596 lines
19 KiB
R
596 lines
19 KiB
R
#' ---
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#' title: "Preprocessing log files"
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#' author: "Nora Wickelmaier"
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#' date: "`r Sys.Date()`"
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#' output:
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#' html_document:
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#' toc: true
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#' toc_float: true
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#' pdf_document:
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#' toc: true
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#' number_sections: true
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#' geometry: margin = 2.5cm
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#' ---
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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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header = TRUE)
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dat0$date <- as.POSIXct(dat0$date) # create date object
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dat0$glossar <- ifelse(dat0$artwork == "glossar", 1, 0)
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#' # Remove irrelevant events
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#' ## Remove Start Application and Show Application
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dat <- subset(dat0, !(dat0$event %in% c("Start Application",
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"Show Application")))
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rownames(dat) <- NULL
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#' # Close events
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########
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#' Do it for Transform events first
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dat1 <- dat[dat$event %in% c("Transform start", "Transform stop"), ]
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dat1 <- dat1[order(dat1$artwork, dat1$date), ]
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rownames(dat1) <- NULL
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# Create event ID for closing events
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num_start <- diff(c(0, which(dat1$event == "Transform stop")))
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dat1$eventid <- rep(seq_along(num_start), num_start)
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head(dat1[, c("event", "eventid")], 25)
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table(table(dat1$eventid))
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# 1 2 3 4 5 6 7 8 10 11
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# 70 78435 5153 842 222 66 18 14 3 1
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# --> compare to table(num_start)!
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# Find out how often "Transform stop" follows each other
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num_stop <- c(diff(c(0, which(dat1$event == "Transform start"))))
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table(num_stop)
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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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# remove duplicated "Transform start" events
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dat1 <- dat1[!duplicated(dat1[, c("event", "eventid")]), ]
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# remove duplicated "Transform stop" events
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id_stop <- which(dat1$event == "Transform stop")
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id_rm_stop <- id_stop[diff(id_stop) == 1]
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dat1 <- dat1[-(id_rm_stop + 1), ]
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# transform to wide data format
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dat1$time <- ifelse(dat1$event == "Transform start", "start", "stop")
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trans_wide <- reshape(dat1, direction = "wide",
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idvar = c("eventid", "artwork", "glossar"),
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timevar = "time",
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drop = c("popup", "card", "event")
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)
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# TODO: This runs for quite some time
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# --> Is this more efficient with tidyr::pivot_wider?
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# --> when fileid is part of the reshape, it does not work correctly, since
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# we sometimes have a start - stop event that is recorded in two separate
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# log files, BUT: after finding out, that `time_ms` changes for each log
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# file, I want to exclude those cases, so `fileid` has to be included!!!
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# check how often an eventid is associated with two fileids
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nrow(subset(trans_wide, trans_wide$fileid.start != trans_wide$fileid.stop))
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# exclude from data set ??
