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mtt_haum/code/02_preprocessing.R

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#' ---
#' title: "Preprocessing log files"
#' author: "Nora Wickelmaier"
#' date: "`r Sys.Date()`"
#' output:
#' html_document:
#' toc: true
#' toc_float: true
#' pdf_document:
#' toc: true
#' number_sections: true
#' geometry: margin = 2.5cm
#' ---
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code")
# LogEntry classes:
# TRANSFORM_START: "Transform start" --> "Transformation Start" in Tool
# TRANSFORM_STOP: "Transform stop"
# START_APPLICATION: "Start Application"
# SHOW_APPLICATION: "Show Application"
# SHOW_INFO: "Show Info" --> "Flip Card" in Tool
# SHOW_FRONT: "Show Front"
# SHOW_POPUP: "ShowPopup" --> "Show Popup" in Tool
# HIDE_POPUP: "HidePopup"
# ARTWORK: "Artwork" --> "Show Topic" in Tool
#' # Read data
dat0 <- read.table("../data/rawdata_logfiles_small.csv", sep = ";",
header = TRUE)
dat0$date <- as.POSIXct(dat0$date) # create date object
dat0$glossar <- ifelse(dat0$artwork == "glossar", 1, 0)
#' # Remove irrelevant events
#' ## Remove Start Application and Show Application
dat <- subset(dat0, !(dat0$event %in% c("Start Application",
"Show Application")))
rownames(dat) <- NULL
#' # Close events
########
#' Do it for Transform events first
dat1 <- dat[dat$event %in% c("Transform start", "Transform stop"), ]
dat1 <- dat1[order(dat1$artwork, dat1$date), ]
rownames(dat1) <- NULL
# Create event ID for closing events
num_start <- diff(c(0, which(dat1$event == "Transform stop")))
dat1$eventid <- rep(seq_along(num_start), num_start)
head(dat1[, c("event", "eventid")], 25)
table(table(dat1$eventid))
# 1 2 3 4 5 6 7 8 10 11
# 70 78435 5153 842 222 66 18 14 3 1
# --> compare to table(num_start)!
# Find out how often "Transform stop" follows each other
num_stop <- c(diff(c(0, which(dat1$event == "Transform start"))))
table(num_stop)
dat1$eventrep <- rep(num_start, num_start)
dat1$dupl <- duplicated(dat1[, c("event", "eventid")]) # keep first
dat1$dupl <- duplicated(dat1[, c("event", "eventid")], fromLast = TRUE) # keep last
dat1[dat1$eventrep == 10, ]
dat1$dupl <- NULL
dat1$eventrep <- NULL
# remove duplicated "Transform start" events
dat1 <- dat1[!duplicated(dat1[, c("event", "eventid")]), ]
# remove duplicated "Transform stop" events
id_stop <- which(dat1$event == "Transform stop")
id_rm_stop <- id_stop[diff(id_stop) == 1]
dat1 <- dat1[-(id_rm_stop + 1), ]
# transform to wide data format
dat1$time <- ifelse(dat1$event == "Transform start", "start", "stop")
trans_wide <- reshape(dat1, direction = "wide",
idvar = c("eventid", "artwork", "glossar"),
timevar = "time",
drop = c("popup", "card", "event")
)
# TODO: This runs for quite some time
# --> Is this more efficient with tidyr::pivot_wider?
# --> when fileid is part of the reshape, it does not work correctly, since
# we sometimes have a start - stop event that is recorded in two separate
# log files, BUT: after finding out, that `time_ms` changes for each log
# file, I want to exclude those cases, so `fileid` has to be included!!!
# check how often an eventid is associated with two fileids
nrow(subset(trans_wide, trans_wide$fileid.start != trans_wide$fileid.stop))
