Worked on preprocessing; added trace variable; tried out modeling; prepared questions for PM
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README.md
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README.md
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# Offene Fragen
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## Datenverständnis
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* Welche Einheit haben x und y? Pixel?
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* Welche Einheit hat scale?
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* rotation wirklich degree?
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* Nach welchem Zeitintervall resettet sich der Tisch wieder in die
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Ausgangskonfiguration?
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## Tisch-Software
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* Gibt es Doku für die Bilder, die über die xml files hinausgeht? Sowas wie
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ein Manual oder ähnliches?
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* Gibt es evtl. irgendwo noch ein Tablet mit der Anwendung drauf?
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* Was bedeuten die Farben der Topic Cards?
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## Event Logs
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* Wie gehen wir mit "nicht geschlossenen" Events um? Einfach rauslöschen?
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- für Transform tendiere ich zu ja, weil sonst total uninteressant
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- bei flipCard bin ich nicht so sicher... Aber man kann dann keine
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duration berechnen, wäre NA
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* Moves/scales/rotations ohne Veränderung würde ich auf jeden Fall
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rauslöschen
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* Es ist nicht möglich (bzw. ich weiß nicht wie) zusammengehörige Events
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eineindeutig zu identifizieren
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- nach Heuristik vorgehen? Doppelte Transformation start und stop einfach
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raus?
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- Daten sind nicht "fehlerfrei"; es gibt z.B. Transformation-Events wo
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das Ende nicht geloggt wurde
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* Wie identifiziere ich eine "Interaktionseinheit"?
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- Was ist ein "case"?
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- Eher grob über Zeitintervalle?
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- Noch irgendeine andere Idee?
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* Herausfinden, ob mehr als eine Person am Tisch steht?
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- Sliding window, in der Anzahl von Artworks gezählt wird? Oder wie weit
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angefasste Artworks voneinander entfernt sind?
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- Man kann sowas schon "sehen" in den Logs - aber wie kann ich es
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automatisiert rausziehen? Was ist meine Definition von
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"Interaktionsboost"?
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- Egal wie wir es machen, geht es auf den "Event-Log-Daten"?
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* Anreicherung der Log-Daten mit weiteren Metadaten? Was wäre interessant?
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- Metadata on artworks like, name, artist, type of artwork, epoch, etc.
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- School vacations and holidays
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- Special exhibits at the museum
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- Number of visitors per day
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- Age structure of visitors per day?
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- ... ????
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## HAUM
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* Bei Sven noch mal nachhaken wegen Besucherzahlen?
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# Problems and how I handled them
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This lists some problems with the log data that required decisions. These
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decisions influence the outcome and maybe even the data quality. Hence, I
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tried to document how I handled these problems and explain the decisions I
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made.
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## Weird behavior of `time_ms` and neg. `duration`values
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## Events that only close (`date.start` is NA)
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## Timestamps repeat
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## Popups from glossar cannot be assigned to a specific artwork
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## Assign a case variable based on "time heuristic"
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## A `move`event does not record any change
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## Add moves to `trace` variable
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# Reading list
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* @Arizmendi2022 [$-$]
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* @Bannert2014 [x]
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* @Bousbia2010 [$-$]
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* @Cerezo2020
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* @GerjetsSchwan2021 [x]
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* @Goldhammer2020
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* @Guenther2007
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* @HuberBannert2023 [x]
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* @Kroehne2018
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* @SchwanGerjets2021 [x]
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* @vanderAalst2016 [Chap. 2, x]
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* @vanderAalst2016 [Chap. 3]
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* @vanderAalst2016 [Chap. 5, x]
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* @Wang2019
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@ -4,8 +4,7 @@
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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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#' default
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#' pdf_document:
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#' toc: true
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#' number_sections: true
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@ -17,8 +16,6 @@
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#+ setup, include = FALSE
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knitr::opts_chunk$set(warning = FALSE, message = FALSE)
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#' # Preprocessing raw log files into data frame
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#' The following events can be extracted from the log files:
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#'
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#' ```
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@ -36,8 +33,8 @@ knitr::opts_chunk$set(warning = FALSE, message = FALSE)
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#' Choose which folders with raw log files should be included:
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folders <- "all"
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#folders <- "_2016b"
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#folders <- "all"
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folders <- "_2016b"
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dirpaths <- paste0("../data/haum_logs_2016-2023/", folders)
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@ -71,7 +68,7 @@ dat <- subset(dat, dat$logs != "")
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d2 <- dim(dat)[1]
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#' The files contain `r d1-d2` corrupt lines that were remooved from the data.