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# trans_wide <- subset(trans_wide, trans_wide$fileid.start != trans_wide$fileid.stop)
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# which(is.na(trans_wide$date.start))
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trans_wide$event <- "move"
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trans_wide$eventid <- NULL
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rownames(trans_wide) <- NULL
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trans_wide$duration <- trans_wide$time_ms.stop - trans_wide$time_ms.start
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#trans_wide$duration <- trans_wide$date.stop - trans_wide$date.start
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# only seconds - not fine grained enough
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trans_wide$distance <- apply(
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trans_wide[, c("x.start", "y.start", "x.stop", "y.stop")], 1,
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function(x) dist(matrix(x, 2, 2, byrow = TRUE)))
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trans_wide$rotationDegree <- trans_wide$rotation.stop -
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trans_wide$rotation.start
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trans_wide$scaleSize <- trans_wide$scale.stop / trans_wide$scale.start
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trans_wide$trace <- NA
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trans_wide$card <- NA
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trans_wide$popup <- NA
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dat_trans <- trans_wide[trans_wide$distance != 0 &
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trans_wide$rotationDegree != 0 &
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trans_wide$scaleSize != 1,
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c("fileid.start", "fileid.stop", "event", "artwork",
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"trace", "glossar", "date.start", "date.stop",
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"time_ms.start", "time_ms.stop", "duration",
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"card", "popup", "x.start", "y.start", "x.stop",
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"y.stop", "distance", "scale.start", "scale.stop",
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"scaleSize", "rotation.start", "rotation.stop",
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"rotationDegree")]
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1 - nrow(dat_trans) / nrow(trans_wide)
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# removes almost 2/3 of the data (for small data set)
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rm(id_rm_stop, id_stop, trans_wide, num_start, num_stop)
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summary(dat_trans)
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#' # Close other events
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dat2 <- dat[!dat$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 (i in 1:nrow(dat2)) { # go through rows
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if (last_event == "Show Info" & dat2$artwork[i] == art) {
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dat2$trace[i] <- i
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j <- i
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} else if (last_event == "Show Front" & dat2$artwork[i] == art) {
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dat2$trace[i] <- j
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} else if (!(last_event %in% c("Show Info", "Show Front")) &
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dat2$artwork[i] == art) {
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dat2$trace[i] <- j
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}
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if (i <= nrow(dat2)) {
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last_event <- dat2$event[i + 1]
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}
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}
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}
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head(dat2[, c("artwork", "event", "trace")], 50)
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tail(dat2[, c("artwork", "event", "trace")], 50)
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rm(aws, i, j, last_event, art)
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## Fix glossar entries
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### Find artwork for glossar entry
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glossar_files <- unique(dat2[dat2$artwork == "glossar", "popup"])
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# load lookup table for artworks and glossar files
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load("../data/glossar_dict.RData")
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lut <- glossar_dict[glossar_dict$glossar_file %in% glossar_files, ]
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head(dat2[, c("artwork", "event", "popup", "trace")], 20)
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for (file in lut$glossar_file) {
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artwork_list <- unlist(lut[lut$glossar_file == file, "artwork"])
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for (i in seq_len(nrow(dat2))) {
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if (dat2$event[i] == "Show Info") {
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current_artwork <- dat2[i, "artwork"]
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j <- i
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k <- i
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} else {
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current_artwork <- current_artwork
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}
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if (dat2$event[i] == "Show Front" & dat2$artwork[i] == current_artwork) {
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# make sure artwork has not been closed, yet!
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k <- i
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}
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if (dat2$artwork[i] == "glossar" &
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(current_artwork %in% artwork_list) &
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dat2$popup[i] == file & (j-k == 0)) {
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dat2[i, "trace"] <- dat2[j, "trace"]
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dat2[i, "artwork"] <- current_artwork
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}
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}
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}
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# --> finds about half of the glossar entries for the small data set...
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table(is.na(dat2[dat2$glossar == 1, "trace"]))
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# REMEMBER: It can never bo 100% correct, since it is always possible that
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# several cards are open and that they link to the same glossar entry
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# How many glossar_files are only associated with one artwork?