# exclude from data set ??
# trans_wide <- subset(trans_wide, trans_wide$fileid.start != trans_wide$fileid.stop)
# which(is.na(trans_wide$date.start))
trans_wide$event <- "move"
trans_wide$eventid <- NULL
rownames(trans_wide) <- NULL
trans_wide$duration <- trans_wide$time_ms.stop - trans_wide$time_ms.start
#trans_wide$duration <- trans_wide$date.stop - trans_wide$date.start
# only seconds - not fine grained enough
trans_wide$distance <- apply(
trans_wide[, c("x.start", "y.start", "x.stop", "y.stop")], 1,
function(x) dist(matrix(x, 2, 2, byrow = TRUE)))
trans_wide$rotationDegree <- trans_wide$rotation.stop -
trans_wide$rotation.start
trans_wide$scaleSize <- trans_wide$scale.stop / trans_wide$scale.start
trans_wide$trace <- NA
trans_wide$card <- NA
trans_wide$popup <- NA
dat_trans <- trans_wide[trans_wide$distance != 0 &
trans_wide$rotationDegree != 0 &
trans_wide$scaleSize != 1,
c("fileid.start", "fileid.stop", "event", "artwork",
"trace", "glossar", "date.start", "date.stop",
"time_ms.start", "time_ms.stop", "duration",
"card", "popup", "x.start", "y.start", "x.stop",
"y.stop", "distance", "scale.start", "scale.stop",
"scaleSize", "rotation.start", "rotation.stop",
"rotationDegree")]
1 - nrow(dat_trans) / nrow(trans_wide)
# removes almost 2/3 of the data (for small data set)
rm(id_rm_stop, id_stop, trans_wide, num_start, num_stop)
summary(dat_trans)
#' # Close other events
dat2 <- dat[!dat$event %in% c("Transform start", "Transform stop"), ]
# dat2$x <- NULL
# dat2$y <- NULL
# dat2$scale <- NULL
# dat2$rotation <- NULL
rownames(dat2) <- NULL
# Create event ID for closing events
# num_start <- diff(c(0, which(dat2$event == "Show Front")))
# dat2$trace <- rep(seq_along(num_start), num_start)
# head(dat2[, c("artwork", "event", "trace")], 50)
# --> does not work because of glossar entries... can't sort by artwork
dat2$trace <- NA
last_event <- dat2$event[1]
aws <- unique(dat2$artwork)[unique(dat2$artwork) != "glossar"]
#
for (art in aws) { # select artwork
for (i in 1:nrow(dat2)) { # go through rows
if (last_event == "Show Info" & dat2$artwork[i] == art) {
dat2$trace[i] <- i
j <- i
} else if (last_event == "Show Front" & dat2$artwork[i] == art) {
dat2$trace[i] <- j
} else if (!(last_event %in% c("Show Info", "Show Front")) &
dat2$artwork[i] == art) {
dat2$trace[i] <- j
}
if (i <= nrow(dat2)) {
last_event <- dat2$event[i + 1]
}
}
}
head(dat2[, c("artwork", "event", "trace")], 50)
tail(dat2[, c("artwork", "event", "trace")], 50)
# TODO: How to handle popups from glossar???
rm(aws, i, j, last_event, art)
## Fix glossar entries
### Find artwork for glossar entry
glossar_files <- unique(dat2[dat2$artwork == "glossar", "popup"])
# load lookup table for artworks and glossar files
load("../data/glossar_dict.RData")
lut <- glossar_dict[glossar_dict$glossar_file %in% glossar_files, ]
head(dat2[, c("artwork", "event", "popup", "trace")], 20)
for (file in lut$glossar_file) {
artwork_list <- unlist(lut[lut$glossar_file == file, "artwork"])
for (i in seq_len(nrow(dat2))) {
if (dat2$event[i] == "Show Info") {
current_artwork <- dat2[i, "artwork"]