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#'
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#' ### Extract relevant infos
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@ -136,6 +133,8 @@ dat <- dat[order(dat$date), ]
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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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#' ### Save data frame
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write.table(dat, "../data/rawdata_logfiles_small.csv",
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sep = ";", quote = FALSE, row.names = FALSE)
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@ -222,3 +222,18 @@ lattice::barchart(counts, auto.key = TRUE)
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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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#' ## 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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@ -109,11 +109,16 @@ trans_wide$rotationDegree <- trans_wide$rotation.stop -
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trans_wide$rotation.start
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trans_wide$scaleSize <- trans_wide$scale.stop - trans_wide$scale.start
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dat_trans <- dat_trans[trans_wide$distance != 0 &
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trans_wide$trace <- NA
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trans_wide$card <- NA
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trans_wide$popup <- NA
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dat_trans <- trans_wide[trans_wide$distance != 0 &
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trans_wide$rotationDegree != 0 &
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trans_wide$scaleSize != 0,
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c("event", "artwork", "date.start", "date.stop",
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c("event", "artwork", "trace", "date.start", "date.stop",
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"time_ms.start", "time_ms.stop", "duration",
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"card", "popup",
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"x.start", "y.start", "x.stop", "y.stop",
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"distance", "scale.start", "scale.stop",
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"scaleSize", "rotation.start", "rotation.stop",
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@ -128,7 +133,8 @@ summary(dat_trans)
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# --> Hat er eine Erklärung dafür?
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#plot(time_ms.stop ~ time_ms.start, dat_trans, type = "b")
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plot(time_ms.stop ~ time_ms.start, dat_trans, col = rgb(red = 0, green = 0, blue = 0, alpha = 0.2))
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plot(time_ms.stop ~ time_ms.start, dat_trans,
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col = rgb(red = 0, green = 0, blue = 0, alpha = 0.2))
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plot(date.stop ~ date.start, dat_trans[1:1000,], type = "b")
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@ -248,7 +254,7 @@ lut[sapply(lut$artwork, length) == 1, "glossar_file"]
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# TODO: Fill in the ones that are associated with one artwork
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# --> Can't come up with something -- maybe ask AK???
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# TODO: How to check if one of the former "Show Infos" is correct on
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# TODO: How to check if one of the former "Show Infos" is correct
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# --> Can't come up with something -- maybe ask AK???
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# for (file in lut$glossar_file) {
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@ -285,6 +291,8 @@ lut[sapply(lut$artwork, length) == 1, "glossar_file"]
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# correct: 17940
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# incorrect: 17963
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# TODO: "glossar" entry should be changed to the corresponding artwork
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# TODO: Add additional variable `glossar` with 0/1 or similar instead
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# TODO: For now: Exclude not matched glossar entries
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@ -314,10 +322,28 @@ flipCard_wide$duration <- flipCard_wide$time_ms.stop -
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flipCard_wide$duration <- ifelse(flipCard_wide$duration < 0,
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NA, flipCard_wide$duration)
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flipCard_wide$card <- NA
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flipCard_wide$popup <- NA
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flipCard_wide$x.start <- NA
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flipCard_wide$x.stop <- NA
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flipCard_wide$y.start <- NA
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flipCard_wide$y.stop <- NA
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flipCard_wide$distance <- NA
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flipCard_wide$scale.start <- NA
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flipCard_wide$scale.stop <- NA
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flipCard_wide$scaleSize <- NA
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flipCard_wide$rotation.start <- NA
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flipCard_wide$rotation.stop <- NA
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flipCard_wide$rotationDegree <- NA
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dat_flipCard <- flipCard_wide[, c("event", "artwork", "trace",
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"date.start", "date.stop",
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"time_ms.start", "time_ms.stop",
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"duration")]
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"duration", "card", "popup",
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"x.start", "y.start", "x.stop", "y.stop",
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"distance", "scale.start", "scale.stop",
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"scaleSize", "rotation.start",
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"rotation.stop", "rotationDegree")]
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rm(tmp, flipCard_wide)
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@ -344,10 +370,27 @@ openTopic_wide$duration <- ifelse(openTopic_wide$duration < 0,
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# TODO: How to handle duration < 0
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# --> Replace with NA for now...