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lut[sapply(lut$artwork, length) == 1, "glossar_file"]
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single <- lut[sapply(lut$artwork, length) == 1, "glossar_file"]
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tmp <- subset(dat2, is.na(dat2$trace))$popup
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inside <- unique(tmp[tmp %in% lut[sapply(lut$artwork, length) == 1, "glossar_file"]])
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single_art <- unlist(lut[lut$glossar_file %in% inside, "artwork"])
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tmp_lut <- data.frame(glossar_file = sort(inside), artwork = single_art)
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dat2[dat2$glossar == 1, c("artwork", "popup", "glossar", "trace")]
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for (file in tmp_lut$glossar_file) {
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for (i in seq_len(nrow(dat2))) {
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if (dat2$event[i] == "Artwork/OpenCard" & dat2$artwork[i] %in% tmp_lut$artwork) {
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current_artwork <- dat2[i, "artwork"]
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j <- i
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}
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if (dat2$artwork[i] == "glossar" &
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dat2$popup[i] == file) {
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dat2[i, "trace"] <- dat2[j, "trace"]
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dat2[i, "artwork"] <- current_artwork
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}
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}
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}
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dat2[14110:14130, ]
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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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df <- subset(dat2, !is.na(dat2$trace))
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df <- df[order(df$trace), ]
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rownames(df) <- NULL
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rm(lut, current_artwork, file, glossar_dict, i, j, k, artwork_list,
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glossar_files)
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#' ## Close flipCard
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dat3 <- subset(df, df$event %in% c("Show Info", "Show Front"))
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dat3$time <- ifelse(dat3$event == "Show Info", "start", "stop")
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flipCard_wide <- reshape(dat3, direction = "wide",
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idvar = c("trace", "artwork", "glossar"),
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timevar = "time",
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drop = c("popup", "card"))
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flipCard_wide$event <- "flipCard"
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flipCard_wide$duration <- flipCard_wide$time_ms.stop -
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flipCard_wide$time_ms.start
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flipCard_wide$card <- NA
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flipCard_wide$popup <- NA
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flipCard_wide$x.start <- NA
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flipCard_wide$x.stop <- NA
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flipCard_wide$y.start <- NA
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flipCard_wide$y.stop <- NA
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flipCard_wide$distance <- NA
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flipCard_wide$scale.start <- NA
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flipCard_wide$scale.stop <- NA
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flipCard_wide$scaleSize <- NA
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flipCard_wide$rotation.start <- NA
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flipCard_wide$rotation.stop <- NA
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flipCard_wide$rotationDegree <- NA
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dat_flipCard <- flipCard_wide[, c("fileid.start", "fileid.stop", "event",
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"artwork", "trace", "glossar",
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"date.start", "date.stop",
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"time_ms.start", "time_ms.stop",
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"duration", "card", "popup", "x.start",
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"y.start", "x.stop", "y.stop",
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"distance", "scale.start", "scale.stop",
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"scaleSize", "rotation.start",
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"rotation.stop", "rotationDegree")]
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rm(flipCard_wide)
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#' ## Close openTopic
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dat4 <- subset(df, df$event %in% c("Artwork/OpenCard", "Artwork/CloseCard"))
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dat4 <- dat4[order(dat4$artwork, dat4$date), ]
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rownames(dat4) <- NULL
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num_start <- diff(c(0, which(dat4$event == "Artwork/CloseCard")))
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dat4$eventid <- rep(seq_along(num_start), num_start)
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dat4$time <- ifelse(dat4$event == "Artwork/OpenCard", "start", "stop")
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openTopic_wide <- reshape(dat4, direction = "wide",
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idvar = c("eventid", "trace", "glossar", "artwork", "card"),
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timevar = "time", drop = "popup")
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openTopic_wide$event <- "openTopic"
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openTopic_wide$duration <- openTopic_wide$time_ms.stop -
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openTopic_wide$time_ms.start
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openTopic_wide$popup <- NA
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openTopic_wide$x.start <- NA
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openTopic_wide$x.stop <- NA
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openTopic_wide$y.start <- NA
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openTopic_wide$y.stop <- NA
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openTopic_wide$distance <- NA
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openTopic_wide$scale.start <- NA
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openTopic_wide$scale.stop <- NA
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openTopic_wide$scaleSize <- NA
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openTopic_wide$rotation.start <- NA
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openTopic_wide$rotation.stop <- NA
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openTopic_wide$rotationDegree <- NA
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dat_openTopic <- openTopic_wide[, c("fileid.start", "fileid.stop", "event",
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"artwork", "trace", "glossar",
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"date.start", "date.stop",
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"time_ms.start", "time_ms.stop",
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"duration", "card", "popup", "x.start",
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"y.start", "x.stop", "y.stop",
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"distance", "scale.start",
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"scale.stop", "scaleSize",
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"rotation.start", "rotation.stop",
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"rotationDegree")]
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# TODO: card should have a unique identifier for each artwork
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rm(openTopic_wide, num_start)
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#' ## close openPopup
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dat5 <- subset(df, df$event %in% c("ShowPopup", "HidePopup"))
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dat5 <- dat5[order(dat5$artwork, dat5$popup, dat5$date), ]
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rownames(dat5) <- NULL
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num_start <- diff(c(0, which(dat5$event == "HidePopup")))
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# last event is "ShowPopup"! Needs to be fixed
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# num_start <- c(num_start, 1)
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# TODO: Needs to be caught in a function --> not anymore - still relevant???
