#' ---
#' 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