627 lines
20 KiB
R
627 lines
20 KiB
R
#' ---
|
|
#' 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
|
|
|
|
#' # Remove irrelevant events
|
|
|
|
#' ## Remove Start Application and Show Application
|
|
|
|
dat <- subset(dat0, !(dat0$event %in% c("Start Application",
|
|
"Show Application")))
|
|
|
|
#' # Close events
|
|
|
|
########
|
|
|
|
#' Do it for Transform events first
|
|
dat1 <- dat[dat$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$eventid))
|
|
# 1 2 3 4 5 6 7 8 10 11
|
|
# 73 78429 5156 842 222 66 18 14 3 1
|
|
# --> compare to table(num_start)!
|
|
|
|
# Find out how often "Transform stop" follows each other
|
|
num_stop <- c(diff(c(0, which(dat1$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$event == "Transform stop")
|
|
id_rm_stop <- id_stop[diff(id_stop) == 1]
|
|
|
|
dat1 <- dat1[-(id_rm_stop + 1), ]
|
|
|
|
# transform to wide data format
|
|
dat1$event <- ifelse(dat1$event == "Transform start", "start", "stop")
|
|
|
|
trans_wide <- reshape(dat1, direction = "wide",
|
|
idvar = c("eventid", "artwork"),
|
|
timevar = "event", drop = c("fileid", "popup", "card")
|
|
)
|
|
# --> when fileid is part of the reshape, it does not work correctly, since
|
|
# we sometimes have a start - stop event that is recorded in two separate
|
|
# log files
|
|
# TODO: This runs for quite some time
|
|
# --> Is this more efficient with tidyr::pivot_wider?
|
|
|
|
# which(is.na(trans_wide$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$scaleSize != 1,
|
|
c("event", "artwork", "trace", "date.start", "date.stop",
|
|
"time_ms.start", "time_ms.stop", "duration",
|
|
"card", "popup",
|
|
"x.start", "y.start", "x.stop", "y.stop",
|
|
"distance", "scale.start", "scale.stop",
|
|
"scaleSize", "rotation.start", "rotation.stop",
|
|
"rotationDegree")]
|
|
# removes almost 2/3 of the data (for small data set)
|
|
|
|
rm(id_rm_stop, id_stop, trans_wide, num_start, num_stop)
|
|
|
|
summary(dat_trans)
|
|
|
|
# TODO: Ask Phillip what is wrong with `time_ms`
|
|
# --> Hat er eine Erklärung dafür?
|
|
|
|
#plot(time_ms.stop ~ time_ms.start, dat_trans, type = "b")
|
|
plot(time_ms.stop ~ time_ms.start, dat_trans,
|
|
col = rgb(red = 0, green = 0, blue = 0, alpha = 0.2))
|
|
|
|
plot(date.stop ~ date.start, dat_trans[1:1000,], type = "b")
|
|
|
|
|
|
|
|
|
|
#' # Close other events
|
|
|
|
dat2 <- dat[!dat$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$glossar_file %in% glossar_files, ]
|
|
|
|
head(dat2[, c("artwork", "event", "popup", "trace")], 20)
|
|
|
|
|
|
#df <- NULL
|
|
|
|
for (file in lut$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)) {
|
|
|
|
#df <- rbind(df, data.frame(file, current_artwork, i, j))
|
|
dat2[i, "trace"] <- dat2[j, "trace"]
|
|
|
|
}
|
|
}
|
|
}
|
|
# dim(dat2[is.na(dat2$trace), ])
|
|
# --> finds about half of the glossar entries for the small data set...
|
|
|
|
# dat2[apply(df[, c("j", "i")], 1, c), c("artwork", "event", "popup", "trace")]
|
|
|
|
# REMEMBER: It can never bo 100% correct, since it is always possible that
|
|
# several cards are open and that they link to the same glossar entry
|
|
|
|
# How many glossar_files are only associated with one artwork?
|
|
lut[sapply(lut$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???