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dat5$eventid <- rep(seq_along(num_start), num_start)
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dat5$time <- ifelse(dat5$event == "ShowPopup", "start", "stop")
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openPopup_wide <- reshape(dat5, direction = "wide",
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idvar = c("eventid", "trace", "glossar", "artwork", "popup"),
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timevar = "time", drop = "card")
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# there is a pathological entry which gets deleted...
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# df[df$trace == 4595, ]
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# --> artwork 046 popup selene.xml gets opened twice
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openPopup_wide$event <- "openPopup"
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openPopup_wide$duration <- openPopup_wide$time_ms.stop -
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openPopup_wide$time_ms.start
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openPopup_wide$card <- NA
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openPopup_wide$x.start <- NA
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openPopup_wide$x.stop <- NA
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openPopup_wide$y.start <- NA
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openPopup_wide$y.stop <- NA
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openPopup_wide$distance <- NA
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openPopup_wide$scale.start <- NA
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openPopup_wide$scale.stop <- NA
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openPopup_wide$scaleSize <- NA
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openPopup_wide$rotation.start <- NA
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openPopup_wide$rotation.stop <- NA
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openPopup_wide$rotationDegree <- NA
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dat_openPopup <- openPopup_wide[, c("fileid.start", "fileid.stop", "event",
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"artwork", "trace", "glossar",
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"date.start", "date.stop",
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"time_ms.start", "time_ms.stop",
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"duration", "card", "popup", "x.start",
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"y.start", "x.stop", "y.stop",
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"distance", "scale.start",
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"scale.stop", "scaleSize",
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"rotation.start", "rotation.stop",
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"rotationDegree")]
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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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dat_all <- rbind(dat_trans, dat_flipCard, dat_openTopic, dat_openPopup)
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# check
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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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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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df[df$trace == 1843, ]
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dat_openPopup[dat_openPopup$trace == 1843, ]
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## --> still 50 (small data set) left, and some really do not seem to be
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## opened! Must be a log error
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# --> others should be closed!
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dat[31000:31019,] # this one e.g.
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# --> Actually NOT! card gets flipped before! Again - log error!
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# Remove durations when event spans more than one log file, since they are
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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$date.start), ]
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rownames(dat_all) <- NULL
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ind <- rowSums(is.na(dat_all)) == ncol(dat_all)
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any(ind)
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dat_all[ind, ]
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# --> No rows with only NA, as it should be.
