Cleaned out some commented code, that I do not need anymore
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@ -14,17 +14,6 @@
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code")
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# LogEntry classes:
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# TRANSFORM_START: "Transform start" --> "Transformation Start" in Tool
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# TRANSFORM_STOP: "Transform stop"
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# START_APPLICATION: "Start Application"
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# SHOW_APPLICATION: "Show Application"
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# SHOW_INFO: "Show Info" --> "Flip Card" in Tool
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# SHOW_FRONT: "Show Front"
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# SHOW_POPUP: "ShowPopup" --> "Show Popup" in Tool
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# HIDE_POPUP: "HidePopup"
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# ARTWORK: "Artwork" --> "Show Topic" in Tool
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#' # Read data
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dat0 <- read.table("../data/rawdata_logfiles_small.csv", sep = ";",
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@ -63,11 +52,11 @@ table(table(dat1$eventid))
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num_stop <- c(diff(c(0, which(dat1$event == "Transform start"))))
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table(num_stop)
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# TODO: Do I still need this?
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dat1$eventrep <- rep(num_start, num_start)
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dat1$dupl <- duplicated(dat1[, c("event", "eventid")]) # keep first
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dat1$dupl <- duplicated(dat1[, c("event", "eventid")], fromLast = TRUE) # keep last
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dat1[dat1$eventrep == 10, ]
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dat1$dupl <- NULL
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dat1$eventrep <- NULL
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@ -145,24 +134,13 @@ summary(dat_trans)
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#' # Close other events
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dat2 <- dat[!dat$event %in% c("Transform start", "Transform stop"), ]
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# dat2$x <- NULL
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# dat2$y <- NULL
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# dat2$scale <- NULL
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# dat2$rotation <- NULL
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rownames(dat2) <- NULL
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# Create event ID for closing events
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# num_start <- diff(c(0, which(dat2$event == "Show Front")))
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# dat2$trace <- rep(seq_along(num_start), num_start)
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# head(dat2[, c("artwork", "event", "trace")], 50)
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# --> does not work because of glossar entries... can't sort by artwork
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dat2$trace <- NA
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last_event <- dat2$event[1]
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aws <- unique(dat2$artwork)[unique(dat2$artwork) != "glossar"]
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#
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for (art in aws) { # select artwork
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for (art in aws) { # select artwork
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for (i in 1:nrow(dat2)) { # go through rows
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@ -189,9 +167,7 @@ tail(dat2[, c("artwork", "event", "trace")], 50)
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rm(aws, i, j, last_event, art)
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## Fix glossar entries
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### Find artwork for glossar entry
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#' ## Fix glossar entries (find corresponding artworks)
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glossar_files <- unique(dat2[dat2$artwork == "glossar", "popup"])
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@ -278,7 +254,7 @@ for (file in tmp_lut$glossar_file) {
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dat2[14110:14130, ]
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# TODO: Integrate for loop into for loop above
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# TODO: Integrate for-loop into for-loop above
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# TODO: For now: Exclude not matched glossar entries
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@ -303,7 +279,8 @@ flipCard_wide$event <- "flipCard"
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flipCard_wide$duration <- flipCard_wide$time_ms.stop -
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flipCard_wide$time_ms.start
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# TODO: Check if I still need to enter all of these variables
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# --> x, y, scale, rotation?
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flipCard_wide$card <- NA
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flipCard_wide$popup <- NA
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flipCard_wide$x.start <- NA
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@ -377,6 +354,7 @@ dat_openTopic <- openTopic_wide[, c("fileid.start", "fileid.stop", "event",
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rm(openTopic_wide, num_start)
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#' ## close openPopup
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dat5 <- subset(df, df$event %in% c("ShowPopup", "HidePopup"))
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dat5 <- dat5[order(dat5$artwork, dat5$popup, dat5$date), ]
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rownames(dat5) <- NULL
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@ -430,8 +408,7 @@ rm(num_start, openPopup_wide)
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# TODO: Should card maybe also be filled in for "openPopup"?
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#' ## Use `rbind()` instead...
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# --> unbeatable in terms of time!
