("topleft", c("Total interactions", "First interactions"),
       col = c("#434F4F", "#FF6900"), pch = 15, bty = "n")
dev.off()

### 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   # in secs

lattice::bwplot(ds ~ event, datagg)

# without aggregation
lattice::bwplot(duration / 1000 / 60 ~ event, datlogs)
# in min

set.seed(1027)

pdf("results/figures/duration.pdf", width = 5, height = 5, pointsize = 10)
lattice::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)


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("results/figures/heatmap_start.pdf", width = 5, height = 5, pointsize = 10)
heatmap(tab.start, Rowv = NA, Colv = NA)
dev.off()

# 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("results/figures/heatmap_stop.pdf", width = 5, height = 5, pointsize = 10)
heatmap(tab.stop, Rowv = NA, Colv = NA)
dev.off()

### 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("results/figures/cases_per_day.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 = "#434F4F")
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 = "#FF6900")
dev.off()

## weird behavior of timeMs

pdf("results/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")
lattice::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("results/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))


# How many events per topic, per path, ...
# How many popups per artwork?

# Number of events per artwork
tab <- xtabs( ~ item + event, datlogs)
addmargins(tab)

proportions(tab, margin = "item")
proportions(tab, margin = "event")

cc <- palette.colors(palette = "Okabe-Ito")[c(3,2,4,8)]

pdf("results/figures/event-dist.pdf", height = 3.375, width = 12, pointsize = 10)
par(mai = c(.4,.4,.1,.1), mgp = c(2.4, 1, 0))

barplot(t(proportions(tab, margin = "item")), las = 2, col = c("#78004B", "#3CB4DC", "#91C86E", "#FF6900"),
        legend.text = levels(datlogs$event), args.legend = list(x = "bottomleft", bg = "white"))

dev.off()

#lattice::barchart(proportions(tab, margin = "item"), las = 2)

# Proportion of events
proportions(xtabs( ~ event, datlogs))
# Mean proportion of event per path
colMeans(proportions(xtabs( ~ path + event, datlogs), margin = "path"))
# Mean proportion of event per item
colMeans(proportions(tab, margin = "item"))

# Proportion of unclosed events

nrow(datlogs[is.na(datlogs$complete), ])
nrow(datlogs[is.na(datlogs$complete), ]) / nrow(datlogs)

# Proportion of events spanning more than one log file
sum(datlogs$fileId.start != datlogs$fileId.stop, na.rm = TRUE)
sum(datlogs$fileId.start != datlogs$fileId.stop, na.rm = TRUE) / nrow(datlogs)


#--------------- (3) Process Mining ---------------

#--------------- (3.1) Check data quality ---------------

datlogs$start <- datlogs$date.start
datlogs$complete <- datlogs$date.stop

alog <- bupaR::activitylog(datlogs,
                    case_id = "path",
                    activity_id = "event",
                    #resource_id = "case",
                    resource_id = "item",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

alogf <- edeaR::filter_trace_frequency(alog, percentage = 0.9)

processmapR::process_map(alogf,   # alog,
                         type_nodes = processmapR::frequency("absolute"),
                         sec_nodes  = processmapR::frequency("relative"),
                         type_edges = processmapR::frequency("absolute"),
                         sec_edges  = processmapR::frequency("relative"),
                         rankdir    = "TB")

alog_no_move <- alog[alog$event != "move", ]

pdf("results/figures/traceexplore_trace-event.pdf", height = 8, width = 12, pointsize = 10)
set.seed(1447)
processmapR::trace_explorer(alog_no_move[alog_no_move$path %in%
                            sample(unique(alog_no_move$path), 400),],
                            coverage = 1, type = "frequent",
                            abbreviate = T)
dev.off()

pdf("results/figures/ra_trace-event.pdf", height = 8, width = 12, pointsize = 10)

ra_no_move <- edeaR::resource_frequency(alog_no_move, "resource-activity")
levels(ra_no_move$event) <- c("flipCard", "flipCard", "openTopic", "openPopup")
plot(ra_no_move)

dev.off()

ra <- edeaR::resource_frequency(alog, "resource-activity")
plot(ra)

heatmap(xtabs(relative_activity ~ artwork + event, ra))
heatmap(xtabs(relative_resource ~ artwork + event, ra_no_move))
heatmap(xtabs(relative_activity ~ artwork + event, ra_no_move))

aggregate(relative_activity ~ event, ra, sum)
aggregate(relative_resource ~ artwork, ra, sum)


#--------------- (3.2) Interactions for different artworks ---------------
# Do interaction patterns for events per trace look different for different
# artworks?

which.max(table(datlogs$artwork))
which.min(table(datlogs$artwork))
which.min(table(datlogs$artwork)[-c(71,72)])

alog080 <- bupaR::activitylog(datlogs[datlogs$artwork == "080",],
                    case_id = "path",
                    activity_id = "event",
                    resource_id = "artwork",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog80, processmapR::frequency("relative"))

alog087 <- bupaR::activitylog(datlogs[datlogs$artwork == "087",],
                    case_id = "path",
                    activity_id = "event",
                    resource_id = "artwork",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog087, processmapR::frequency("relative"))

alog504 <- bupaR::activitylog(datlogs[datlogs$artwork == "504",],
                    case_id = "path",
                    activity_id = "event",
                    resource_id = "artwork",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog504, processmapR::frequency("relative"))