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summary(dat_all) # OK, this actually makes a lot of sense :)
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#' ## Create case variable
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#dat_all$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$timediff < 40], breaks = 50)
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|
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# TODO: What is the best choice for the cutoff here? I took 20 secs for now
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dat_all$case <- NA
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j <- 1
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for (i in seq_len(nrow(dat_all))) {
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if (dat_all$timediff[i] < 21) {
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dat_all$case[i] <- j
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} else {
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j <- j + 1
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dat_all$case[i] <- j
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}
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}
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head(dat_all[, c("event", "artwork", "trace", "date.start", "timediff", "case")], 100)
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#' ## Add event ID
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dat_all$eventid <- seq_len(nrow(dat_all))
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|
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dat_all <- dat_all[, c("fileid.start", "fileid.stop", "eventid", "case",
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"trace", "glossar", "event", "artwork",
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|
"date.start", "date.stop", "time_ms.start",
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|
"time_ms.stop", "duration", "card", "popup",
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|
"x.start", "y.start", "x.stop", "y.stop",
|
|
"distance", "scale.start", "scale.stop",
|
|
"scaleSize", "rotation.start", "rotation.stop",
|
|
"rotationDegree")]
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|
|
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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
|
|
# --> assign it to all `move` events for that case and artwork
|
|
# when case and artwork are identical and there is more than 1 trace value
|
|
# --> assign the `trace` value that was right before this `move` event
|
|
# (could, of course, also be after)
|
|
|
|
cases <- unique(dat_all$case)
|
|
aws <- unique(dat_all$artwork)[unique(dat_all$artwork) != "glossar"]
|
|
max_trace <- max(dat_all$trace, na.rm = TRUE) + 1
|
|
out <- NULL
|
|
|
|
for (case in cases) {
|
|
for (art in aws) {
|
|
tmp <- dat_all[dat_all$case == case & dat_all$artwork == art, ]
|
|
if (nrow(tmp) != 0) {
|
|
|
|
if (length(na.omit(unique(tmp$trace))) == 1) {
|
|
tmp[tmp$event == "move", "trace"] <- na.omit(unique(tmp$trace))
|
|
} else if (length(na.omit(unique(tmp$trace))) > 1) {
|
|
for (i in 1:nrow(tmp)) {
|
|
if (tmp$event[i] == "move") {
|
|
if (i == 1) {
|
|
tmp$trace[i] <- na.omit(unique(tmp$trace))[1]
|
|
} else {
|
|
tmp$trace[i] <- tmp$trace[i - 1]
|
|
}
|
|
}
|
|
}
|
|
} else if (all(is.na(tmp$trace))) {
|
|
for (i in 1:nrow(tmp)) {
|
|
if (tmp$event[i] == "move") {
|
|
tmp$trace[i] <- max_trace
|
|
}
|
|
}
|
|
}
|
|
max_trace <- max_trace + 1
|
|
}
|
|
if (nrow(tmp) > 0) {
|
|
#print(tmp[, c("case", "event", "trace", "artwork")])
|
|
out <- rbind(out, tmp)
|
|
}
|
|
}
|
|
}
|
|
|
|
# TODO: Get rid of the loops
|
|
# --> This takes forever...
|
|
|
|
#head(out[, c("time_ms.start", "case", "trace", "event", "artwork")], 55)
|
|
|
|
#head(dat_all[dat_all$artwork %in% "501", c("time_ms.start", "case", "trace", "event", "artwork")], 50)
|
|
|
|
# identical(dat_all[which(!dat_all$eventid %in% out$eventid), ],
|
|
# dat_all[dat_all$artwork == "glossar", ])
|
|
# --> TRUE
|
|
|
|
# put glossar events back in
|
|
|
|
#dat_all <- rbind(out, dat_all[dat_all$artwork == "glossar", ])
|
|
out <- out[order(out$date.start), ]
|
|
rownames(out) <- NULL
|
|
|
|
# Make `trace` a consecutive number
|
|
out$trace2 <- as.numeric(factor(out$trace, levels = unique(out$trace)))
|
|
|
|
#head(out[, c("trace", "trace2")], 50)
|
|
|
|
#' # Export data
|
|
|
|
write.table(out, "../data/event_logfiles.csv",
|
|
sep = ";", quote = FALSE, row.names = FALSE)
|
|
|
|
# Is `artwork` my case? Or `artwork` per day? Or `artwork` per some other
|
|
# unit??? Maybe look at differences between timestamps separately for
|
|
# `artwork`? And identify "new observational unit" this way?
|
|
#
|
|
# Definition: (???)
|
|
# 1. Touching a new `artwork` corresponds to "observational unit change"
|
|
# 2. Time interval of XX min within one `artwork` on the same day
|
|
# corresponds to "observational unit change"
|
|
|
|
# Split data frame in list of data frame which all correspond to one
|
|
# artwork
|
|
# dat_art <- split(dat, dat$artwork)
|
|
|
|
# TODO: Write function for closing events
|
|
|