|
|
|
|
# TODO: How to check if one of the former "Show Infos" is correct
|
|
# --> Can't come up with something -- maybe ask AK???
|
|
|
|
# for (file in lut$glossar_file) {
|
|
#
|
|
# artwork_list <- unlist(lut[lut$glossar_file == file, "artwork"])
|
|
#
|
|
# for (i in seq_len(nrow(dat2))) {
|
|
#
|
|
# if (dat2$event[i] == "Show Info") {
|
|
#
|
|
# artworks <- NULL
|
|
# current_artwork <- dat2[i, "artwork"]
|
|
# j <- i
|
|
#
|
|
# } else {
|
|
#
|
|
# print(current_artwork)
|
|
# artworks <- c(artworks, dat2[i, "artwork"])
|
|
# print(artworks)
|
|
#
|
|
# }
|
|
#
|
|
# # if (dat2$artwork[i] == "glossar" &
|
|
# # (current_artwork %in% artwork_list) &
|
|
# # dat2$popup[i] == file) {
|
|
# #
|
|
# # #df <- rbind(df, data.frame(file, current_artwork, i, j))
|
|
# # dat2[i, "trace"] <- dat2[j, "trace"]
|
|
#
|
|
# # }
|
|
# }
|
|
# }
|
|
|
|
# correct: 17940
|
|
# incorrect: 17963
|
|
|
|
# TODO: "glossar" entry should be changed to the corresponding artwork
|
|
# TODO: Add additional variable `glossar` with 0/1 or similar instead
|
|
|
|
# TODO: For now: Exclude not matched glossar entries
|
|
|
|
df <- subset(dat2, !is.na(dat2$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$event <- ifelse(dat3$event == "Show Info", "start", "stop")
|
|
|
|
flipCard_wide <- reshape(dat3, direction = "wide",
|
|
idvar = c("trace", "artwork"),
|
|
timevar = "event",
|
|
drop = c("fileid", "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$rotationDegree <- NA
|
|
|
|
dat_flipCard <- flipCard_wide[, c("event", "artwork", "trace",
|
|
"date.start", "date.stop",
|
|
"time_ms.start", "time_ms.stop",
|
|
"duration", "card", "popup",
|
|
"x.start", "y.start", "x.stop", "y.stop",
|
|
"distance", "scale.start", "scale.stop",
|
|
"scaleSize", "rotation.start",
|
|
"rotation.stop", "rotationDegree")]
|
|
|
|
rm(flipCard_wide)
|
|
|
|
#' ## Close openTopic
|
|
|
|
dat4 <- subset(df, df$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$event <- ifelse(dat4$event == "Artwork/OpenCard", "start", "stop")
|
|
|
|
openTopic_wide <- reshape(dat4, direction = "wide",
|
|
idvar = c("eventid", "trace", "artwork", "card"),
|
|
timevar = "event", drop = c("fileid", "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("event", "artwork", "trace",
|
|
"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$date), ]
|
|
rownames(dat5) <- NULL
|
|
|
|
num_start <- diff(c(0, which(dat5$event == "HidePopup")))
|
|
# last event is "ShowPopup"! Needs to be fixed
|
|
num_start <- c(num_start, 1)
|
|
# TODO: Needs to be caught in a function
|
|
|
|
dat5$eventid <- rep(seq_along(num_start), num_start)
|
|
|
|
dat5$event <- ifelse(dat5$event == "ShowPopup", "start", "stop")
|
|
|
|
openPopup_wide <- reshape(dat5, direction = "wide",
|
|
idvar = c("eventid", "trace", "artwork", "popup"),
|
|
timevar = "event", drop = c("fileid", "card"))
|
|
# there is a pathological entry which gets deleted...
|
|
# df[df$trace == 4595, ]
|
|
|
|
# TODO: Some correct entries are not closed:
|
|
df[df$trace == 1843, ]
|
|
# WHY???
|
|
|
|
openPopup_wide$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$rotationDegree <- NA
|
|
|
|
dat_openPopup <- openPopup_wide[, c("event", "artwork", "trace",
|
|
"date.start", "date.stop",
|
|
"time_ms.start", "time_ms.stop",
|
|
"duration", "card", "popup", "x.start",
|
|
"y.start", "x.stop", "y.stop",
|
|
"distance", "scale.start", "scale.stop",
|
|
"scaleSize", "rotation.start",
|
|
"rotation.stop", "rotationDegree")]
|
|
rm(num_start, openPopup_wide)
|
|
|
|
|
|
# Merge all
|
|
# system.time({
|
|
# dat_all <- merge(dat_trans, dat_flipCard, all = TRUE)
|
|
# dat_all <- merge(dat_all, dat_openTopic, all = TRUE)
|
|
# dat_all <- merge(dat_all, dat_openPopup, all = TRUE)
|
|
# })
|
|
#
|
|
# # check
|
|
# nrow(dat_all) == (nrow(dat_trans) + nrow(dat_flipCard) +
|
|
# nrow(dat_openTopic) + nrow(dat_openPopup))
|
|
#
|
|
# dat_all <- dat_all[order(dat_all$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$date.start), ]
|
|
# rownames(dat_all2) <- NULL
|
|
# TODO: --> same result - but faster. Need it?
|
|
# --> Would hate to depend on dplyr...
|
|
|
|
#' ## Use `rbind()` instead...
|
|
# --> unbeatable in terms of time!
|
|
|
|
dat_all <- rbind(dat_trans, dat_flipCard, dat_openTopic, dat_openPopup)
|
|
|
|
# check
|
|
nrow(dat_all) == (nrow(dat_trans) + nrow(dat_flipCard) +
|
|
nrow(dat_openTopic) + nrow(dat_openPopup))
|
|
|
|
# remove all events that do not have a `date.start`
|
|
dat_all <- dat_all[!is.na(dat_all$date.start), ]
|
|
# TODO: Find out how it can be that there is only a `date.stop`
|
|
|
|
# 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("eventid", "case", "trace", "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$artwork == "glossar", ])
|
|
dat_all <- dat_all[order(dat_all$date.start), ]
|
|
rownames(dat_all) <- NULL
|
|
|
|
# Make `trace` a consecutive number
|
|
dat_all$trace <- as.numeric(as.factor(dat_all$trace))
|
|
|
|
|
|
# TODO: How to handle duration < 0
|
|
# --> Replace with NA for now...
|
|
dat_all$duration <- ifelse(dat_all$duration < 0, NA, dat_all$duration)
|
|
|
|
|
|
#' # Export data
|
|
|
|
write.table(dat_all, "../data/event_logfiles.csv",
|
|
sep = ";", quote = FALSE, row.names = FALSE)
|
|
|
|
|
|
# Is `artwork` my case? Or `artwork` per day? Or `artwork` per some other
|
|
# unit??? Maybe look at differences between timestamps separately for
|
|
# `artwork`? And identify "new observational unit" this way?
|
|
#
|
|
# Definition: (???)
|
|
# 1. Touching a new `artwork` corresponds to "observational unit change"
|
|
# 2. Time interval of XX min within one `artwork` on the same day
|
|
# corresponds to "observational unit change"
|
|
|
|
# Split data frame in list of data frame which all correspond to one
|
|
# artwork
|
|
# dat_art <- split(dat, dat$artwork)
|
|
|
|
# TODO: Write function for closing events
|
|
|
|
|
|
|
|
|