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dat_openTopic <- openTopic_wide[, c("event", "artwork", "card", "trace",
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openTopic_wide$popup <- NA
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openTopic_wide$x.start <- NA
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openTopic_wide$x.stop <- NA
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openTopic_wide$y.start <- NA
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openTopic_wide$y.stop <- NA
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openTopic_wide$distance <- NA
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openTopic_wide$scale.start <- NA
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openTopic_wide$scale.stop <- NA
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openTopic_wide$scaleSize <- NA
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openTopic_wide$rotation.start <- NA
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openTopic_wide$rotation.stop <- NA
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openTopic_wide$rotationDegree <- NA
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dat_openTopic <- openTopic_wide[, c("event", "artwork", "trace",
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"date.start", "date.stop",
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"time_ms.start", "time_ms.stop",
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"duration")]
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"duration", "card", "popup", "x.start",
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"y.start", "x.stop", "y.stop",
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"distance", "scale.start", "scale.stop",
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"scaleSize", "rotation.start",
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"rotation.stop", "rotationDegree")]
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# TODO: card should have a unique identifier for each artwork
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rm(openTopic_wide, num_start, tmp)
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@ -385,29 +428,47 @@ openPopup_wide$duration <- ifelse(openPopup_wide$duration < 0,
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# TODO: How to handle duration < 0
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# --> Replace with NA for now...
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dat_openPopup <- openPopup_wide[, c("event", "artwork", "popup", "trace",
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openPopup_wide$card <- NA
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openPopup_wide$x.start <- NA
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openPopup_wide$x.stop <- NA
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openPopup_wide$y.start <- NA
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openPopup_wide$y.stop <- NA
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openPopup_wide$distance <- NA
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openPopup_wide$scale.start <- NA
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openPopup_wide$scale.stop <- NA
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openPopup_wide$scaleSize <- NA
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openPopup_wide$rotation.start <- NA
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openPopup_wide$rotation.stop <- NA
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openPopup_wide$rotationDegree <- NA
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dat_openPopup <- openPopup_wide[, c("event", "artwork", "trace",
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"date.start", "date.stop",
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"time_ms.start", "time_ms.stop",
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"duration")]
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"duration", "card", "popup", "x.start",
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"y.start", "x.stop", "y.stop",
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"distance", "scale.start", "scale.stop",
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"scaleSize", "rotation.start",
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"rotation.stop", "rotationDegree")]
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rm(num_start, openPopup_wide, tmp)
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# Merge all
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system.time({
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dat_all <- merge(dat_trans, dat_flipCard, all = TRUE)
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dat_all <- merge(dat_all, dat_openTopic, all = TRUE)
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dat_all <- merge(dat_all, dat_openPopup, all = TRUE)
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})
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# check
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nrow(dat_all) == (nrow(dat_trans) + nrow(dat_flipCard) +
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nrow(dat_openTopic) + nrow(dat_openPopup))
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dat_all <- dat_all[order(dat_all$date.start), ]
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rownames(dat_all) <- NULL
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# system.time({
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# dat_all <- merge(dat_trans, dat_flipCard, all = TRUE)
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# dat_all <- merge(dat_all, dat_openTopic, all = TRUE)
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# dat_all <- merge(dat_all, dat_openPopup, all = TRUE)
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# })
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#
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# # check
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# nrow(dat_all) == (nrow(dat_trans) + nrow(dat_flipCard) +
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# nrow(dat_openTopic) + nrow(dat_openPopup))
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#
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# dat_all <- dat_all[order(dat_all$date.start), ]
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# rownames(dat_all) <- NULL
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#
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# TODO: from here on NA... WHY??
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dat_all[19426:19435, ]
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# dat_all[19426:19435, ]
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# TODO: Should card maybe also be filled in for "openPopup"?
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@ -423,19 +484,135 @@ dat_all[19426:19435, ]
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# TODO: --> same result - but faster. Need it?
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# --> Would hate to depend on dplyr...
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#' ## Use `rbind()` instead...
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# --> unbeatable in terms of time!
