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code/01_parse-logfiles.R
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135
code/01_parse-logfiles.R
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#' ---
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#' title: "Preprocessing raw 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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#' # Preprocessing raw log files into data frame
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#' The following events can be extracted from the log files:
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#'
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#' ```
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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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#' ```
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#' Choose which folders with raw log files should be included:
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folders <- c(
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"_2016"
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, "_2017a"
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, "_2017b"
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, "_2018"
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)
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dirpaths <- paste0("../data/HAUM Logs/", folders)
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fnames <- dir(dirpaths, pattern = "*.log", full.names = TRUE)
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# TODO: Enter all data
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length(fnames)
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head(fnames)
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logs <- lapply(fnames, readLines)
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nlog <- sapply(logs, length)
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dat <- data.frame(fileid = rep(fnames, nlog), logs = unlist(logs))
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head(dat$logs)
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#' Remove corrupted lines
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# Warning messages:
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# incomplete final line found on '_2016/2016_11_18-11_31_0.log'
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# incomplete final line found on '_2016/2016_11_18-11_38_30.log'
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# incomplete final line found on '_2016/2016_11_18-11_40_36.log'
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## --> files have a last line that looks like a binary entry??
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# From LogEntry.as:
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# //pm: inserted this check to account for some broken logfiles
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# if (metaData[1] == null){
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# trace("corrupt line... still do not know how these came to happen.");
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# corrupt lines are "" and need to be removed
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d1 <- dim(dat)[1]
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dat <- subset(dat, dat$logs != "")
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d2 <- dim(dat)[1]
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#' The files contain `r d1-d2` corrupt lines that were remooved from the data.
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#' ### Extract relevant infos
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date <- sapply(dat$logs, gsub,
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pattern = "^\\[(.*)\\], \\[.*$",
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replacement = "\\1",
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USE.NAMES = FALSE)
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timestamp <- sapply(dat$logs, gsub,
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pattern = "^\\[.*\\], \\[(.*)\\].*$",
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replacement = "\\1",
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USE.NAMES = FALSE)
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action <- sapply(dat$logs, gsub,
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pattern = "^.*EyeVisit, (.*):*.*$",
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replacement = "\\1",
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USE.NAMES = FALSE)
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events <- sapply(strsplit(action, ":"), function(x) x[1])
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topics <- sapply(strsplit(action, ":"), function(x) x[2])
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moves <- apply(do.call(rbind,
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strsplit(sapply(strsplit(action, ":"), function(x) x[3]),
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",")),
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2, as.numeric)
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# ATTENTION: as.numeric() forces NAs for "OpenCard" and "CloseCard"
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card_action <- trimws(sapply(strsplit(action, ":"),
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function(x) x[3])[grep("Artwork", events)])
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card <- as.numeric(sapply(strsplit(action, ":"), function(x) x[4]))
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events[grep("Artwork", events)] <- paste("Artwork", card_action, sep = "/")
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ts_elements <- strsplit(timestamp, ":")
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time_ms <- as.numeric(sapply(ts_elements, function(x) x[4])) +
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as.numeric(sapply(ts_elements, function(x) x[3])) * 1000 +
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as.numeric(sapply(ts_elements, function(x) x[2])) * 1000 * 60
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dat$date <- lubridate::parse_date_time(date, "bdyHMSOp")
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dat$time_ms <- time_ms
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dat$event <- events
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dat$artwork <- trimws(sapply(strsplit(topics, "/"), function(x) x[1]))
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dat$popup <- sapply(strsplit(topics, "/"), function(x) x[2])
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dat$card <- card
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dat$x <- moves[,1]
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dat$y <- moves[,2]
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dat$scale <- moves[,3]
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dat$rotation <- moves[,4]
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str(dat)
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head(dat[, 3:ncol(dat)], 20)
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## TODO: Replace artwork and popup numbers with informative strings
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write.table(dat, "../data/rawdata_logfiles.csv",
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sep = ";", quote = FALSE, row.names = FALSE)