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#' ## Merge data sets for different events
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dat_all <- rbind(dat_trans, dat_flipCard, dat_openTopic, dat_openPopup)
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@ -439,7 +416,8 @@ dat_all <- rbind(dat_trans, dat_flipCard, dat_openTopic, dat_openPopup)
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nrow(dat_all) == (nrow(dat_trans) + nrow(dat_flipCard) +
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nrow(dat_openTopic) + nrow(dat_openPopup))
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# remove all events that do not have a `date.start`
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#' ## Remove all events that do not have a `date.start`
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dim(dat_all[is.na(dat_all$date.start), ])
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dat_all <- dat_all[!is.na(dat_all$date.start), ]
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# There is only a `date.stop`, when event is not properly closed, see here:
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@ -456,7 +434,6 @@ dat[31000:31019,] # this one e.g.
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# not interpretable
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dat_all[which(dat_all$fileid.start != dat_all$fileid.stop), "duration"] <- NA
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# sort by `start.date`
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dat_all <- dat_all[order(dat_all$date.start), ]
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rownames(dat_all) <- NULL
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@ -470,8 +447,6 @@ summary(dat_all) # OK, this actually makes a lot of sense :)
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#' ## Create case variable
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#dat_all$timediff <- as.numeric(dat_all$date.stop - dat_all$date.start)
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dat_all$timediff <- as.numeric(diff(c(dat_all$date.start[1], dat_all$date.start)))
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hist(dat_all$timediff[dat_all$timediff < 40], breaks = 50)
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@ -507,12 +482,6 @@ dat_all <- dat_all[, c("fileid.start", "fileid.stop", "eventid", "case",
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#' ## Add `trace` numbers for `move` events
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# when case and artwork are identical and there is only 1 trace value
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# --> assign it to all `move` events for that case and artwork
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# when case and artwork are identical and there is more than 1 trace value
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# --> assign the `trace` value that was right before this `move` event
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# (could, of course, also be after)
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cases <- unique(dat_all$case)
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aws <- unique(dat_all$artwork)[unique(dat_all$artwork) != "glossar"]
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max_trace <- max(dat_all$trace, na.rm = TRUE) + 1
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@ -545,7 +514,6 @@ for (case in cases) {
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max_trace <- max_trace + 1
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}
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if (nrow(tmp) > 0) {
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#print(tmp[, c("case", "event", "trace", "artwork")])
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out <- rbind(out, tmp)
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}
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}
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@ -554,15 +522,7 @@ for (case in cases) {
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# TODO: Get rid of the loops
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# --> This takes forever...
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#head(out[, c("time_ms.start", "case", "trace", "event", "artwork")], 55)
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#head(dat_all[dat_all$artwork %in% "501", c("time_ms.start", "case", "trace", "event", "artwork")], 50)
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# identical(dat_all[which(!dat_all$eventid %in% out$eventid), ],
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# dat_all[dat_all$artwork == "glossar", ])
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# --> TRUE
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# put glossar events back in
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# put glossar events back in --> not relevant anymore
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#dat_all <- rbind(out, dat_all[dat_all$artwork == "glossar", ])
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out <- out[order(out$date.start), ]
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@ -571,25 +531,10 @@ rownames(out) <- NULL
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# Make `trace` a consecutive number
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out$trace2 <- as.numeric(factor(out$trace, levels = unique(out$trace)))
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#head(out[, c("trace", "trace2")], 50)
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#' # Export data
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write.table(out, "../data/event_logfiles.csv",
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sep = ";", quote = FALSE, row.names = FALSE)
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# Is `artwork` my case? Or `artwork` per day? Or `artwork` per some other
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# unit??? Maybe look at differences between timestamps separately for
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# `artwork`? And identify "new observational unit" this way?
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#
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# Definition: (???)
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# 1. Touching a new `artwork` corresponds to "observational unit change"
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# 2. Time interval of XX min within one `artwork` on the same day
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# corresponds to "observational unit change"
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# Split data frame in list of data frame which all correspond to one
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# artwork
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# dat_art <- split(dat, dat$artwork)
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# TODO: Write function for closing events
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