j <- i
k <- i
} else {
current_artwork <- current_artwork
}
if (dat2$event[i] == "Show Front" & dat2$artwork[i] == current_artwork) {
# make sure artwork has not been closed, yet!
k <- i
}
if (dat2$artwork[i] == "glossar" &
(current_artwork %in% artwork_list) &
dat2$popup[i] == file & (j-k == 0)) {
dat2[i, "trace"] <- dat2[j, "trace"]
dat2[i, "artwork"] <- current_artwork
}
}
}
# --> finds about half of the glossar entries for the small data set...
table(is.na(dat2[dat2$glossar == 1, "trace"]))
# REMEMBER: It can never bo 100% correct, since it is always possible that
# several cards are open and that they link to the same glossar entry
# How many glossar_files are only associated with one artwork?
lut[sapply(lut$artwork, length) == 1, "glossar_file"]
# TODO: Fill in the ones that are associated with one artwork
# --> Can't come up with something -- maybe ask AK???
single <- lut[sapply(lut$artwork, length) == 1, "glossar_file"]
tmp <- subset(dat2, is.na(dat2$trace))$popup
inside <- unique(tmp[tmp %in% lut[sapply(lut$artwork, length) == 1, "glossar_file"]])
single_art <- unlist(lut[lut$glossar_file %in% inside, "artwork"])
tmp_lut <- data.frame(glossar_file = sort(inside), artwork = single_art)
dat2[dat2$glossar == 1, c("artwork", "popup", "glossar", "trace")]
for (file in tmp_lut$glossar_file) {
for (i in seq_len(nrow(dat2))) {
if (dat2$event[i] == "Artwork/OpenCard" & dat2$artwork[i] %in% tmp_lut$artwork) {
current_artwork <- dat2[i, "artwork"]
j <- i
}
if (dat2$artwork[i] == "glossar" &
dat2$popup[i] == file) {
dat2[i, "trace"] <- dat2[j, "trace"]
dat2[i, "artwork"] <- current_artwork
}
}
}
dat2[14110:14130, ]
# TODO: Integrate for loop into for loop above
# TODO: "glossar" entry should be changed to the corresponding artwork
# TODO: For now: Exclude not matched glossar entries
df <- subset(dat2, !is.na(dat2$trace))
df <- df[order(df$trace), ]
rownames(df) <- NULL
rm(lut, current_artwork, file, glossar_dict, i, j, k, artwork_list,
glossar_files)
#' ## Close flipCard
dat3 <- subset(df, df$event %in% c("Show Info", "Show Front"))
dat3$time <- ifelse(dat3$event == "Show Info", "start", "stop")
flipCard_wide <- reshape(dat3, direction = "wide",
idvar = c("trace", "artwork", "glossar"),
timevar = "time",
drop = c("popup", "card"))
flipCard_wide$event <- "flipCard"
flipCard_wide$duration <- flipCard_wide$time_ms.stop -
flipCard_wide$time_ms.start
flipCard_wide$card <- NA
flipCard_wide$popup <- NA
flipCard_wide$x.start <- NA
flipCard_wide$x.stop <- NA
flipCard_wide$y.start <- NA
flipCard_wide$y.stop <- NA
flipCard_wide$distance <- NA
flipCard_wide$scale.start <- NA
flipCard_wide$scale.stop <- NA
flipCard_wide$scaleSize <- NA
flipCard_wide$rotation.start <- NA
flipCard_wide$rotation.stop <- NA
flipCard_wide$rotationDegree <- NA
dat_flipCard <- flipCard_wide[, c("fileid.start", "fileid.stop", "event",
"artwork", "trace", "glossar",
"date.start", "date.stop",
"time_ms.start", "time_ms.stop",
"duration", "card", "popup", "x.start",
"y.start", "x.stop", "y.stop",
"distance", "scale.start", "scale.stop",
"scaleSize", "rotation.start",
"rotation.stop", "rotationDegree")]
