Deleted zz_investigate.R since all stuff in their was obsolete; explanations for how I handled open questions can be found in README.Rmd

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Nora Wickelmaier 2023-09-13 14:25:56 +02:00
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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.csv", sep = ";", header = TRUE)
dat0$date <- as.POSIXct(dat0$date) # create date object
plot(dat0$time_ms[1:3000], type = "l")
# what happens here? Why does `time_ms` go down, but not to 0?
plot(dat0$time_ms[2500:3000], type = "l")
plot(dat0$time_ms[2755:2765], type = "l") # "zoom in"
dat0[2755:2765, ]
# --> overall time stamp keeps going up...
# TODO: How to create a plot that gives the same information based on
# `time_ms` und `date`??
plot(time_ms ~ date, dat0[1:5000, ], type = "b")
abline(h = 0, col = "red", lty = 3)
# Visualize night
plot(time_ms ~ date, dat0[1:10000, ], type = "b")
# Not all `Start Application` have `time_ms = 0` - why??
dat0[125537:125542, ]
dat0[6673501:6673510, ]
# --> What's happening here?
table(dat0[dat0$event %in% "Start Application", c("event", "date", "time_ms")]$time_ms)
# 0 1 15 16 296 2819 2914 3191 5316 6535
# 3131 4 21 48 1 1 1 1 1 1
# --> ???
dat0[dat0$event == "Start Application" & dat0$time_ms == 6535, ]
dat0[989313:989317, ]
dat0[dat0$event == "Start Application" & dat0$time_ms == 5316, ]
dat0[2071078:2071082, ]
dat0[dat0$event == "Start Application" & dat0$time_ms == 3191, ]
dat0[2851863:2851867, ]
dat0[dat0$event == "Start Application" & dat0$time_ms == 16, ]
dat0[156382:156386, ]
dat0[5566940:5566947, ]
# --> pattern is *not* consistent
dat0[dat0$event == "Start Application" & dat0$time_ms == 1, ]
dat0[125537:125542, ]
xtabs( ~ event + as.Date(date), dat0[1:1000, ])
# How many days do we have with up to 8 "Start Applications"
table(xtabs( ~ event + as.Date(date), dat0[dat0$event == "Start Application", ]))
# 1 2 3 4 5 6 7 8
# 381 657 272 86 37 14 10 2
# --> 8 days without any "Start Application"
length(unique(as.Date(dat0$date))) -
length(xtabs( ~ event + as.Date(date), dat0[dat0$event == "Start Application", ]))
# But only 6 files with 2 "Start Applications"
table(xtabs( ~ event + fileid, dat0[dat0$event == "Start Application", ]))
# 1 2
# 3198 6
# --> That means we have 36,563 file ids without any "Start Application"
#' # Remove irrelevant events
#' ## Remove Start Application and Show Application
dat <- subset(dat0, !(dat0$event %in% c("Start Application", "Show Application")))
#' ## Remove "button presses"
# Sort data frame by artwork and date
dat <- dat[order(dat$artwork, dat$date), ]
# remove "Transform start" and "Transform stop" following directly each
# other, since I do not know how to interpret them as events
id_start <- which(dat$event == "Transform start")
id_stop <- which(dat$event == "Transform stop")
id_rm_start <- id_start[diff(id_start) == 1]
id_rm_stop <- id_stop[diff(id_stop) == 1]
dat <- dat[-c(id_rm_start, id_rm_stop), ]
rownames(dat) <- NULL
id_start2 <- which(dat$event == "Transform start")
id_stop2 <- which(dat$event == "Transform stop")
length(id_start2) - length(id_stop2)
# 340 --> "starts too many"
# remove "Transform start" and "Transform stop" following directly each
# other (but with events in between!)
id_start_new <- id_start2
id_stop_new <- id_stop2
for (i in 2:length(id_start_new)) {
if (id_start_new[i-1] < id_stop_new[i-1] & id_start_new[i] < id_stop_new[i-1]) {
id_start_new <- id_start_new[-(i-1)]
} else if (id_start_new[i-1] > id_stop_new[i-1] & id_start_new[i] > id_stop_new[i-1]) {
id_stop_new <- id_stop_new[-(i-1)]
}
}
length(id_start2) - length(id_start_new)
length(id_stop2) - length(id_stop_new)
ids <- data.frame(start = id_start_new, stop = id_stop_new)
ids$diff <- ids$stop - ids$start
table(ids$diff)
# remove "Transform start" and "Transform stop" around other events
id_rm_start2 <- id_start2[!(id_start2 %in% id_start_new)]
id_rm_stop2 <- id_stop2[!(id_stop2 %in% id_stop_new)]
# TODO: It still does not work correctly:
dat[64764:64769,]
# time_ms event artwork popup x y scale rotation
# 64764 473081 Transform start 052 052.xml 1958.65 1505.75 0.8234455 -0.1351998
# 64765 474226 Show Info 052 052.xml NA NA NA NA
# 64766 475735 Transform start 052 052.xml 1988.25 1625.25 0.9927645 2.4527958
# 64767 475739 Transform stop 052 052.xml 1988.25 1625.25 0.9927645 2.4527958
# 64768 479326 Artwork 052 052.xml NA NA NA NA
# 64769 479751 Transform stop 052 052.xml 1660.90 1883.20 0.8074586 29.0875534
# --> but no idea how to find these cases in an automated way...
dat <- dat[-c(id_rm_start2, id_rm_stop2), ]
# --> Every start ends with a stop now (but not necessarily the correct one!)
dat1 <- dat[order(dat$date, dat$time_ms), ]
dat1$time_diff <- c(NA, diff(dat1$time_ms))
boxplot(time_diff ~ as.Date(date), dat1[dat1$time_diff > 1000 & dat1$time_diff < 4000, ])
boxplot(time_ms ~ event, dat1)
#' ## Plots
counts <- table(as.Date(dat$date), dat$event)
lattice::barchart(counts, auto.key = TRUE)
start_events <- c("Transform start", "Show Info", "ShowPopup", "Artwork/OpenCard")
counts <- table(as.Date(dat$date[dat$event %in% start_events]),
dat$event[dat$event %in% start_events])
lattice::barchart(counts, auto.key = TRUE)
# TODO: Do I want to "collapse" the data frame in a way, that I only have
# one event for each "set", meaning
#
# * Transform start + Transform stop --> Transform
# * Artwork/OpenCard + Artwork/CloseCard --> Show Subcard
# * ShowPopup + HidePopup --> Show Popup
# * Show Info + Show Front --> Flip Card
# (s.o. ;))
#
# Then I would have meaningful variables like duration, distance, degree of
# rotation, size of scaling, selection of Subcard etc.
# This means that I would have to delete all "unclosed" events.
# Create a data frame with
# case event attributes (can differ for different events)
# ??
# 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"
# id activity timestamp
# Split data frame in list of data frame which all correspond to one
# artwork
# dat_art <- split(dat, dat$artwork)
## --> Maybe need it at some point?
#' # Problems
#' * Opening and closing of events cannot be identified unambiguously; it
#' can happen that the wrong tags have been put together (e.g., Transform
#' start and Transform stop); therefore, durations etc. are only heuristic
#' ## Plots
counts <- table(as.Date(dat$date), dat$event)
lattice::barchart(counts, auto.key = TRUE)
start_events <- c("Transform start", "Show Info", "ShowPopup", "Artwork/OpenCard")
counts <- table(as.Date(dat$date[dat$event %in% start_events]),
dat$event[dat$event %in% start_events])
lattice::barchart(counts, auto.key = TRUE)
# 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")