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dat_all <- rbind(dat_trans, dat_flipCard, dat_openTopic, dat_openPopup)
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# check
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nrow(dat_all) == (nrow(dat_trans) + nrow(dat_flipCard) +
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nrow(dat_openTopic) + nrow(dat_openPopup))
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# remove all events that do not have a `date.start`
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dat_all <- dat_all[!is.na(dat_all$date.start), ]
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# TODO: Find out how it can be that there is only a `date.stop`
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# sort by `start.date`
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dat_all <- dat_all[order(dat_all$date.start), ]
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rownames(dat_all) <- NULL
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ind <- rowSums(is.na(dat_all)) == ncol(dat_all)
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any(ind)
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dat_all[ind, ]
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# --> No rows with only NA, as it should be.
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summary(dat_all) # OK, this actually makes a lot of sense :)
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#' ## Create case variable
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#dat_all$timediff <- as.numeric(dat_all$date.stop - dat_all$date.start)
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dat_all$timediff <- as.numeric(diff(c(dat_all$date.start[1], dat_all$date.start)))
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hist(dat_all$timediff[dat_all$timediff < 40], breaks = 50)
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#' ## Plots
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# TODO: What is the best choice for the cutoff here? I took 20 secs for now
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dat_all$case <- NA
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j <- 1
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counts <- table(as.Date(dat$date), dat$event)
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lattice::barchart(counts, auto.key = TRUE)
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for (i in seq_len(nrow(dat_all))) {
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if (dat_all$timediff[i] < 21) {
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dat_all$case[i] <- j
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} else {
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j <- j + 1
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dat_all$case[i] <- j
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}
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}
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head(dat_all[, c("event", "artwork", "trace", "date.start", "timediff", "case")], 100)
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start_events <- c("Transform start", "Show Info", "ShowPopup", "Artwork/OpenCard")
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#' ## Add event ID
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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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dat_all$eventid <- seq_len(nrow(dat_all))
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dat_all <- dat_all[, c("eventid", "case", "trace", "event", "artwork",
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"date.start", "date.stop", "time_ms.start",
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"time_ms.stop", "duration", "card", "popup",
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"x.start", "y.start", "x.stop", "y.stop",
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"distance", "scale.start", "scale.stop",
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"scaleSize", "rotation.start", "rotation.stop",
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"rotationDegree")]
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#' ## Add `trace` numbers for `move` events
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# when case and artwork are identical and there is only 1 trace value
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# --> assign it to all `move` events for that case and artwork
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# when case and artwork are identical and there is more than 1 trace value
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# --> assign the `trace` value that was right before this `move` event
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# (could, of course, also be after)
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cases <- unique(dat_all$case)
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aws <- unique(dat_all$artwork)[unique(dat_all$artwork) != "glossar"]
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max_trace <- max(dat_all$trace, na.rm = TRUE) + 1
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out <- NULL
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for (case in cases) {
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for (art in aws) {
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tmp <- dat_all[dat_all$case == case & dat_all$artwork == art, ]
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if (nrow(tmp) != 0) {
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if (length(na.omit(unique(tmp$trace))) == 1) {
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tmp[tmp$event == "move", "trace"] <- na.omit(unique(tmp$trace))
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} else if (length(na.omit(unique(tmp$trace))) > 1) {
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for (i in 1:nrow(tmp)) {
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if (tmp$event[i] == "move") {
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if (i == 1) {
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tmp$trace[i] <- na.omit(unique(tmp$trace))[1]
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} 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$trace))
|
||||
|
||||
#' # Export data
|
||||
|
||||
write.table(dat_all, "../data/event_logfiles.csv",
|
||||
sep = ";", quote = FALSE, row.names = FALSE)
|
||||
|
||||
|
||||
# Is `artwork` my case? Or `artwork` per day? Or `artwork` per some other
|
||||
@ -451,16 +628,5 @@ lattice::barchart(counts, auto.key = TRUE)
|
||||
# artwork
|
||||
# dat_art <- split(dat, dat$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
|
||||
|
||||
# TODO: Add a case identifier based on timestamps
|
||||
# --> needs to be done on "raw data". Is it possible? Something seems
|
||||
# seriously wrong with `time_ms`
|
||||
|
||||
# TODO: Write function for closing events
|
||||
|
||||
|
||||
115
code/03_modeling.R
Normal file
115
code/03_modeling.R
Normal file
@ -0,0 +1,115 @@
|
||||
#' ---
|
||||
#' title: "Modelling log files with Process Mining"
|
||||
#' 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")
|
||||
|
||||
#' # Read data
|
||||
|
||||
dat <- read.table("../data/event_logfiles.csv", sep = ";", header = TRUE)
|
||||
dat$date.start <- as.POSIXct(dat$date.start)
|
||||
dat$date.stop <- as.POSIXct(dat$date.stop)
|
||||
|
||||
#' # Creating event logs
|
||||
|
||||
library(bupaverse)
|
||||
|
||||
names(dat)[6:7] <- c("start", "complete")
|
||||
|
||||
table(table(dat$start))