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164
code/02_preprocessing.R
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code/02_preprocessing.R
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#' ---
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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.csv", sep = ";", header = TRUE)
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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$event %in% c("Start Application", "Show Application")))
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dat$logs <- NULL # do not need original log files
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dat$date <- as.POSIXct(dat$date) # create date object
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#' ## Remove "button presses"
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# Sort data frame by artwork and date
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dat <- dat[order(dat$artwork, dat$date), ]
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# remove "Transform start" and "Transform stop" following directly each
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# other, since I do not know how to interpret them as events
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id_start <- which(dat$event == "Transform start")
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id_stop <- which(dat$event == "Transform stop")
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id_rm_start <- id_start[diff(id_start) == 1]
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id_rm_stop <- id_stop[diff(id_stop) == 1]
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dat <- dat[-c(id_rm_start, id_rm_stop), ]
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rownames(dat) <- NULL
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id_start2 <- which(dat$event == "Transform start")
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id_stop2 <- which(dat$event == "Transform stop")
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length(id_start2) - length(id_stop2)
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# 340 --> "starts too many"
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# remove "Transform start" and "Transform stop" following directly each
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# other (but with events in between!)
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id_start_new <- id_start2
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id_stop_new <- id_stop2
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for (i in 2:length(id_start_new)) {
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if (id_start_new[i-1] < id_stop_new[i-1] & id_start_new[i] < id_stop_new[i-1]) {
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id_start_new <- id_start_new[-(i-1)]
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} else if (id_start_new[i-1] > id_stop_new[i-1] & id_start_new[i] > id_stop_new[i-1]) {
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id_stop_new <- id_stop_new[-(i-1)]
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}
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}
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length(id_start2) - length(id_start_new)
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length(id_stop2) - length(id_stop_new)
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ids <- data.frame(start = id_start_new, stop = id_stop_new)
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ids$diff <- ids$stop - ids$start
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table(ids$diff)
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# remove "Transform start" and "Transform stop" around other events
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id_rm_start2 <- id_start2[!(id_start2 %in% id_start_new)]
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id_rm_stop2 <- id_stop2[!(id_stop2 %in% id_stop_new)]
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# TODO: It still does not work correctly:
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dat[64764:64769,]
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# time_ms event artwork popup x y scale rotation
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# 64764 473081 Transform start 052 052.xml 1958.65 1505.75 0.8234455 -0.1351998
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# 64765 474226 Show Info 052 052.xml NA NA NA NA
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# 64766 475735 Transform start 052 052.xml 1988.25 1625.25 0.9927645 2.4527958
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# 64767 475739 Transform stop 052 052.xml 1988.25 1625.25 0.9927645 2.4527958
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# 64768 479326 Artwork 052 052.xml NA NA NA NA
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# 64769 479751 Transform stop 052 052.xml 1660.90 1883.20 0.8074586 29.0875534
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# --> but no idea how to find these cases in an automated way...
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dat <- dat[-c(id_rm_start2, id_rm_stop2), ]
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# --> Every start ends with a stop now (but not necessarily the correct one!)
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dat1 <- dat[order(dat$date, dat$time_ms), ]
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dat1$time_diff <- c(NA, diff(dat1$time_ms))
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boxplot(time_diff ~ as.Date(date), dat1[dat1$time_diff > 1000 & dat1$time_diff < 4000, ])
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boxplot(time_ms ~ event, dat1)
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#' ## Plots
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counts <- table(as.Date(dat$date), dat$event)
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lattice::barchart(counts, auto.key = TRUE)
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start_events <- c("Transform start", "Show Info", "ShowPopup", "Artwork/OpenCard")
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counts <- table(as.Date(dat$date[dat$event %in% start_events]),
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dat$event[dat$event %in% start_events])
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lattice::barchart(counts, auto.key = TRUE)
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# TODO: Do I want to "collapse" the data frame in a way, that I only have
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# one event for each "set", meaning
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#
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# * Transform start + Transform stop --> Transform
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# * Artwork/OpenCard + Artwork/CloseCard --> Show Subcard
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# * ShowPopup + HidePopup --> Show Popup
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# * Show Info + Show Front --> Flip Card
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# (s.o. ;))
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#
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# Then I would have meaningful variables like duration, distance, degree of
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# rotation, size of scaling, selection of Subcard etc.