#--------------- (3.3) Patterns of cases ---------------

# What kind of patterns do we have? Are their typical sequences for cases?
# Do case patterns look different for ...
# ... mornings and afternoons?
# ... weekdays and weekends?
# ... weekdays for "normal" and school vacation days?
# ... pre and post corona?

alog <- bupaR::activitylog(datlogs,
                    case_id = "case",
                    activity_id = "event",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

alog_no_move <- alog[alog$event != "move", ]

pdf("results/figures/traceexplore_case-event.pdf", height = 8, width = 12, pointsize = 10)
set.seed(1050)
processmapR::trace_explorer(alog_no_move[alog_no_move$path %in%
                            sample(unique(alog_no_move$path), 300),],
                            coverage = 1, type = "frequent",
                            abbreviate = T)
dev.off()

processmapR::process_map(alog080, processmapR::frequency("relative"))

alog087 <- bupaR::activitylog(datlogs[datlogs$artwork == "087",],
                       case_id = "case",
                       activity_id = "event",
                       resource_id = "path",
                       timestamps = c("start", "complete"))

processmapR::process_map(alog087, processmapR::frequency("relative"))

### Mornings and afternoons

datlogs$tod <- ifelse(lubridate::hour(datlogs$start) > 13, "afternoon", "morning")

alog <- bupaR::activitylog(datlogs[datlogs$tod == "morning",],
                    case_id = "case",
                    activity_id = "event",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

alog <- bupaR::activitylog(datlogs[datlogs$tod == "afternoon",],
                    case_id = "case",
                    activity_id = "event",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

# Are the same artworks looked at?
pdf("results/figures/bp_tod.pdf", height = 3.375, width = 12, pointsize = 10)
par(mai = c(.5,.6,.1,.1), mgp = c(2.4, 1, 0))

barplot(proportions(xtabs( ~ tod + artwork, datlogs), margin = "tod"), #col = cc[1:2],
        las = 2, beside = TRUE, legend = c("afternoon", "morning"),
        args.legend = list(x = "topleft"))

dev.off()

### Weekdays and weekends

datlogs$wd <- ifelse(datlogs$weekdays %in% c("Saturday", "Sunday"), "weekend", "weekday")

alog <- bupaR::activitylog(datlogs[datlogs$wd == "weekend",],
                    case_id = "case",
                    activity_id = "event",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

alog <- bupaR::activitylog(datlogs[datlogs$wd == "weekday",],
                    case_id = "case",
                    activity_id = "event",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

# Are the same artworks looked at?
pdf("results/figures/bp_wd.pdf", height = 3.375, width = 12, pointsize = 10)
par(mai = c(.5,.6,.1,.1), mgp = c(2.4, 1, 0))

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 <- bupaR::activitylog(datlogs[which(datlogs$wds == "school"),],
                    case_id = "case",
                    activity_id = "event",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

alog <- bupaR::activitylog(datlogs[which(datlogs$wds == "vacation"),],
                    case_id = "case",
                    activity_id = "event",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

# Are the same artworks looked at?
pdf("results/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 <- bupaR::activitylog(datlogs[which(datlogs$corona == "pre"),],
                    case_id = "case",
                    activity_id = "event",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

alog <- bupaR::activitylog(datlogs[which(datlogs$corona == "post"),],
                    case_id = "case",
                    activity_id = "event",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

# Are the same artworks looked at?
pdf("results/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 <- bupaR::activitylog(datlogs,#[datlogs$artwork %in% sample(unique(datlogs$artwork), nart), ],
                    case_id = "case",
                    activity_id = "artwork",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

#map <- processmapR::process_map(alog, processmapR::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 <- bupaR::activitylog(datlogs[datlogs$artwork %in% often080, ],
                    case_id = "case",
                    activity_id = "artwork",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))


pdf("results/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 <- bupaR::activitylog(datlogs[which(datlogs$event == "openTopic"),],
                    case_id = "case",
                    activity_id = "topic",
                    resource_id = "path",
                    timestamps = c("start", "complete"))

processmapR::process_map(alog, processmapR::frequency("relative"))

# Order of topics for Vermeer
# alog080 <- bupaR::activitylog(datlogs[datlogs$artwork == "080",],
#                     case_id = "case",
#                     activity_id = "topic",
#                     resource_id = "path",
#                     timestamps = c("start", "complete"))
#
# alog080 <- bupaR::activitylog(datlogs[datlogs$artwork == "080",],
#                        case_id = "case",
#                        activity_id = "topicFile",
#                        resource_id = "path",
#                        timestamps = c("start", "complete"))
#
# #processmapR::process_map(alog080, processmapR::frequency("relative"))
#
# # Comparable artwork
# alog083 <- bupaR::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 <- bupaR::activitylog(datlogs[datlogs$event == "openTopic" & datlogs$artwork == art,],
                      case_id = "case",
                      activity_id = "topic",
                      resource_id = "path",
                      timestamps = c("start", "complete"))

  processmapR::process_map(alog, processmapR::frequency("relative"))
}