|
||||
# --> hmm...
|
||||
|
||||
summary(aggregate(duration ~ trace, dat, mean))
|
||||
|
||||
|
||||
alog <- activitylog(dat,
|
||||
case_id = "trace",
|
||||
activity_id = "event",
|
||||
#resource_id = "case",
|
||||
resource_id = "artwork",
|
||||
timestamps = c("start", "complete"))
|
||||
|
||||
# --> have not understood, yet, which ist what...
|
||||
|
||||
process_map(alog)
|
||||
|
||||
process_map(alog, frequency("relative"))
|
||||
process_map(alog, frequency("relative_consequent"))
|
||||
|
||||
library(processanimateR)
|
||||
|
||||
animate_process(to_eventlog(alog))
|
||||
|
||||
col_vector <- c("#7FC97F", "#BEAED4", "#FDC086", "#FFFF99", "#386CB0",
|
||||
"#F0027F", "#BF5B17", "#666666", "#1B9E77", "#D95F02",
|
||||
"#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D",
|
||||
"#666666", "#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C",
|
||||
"#FB9A99", "#E31A1C", "#FDBF6F", "#FF7F00", "#CAB2D6",
|
||||
"#6A3D9A", "#FFFF99", "#B15928", "#FBB4AE", "#B3CDE3",
|
||||
"#CCEBC5", "#DECBE4", "#FED9A6", "#FFFFCC", "#E5D8BD",
|
||||
"#FDDAEC", "#F2F2F2", "#B3E2CD", "#FDCDAC", "#CBD5E8",
|
||||
"#F4CAE4", "#E6F5C9", "#FFF2AE", "#F1E2CC", "#CCCCCC",
|
||||
"#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",
|
||||
"#FFFF33", "#A65628", "#F781BF", "#999999", "#66C2A5",
|
||||
"#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F",
|
||||
"#E5C494", "#B3B3B3", "#8DD3C7", "#FFFFB3", "#BEBADA",
|
||||
"#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
|
||||
"#D9D9D9")
|
||||
|
||||
animate_process(to_eventlog(alog), mode = "relative", jitter = 10, legend = "color",
|
||||
mapping = token_aes(color = token_scale("artwork",
|
||||
scale = "ordinal",
|
||||
range = col_vector)))
|
||||
|
||||
elog <- to_eventlog(alog)
|
||||
animate_process(elog[elog$artwork == "054", ])
|
||||
animate_process(elog[elog$artwork == "080", ])
|
||||
animate_process(elog[elog$artwork == "501", ])
|
||||
|
||||
process_map(alog[alog$artwork == "054", ])
|
||||
|
||||
animate_process(elog[elog$artwork %in% c("080", "054"), ],
|
||||
mode = "relative", jitter = 10, legend = "color",
|
||||
mapping = token_aes(color = token_scale("artwork",
|
||||
scale = "ordinal",
|
||||
range = c("black", "gray"))))