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# This means that I would have to delete all "unclosed" events.
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# Create a data frame with
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# case event attributes (can differ for different events)
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# ??
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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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# id activity timestamp
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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$artwork)
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## --> Maybe need it at some point?
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#' # Problems
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#' * Opening and closing of events cannot be identified unambiguously; it
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#' can happen that the wrong tags have been put together (e.g., Transform
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#' start and Transform stop); therefore, durations etc. are only heuristic
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code/03_specs.R
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code/03_specs.R
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code")
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library(lubridate)
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dat <- read.table("../data/rawdata_logfiles.csv", header = TRUE, sep = ";")
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dat$event <- factor(dat$event, levels = c("Start Application",
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"Show Application",
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"Transform start",
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"Transform stop",
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"Show Info",
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"Show Front",
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"Artwork/OpenCard",
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"Artwork/CloseCard",
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"ShowPopup", "HidePopup"))
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#dat$logs <- NULL # do not need original log files
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dat$date <- as.POSIXct(dat$date) # create date object
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str(dat)
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head(dat)
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#' Log files between December 2016 and December 2018
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range(dat$date)
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#' Number of log files per year
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table(year(dat$date))
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#' Number of events total and per year
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table(dat$event)
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# Start Application Show Application
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# 1679 1656
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# Transform start Transform stop
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# 2119815 1944618
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# Show Info Show Front
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# 71955 71043
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# Artwork/OpenCard Artwork/CloseCard
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# 64990 56750
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# ShowPopup HidePopup
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# 44070 43813
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# --> more "openups" than "closes" (not surprisingly)
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table(dat$event, year(dat$date))
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#' Number of log files per week day
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table(weekdays(dat$date))
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#' Anzahl der Tage
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length(unique(as.Date(dat$date)))
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#' Which artworks are looked at most often
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table(dat$artwork)
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lattice::barchart(table(dat$artwork))
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#' ## Plots from Visualization Tool
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dat16 <- dat[year(dat$date) == 2016, ]
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counts <- table(as.Date(dat16$date), dat16$event)
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lattice::barchart(counts, auto.key = TRUE)
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start_events <- c("Transform start", "Show Info", "ShowPopup", "Artwork/OpenCard")
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counts <- table(as.Date(dat16$date[dat16$event %in% start_events]),
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dat16$event[dat16$event %in% start_events])
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counts
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lattice::barchart(counts, auto.key = TRUE)
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### Example for log file in order to show structure
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write.table(dat[240:660, 3:12], "set.txt", quote = FALSE)
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# is then edited by hand to have all possible events...
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select <- c(243, 244, 245, 246, 253, 254, 255, 256, 257, 259, 260, 262,
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265, 266, 267, 268, 269, 270, 271, 287, 288, 289, 294, 295,
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296, 303, 304, 305, 306, 307, 318, 319, 320, 321, 322, 323,
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324, 325, 326, 357, 358, 359, 360, 361, 362, 363, 408, 409,
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410, 411, 412, 413, 414, 415, 416, 417, 418, 439, 440, 441,
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516, 518, 519, 587, 588, 589, 590, 591, 592, 593, 594, 595,
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652, 653, 654, 655, 656, 657)
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writeLines(dat[select, "logs"], "set_logs.txt")
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tab <- table(diff(dat$date)) # uninformative on raw log data!
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plot(tab[tab > 2000])
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