rm(flipCard_wide)
#' ## Close openTopic
dat4 <- subset(df, df$event %in% c("Artwork/OpenCard", "Artwork/CloseCard"))
dat4 <- dat4[order(dat4$artwork, dat4$date), ]
rownames(dat4) <- NULL
num_start <- diff(c(0, which(dat4$event == "Artwork/CloseCard")))
dat4$eventid <- rep(seq_along(num_start), num_start)
dat4$time <- ifelse(dat4$event == "Artwork/OpenCard", "start", "stop")
openTopic_wide <- reshape(dat4, direction = "wide",
idvar = c("eventid", "trace", "glossar", "artwork", "card"),
timevar = "time", drop = "popup")
openTopic_wide$event <- "openTopic"
openTopic_wide$duration <- openTopic_wide$time_ms.stop -
openTopic_wide$time_ms.start
openTopic_wide$popup <- NA
openTopic_wide$x.start <- NA
openTopic_wide$x.stop <- NA
openTopic_wide$y.start <- NA
openTopic_wide$y.stop <- NA
openTopic_wide$distance <- NA
openTopic_wide$scale.start <- NA
openTopic_wide$scale.stop <- NA
openTopic_wide$scaleSize <- NA
openTopic_wide$rotation.start <- NA
openTopic_wide$rotation.stop <- NA
openTopic_wide$rotationDegree <- NA
dat_openTopic <- openTopic_wide[, c("fileid.start", "fileid.stop", "event",
"artwork", "trace", "glossar",
"date.start", "date.stop",
"time_ms.start", "time_ms.stop",
"duration", "card", "popup", "x.start",
"y.start", "x.stop", "y.stop",
"distance", "scale.start",
"scale.stop", "scaleSize",
"rotation.start", "rotation.stop",
"rotationDegree")]
# TODO: card should have a unique identifier for each artwork
rm(openTopic_wide, num_start)
#' ## close openPopup
dat5 <- subset(df, df$event %in% c("ShowPopup", "HidePopup"))
dat5 <- dat5[order(dat5$artwork, dat5$popup, dat5$date), ]
rownames(dat5) <- NULL
num_start <- diff(c(0, which(dat5$event == "HidePopup")))
# last event is "ShowPopup"! Needs to be fixed
# num_start <- c(num_start, 1)
# TODO: Needs to be caught in a function --> not anymore - still relevant???
dat5$eventid <- rep(seq_along(num_start), num_start)
dat5$time <- ifelse(dat5$event == "ShowPopup", "start", "stop")
openPopup_wide <- reshape(dat5, direction = "wide",
idvar = c("eventid", "trace", "glossar", "artwork", "popup"),
timevar = "time", drop = "card")
# there is a pathological entry which gets deleted...
# df[df$trace == 4595, ]
# --> artwork 046 popup selene.xml gets opened twice
openPopup_wide$event <- "openPopup"
openPopup_wide$duration <- openPopup_wide$time_ms.stop -
openPopup_wide$time_ms.start
openPopup_wide$card <- NA
openPopup_wide$x.start <- NA
openPopup_wide$x.stop <- NA
openPopup_wide$y.start <- NA
openPopup_wide$y.stop <- NA
openPopup_wide$distance <- NA
openPopup_wide$scale.start <- NA
openPopup_wide$scale.stop <- NA
openPopup_wide$scaleSize <- NA
openPopup_wide$rotation.start <- NA
openPopup_wide$rotation.stop <- NA
openPopup_wide$rotationDegree <- NA
dat_openPopup <- openPopup_wide[, c("fileid.start", "fileid.stop", "event",
"artwork", "trace", "glossar",
"date.start", "date.stop",
"time_ms.start", "time_ms.stop",
"duration", "card", "popup", "x.start",
"y.start", "x.stop", "y.stop",
"distance", "scale.start",
"scale.stop", "scaleSize",
"rotation.start", "rotation.stop",
"rotationDegree")]
rm(num_start, openPopup_wide)