|
||||
# --> not sure, yet, how to interpret this...
|
||||
|
||||
|
||||
|
||||
|
||||
alog080 <- activitylog(dat[dat$artwork %in% "080", ],
|
||||
#case_id = "case",
|
||||
case_id = "trace",
|
||||
activity_id = "event",
|
||||
#resource_id = "trace",
|
||||
resource_id = "case",
|
||||
timestamps = c("start", "complete"))
|
||||
|
||||
process_map(alog080, frequency("relative"))
|
||||
|
||||
|
||||
|
||||
alog054 <- activitylog(dat[dat$artwork %in% "054", ],
|
||||
#case_id = "case",
|
||||
case_id = "trace",
|
||||
activity_id = "event",
|
||||
#resource_id = "trace",
|
||||
resource_id = "case",
|
||||
timestamps = c("start", "complete"))
|
||||
|
||||
process_map(alog054, frequency("relative"))
|
||||
|
||||
|
||||
|
||||
|
||||
125
code/questions_data-inconsistencies.R
Normal file
125
code/questions_data-inconsistencies.R
Normal file
@ -0,0 +1,125 @@
|
||||
#' ---
|
||||
#' title: "Open Questions"
|
||||
#' author: "Nora Wickelmaier"
|
||||
#' date: "`r Sys.Date()`"
|
||||
#' output:
|
||||
#' html_document:
|
||||
#' number_sections: true
|
||||
#' toc: true
|
||||
#' ---
|
||||
|
||||
#+ include = FALSE
|
||||
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code")
|
||||
dat <- read.table("../data/event_logfiles.csv", sep = ";", header = TRUE)
|
||||
dat$date.start <- as.POSIXct(dat$date.start)
|
||||
dat$date.stop <- as.POSIXct(dat$date.stop)
|
||||
|
||||
#' This is what the data look like after preprocessing right now
|
||||
|
||||
#+ include = FALSE
|
||||
mat <- as.data.frame(t(sapply(dat, range, na.rm = TRUE)))
|
||||
names(mat) <- c("min", "max")
|
||||
mat$min <- round(as.numeric(mat$min), 1)
|
||||
mat$max <- round(as.numeric(mat$max), 1)
|
||||
mat$mean <- round(sapply(dat, function(x) mean(x, na.rm = TRUE)), 1)
|
||||
mat$missings <- sapply(dat, function(x) sum(is.na(x)))
|
||||
mat <- mat[!(rownames(mat) %in% c("eventid", "case", "trace", "event", "artwork", "card", "popup", "date.start", "date.stop")), ]
|
||||
|
||||
#+ echo = FALSE
|
||||
knitr::kable(mat)
|
||||
|
||||
#' This is only the data for 2016! So only about 2 weeks in December.
|
||||
|
||||
# Date ranges
|
||||
range(dat$date.start)
|
||||
range(dat$date.stop, na.rm = TRUE)
|
||||
|
||||
#' # Units of x and y
|
||||
#' I assume that x and y are pixel $\to$ correct?
|
||||
|
||||
#' But they look weird, when plotted. Is it possible that there are
|
||||
#' outliers? If yes, how? Do we have the true ranges of the display?
|
||||
|
||||
par(mfrow = c(1, 2))
|
||||
plot(y.start ~ x.start, dat)
|
||||
abline(v = c(0, 3800), h = c(0, 2150), col = "blue", lwd = 2)
|
||||
plot(y.stop ~ x.stop, dat)
|
||||
abline(v = c(0, 3800), h = c(0, 2150), col = "blue", lwd = 2)
|
||||
|
||||
aggregate(cbind(x.start, x.stop, y.start, y.stop) ~ 1, dat, mean)
|
||||
|
||||
#' Looks like the range should be something like $x = [0, 3800]$ and
|
||||
#' $y = [0, 2150]$. Do we have the starting coordinates for each artwork?
|
||||
#'
|
||||
|
||||
#' # Unit of scale
|
||||
|
||||
summary(dat$scaleSize)
|
||||
|
||||
#' I thought it would be some kind of scaling factor, but then I would
|
||||
#' have expected that `scale.start` is always 1 or something.
|
||||
#'
|
||||
|
||||
#' # Unit of rotation
|
||||
|
||||
summary(dat$rotationDegree)
|
||||
|
||||
#' This looks pretty clear. Should be degree. Anything else to consider
|
||||
#' here? I am assuming negative means left, but maybe not?
|
||||
#'
|
||||
|
||||
#' # Meaningful unit for "case"
|
||||
|
||||
#' I pretty randomly chose `20 sec` based on this plot. I would love a
|
||||
#' second opinion. `:)`
|
||||
|
||||
timediff <- as.numeric(diff(c(dat$date.start[1], dat$date.start)))
|
||||
hist(timediff[timediff < 40], breaks = 50)
|
||||
abline(v = 20, col = "red", lwd = 2)
|
||||
|
||||
#' This actually works pretty well and lets me assign `trace` values to the
|
||||
#' moves. But maybe there are other ideas on how to define this?
|
||||
|
||||
dat[1:40, c("date.start", "case", "trace", "event", "artwork")]
|
||||
|
||||
|
||||
#' # Problems with `time_ms`
|
||||
|
||||
#' What exactly happens, when `time_ms` goes down again? Why does it not go
|
||||
#' down to 0?