# Merge all
# system.time({
# dat_all <- merge(dat_trans, dat_flipCard, all = TRUE)
# dat_all <- merge(dat_all, dat_openTopic, all = TRUE)
# dat_all <- merge(dat_all, dat_openPopup, all = TRUE)
# })
#
# # check
# nrow(dat_all) == (nrow(dat_trans) + nrow(dat_flipCard) +
# nrow(dat_openTopic) + nrow(dat_openPopup))
#
# dat_all <- dat_all[order(dat_all$date.start), ]
# rownames(dat_all) <- NULL
#
# TODO: from here on NA... WHY??
# dat_all[19426:19435, ]
# TODO: Should card maybe also be filled in for "openPopup"?
# dat_all2 <- dplyr::full_join(dat_trans, dat_flipCard)
# dat_all2 <- dplyr::full_join(dat_all, dat_openTopic)
# dat_all2 <- dplyr::full_join(dat_all, dat_openPopup)
#
# nrow(dat_all2) == (nrow(dat_trans) + nrow(dat_flipCard) +
# nrow(dat_openTopic) + nrow(dat_openPopup))
#
# dat_all2 <- dat_all2[order(dat_all2$date.start), ]
# rownames(dat_all2) <- NULL
# TODO: --> same result - but faster. Need it?
# --> Would hate to depend on dplyr...
#' ## Use `rbind()` instead...
# --> unbeatable in terms of time!
dat_all <- rbind(dat_trans, dat_flipCard, dat_openTopic, dat_openPopup)
# check
nrow(dat_all) == (nrow(dat_trans) + nrow(dat_flipCard) +
nrow(dat_openTopic) + nrow(dat_openPopup))
# remove all events that do not have a `date.start`
dim(dat_all[is.na(dat_all$date.start), ])
dat_all <- dat_all[!is.na(dat_all$date.start), ]
# There is only a `date.stop`, when event is not properly closed, see here:
df[df$trace == 1843, ]
dat_openPopup[dat_openPopup$trace == 1843, ]
## --> still 50 (small data set) left, and some really do not seem to be
## opened! Must be a log error
# --> others should be closed!
dat[31000:31019,] # this one e.g.
# --> Actually NOT! card gets flipped before! Again - log error!
# Remove durations when event spans more than one log file, since they are
# not interpretable
dat_all[which(dat_all$fileid.start != dat_all$fileid.stop), "duration"] <- NA
# sort by `start.date`
dat_all <- dat_all[order(dat_all$date.start), ]
rownames(dat_all) <- NULL
ind <- rowSums(is.na(dat_all)) == ncol(dat_all)
any(ind)
dat_all[ind, ]
# --> No rows with only NA, as it should be.
summary(dat_all) # OK, this actually makes a lot of sense :)
#' ## Create case variable
#dat_all$timediff <- as.numeric(dat_all$date.stop - dat_all$date.start)
dat_all$timediff <- as.numeric(diff(c(dat_all$date.start[1], dat_all$date.start)))
hist(dat_all$timediff[dat_all$timediff < 40], breaks = 50)
# TODO: What is the best choice for the cutoff here? I took 20 secs for now
dat_all$case <- NA
j <- 1
for (i in seq_len(nrow(dat_all))) {
if (dat_all$timediff[i] < 21) {
dat_all$case[i] <- j
} else {
j <- j + 1
dat_all$case[i] <- j
}
}
head(dat_all[, c("event", "artwork", "trace", "date.start", "timediff", "case")], 100)
#' ## Add event ID
dat_all$eventid <- seq_len(nrow(dat_all))
dat_all <- dat_all[, c("fileid.start", "fileid.stop", "eventid", "case",
"trace", "glossar", "event", "artwork",
"date.start", "date.stop", "time_ms.start",
"time_ms.stop", "duration", "card", "popup",
"x.start", "y.start", "x.stop", "y.stop",
"distance", "scale.start", "scale.stop",
"scaleSize", "rotation.start", "rotation.stop",
"rotationDegree")]
#' ## Add `trace` numbers for `move` events
# 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
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