|
||||
|
||||
par(mfrow = c(1, 2))
|
||||
|
||||
plot(dat$time_ms.start[1:100], type = "b", ylab = "time_ms", xlab = "")
|
||||
points(dat$time_ms.stop[1:100], type = "b", col = rgb(1, 0, 0, .5))
|
||||
legend("topleft", c("start", "stop"), lty = 1, col = c("black", "red"))
|
||||
|
||||
plot(dat$time_ms.stop[1:100] - dat$time_ms.start[1:100], type = "b",
|
||||
ylab = "duration", col = rgb(0, 0, 1, .5))
|
||||
abline(h = 0, lty = 2)
|
||||
|
||||
#' For the regular timestamps everything looks fine.
|
||||
|
||||
par(mfrow = c(1, 2))
|
||||
|
||||
plot(dat$date.stop[1:100], type = "b", ylab = "timestamp", xlab = "",
|
||||
col = rgb(1, 0, 0, .5))
|
||||
points(dat$date.start[1:100], type = "b")
|
||||
legend("topleft", c("start", "stop"), lty = 1, col = c("black", "red"))
|
||||
|
||||
plot(dat$date.stop[1:100] - dat$date.start[1:100], type = "b",
|
||||
ylab = "duration", col = rgb(0, 0, 1, .5))
|
||||
abline(h = 0, lty = 2)
|
||||
|
||||
#+
|
||||
plot(time_ms.start ~ date.start, dat[1:1000, ], type = "b")
|
||||
points(time_ms.stop ~ date.stop, dat[1:1000, ], type = "b", col = rgb(1, 0, 0, .3))
|
||||
|
||||
#' For `time_ms.stop` this looks even weirder.
|
||||
#'
|
||||
|
||||
#' # After which time interval does the table reset?
|
||||
|
||||
#' I cannot see this in the data at all. Or can I? Has this something to do
|
||||
#' with the weird behavior of `time_ms`?
|
||||
|
||||
111
code/questions_programming-input.R
Normal file
111
code/questions_programming-input.R
Normal file
@ -0,0 +1,111 @@
|
||||
#' ---
|
||||
#' title: "Programming input"
|
||||
#' author: "Nora Wickelmaier"
|
||||
#' date: "`r Sys.Date()`"
|
||||
#' output: html_document
|
||||
#' ---
|
||||
|
||||
#+ include = FALSE
|
||||
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code")
|
||||
|
||||
#+
|
||||
dat0 <- read.table("../data/rawdata_logfiles_small.csv", sep = ";", header = TRUE)
|
||||
dat0$date <- as.POSIXct(dat0$date) # create date object
|
||||
|
||||
# Remove irrelevant events
|
||||
dat <- subset(dat0, !(dat0$event %in% c("Start Application", "Show Application")))
|
||||
str(dat)
|
||||
|
||||
# make data better manageable
|
||||
tmp <- dat[!dat$event %in% c("Transform start", "Transform stop"), ]
|
||||
rownames(tmp) <- NULL
|
||||
|
||||
#' # Add `trace` variable for closing events
|
||||
|
||||
tmp$trace <- NA
|
||||
last_event <- tmp$event[1]
|
||||
aws <- unique(tmp$artwork)[unique(tmp$artwork) != "glossar"]
|
||||
|
||||
for (art in aws) { # select artwork
|
||||
|
||||
for (i in 1:nrow(tmp)) { # go through rows
|
||||
|
||||
if (last_event == "Show Info" & tmp$artwork[i] == art) {
|
||||
tmp$trace[i] <- i
|
||||
j <- i
|
||||
|
||||
} else if (last_event == "Show Front" & tmp$artwork[i] == art) {
|
||||
tmp$trace[i] <- j
|
||||
|
||||
} else if (!(last_event %in% c("Show Info", "Show Front")) &
|
||||
tmp$artwork[i] == art) {
|
||||
tmp$trace[i] <- j
|
||||
}
|
||||
|
||||
if (i <= nrow(tmp)) {
|
||||
last_event <- tmp$event[i + 1]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
head(tmp[, c("artwork", "event", "trace")], 50)
|
||||
|
||||
#' # Find artwork for glossar entry
|
||||
|
||||
glossar_files <- unique(tmp[tmp$artwork == "glossar", "popup"])
|
||||
|
||||
# Load lookup table for artworks and glossar files
|
||||
load("../data/glossar_dict.RData")
|
||||
lut <- glossar_dict[glossar_dict$glossar_file %in% glossar_files, ]
|
||||
|
||||
# Fill in trace variable based on last `Show Info`
|
||||
for (file in lut$glossar_file) {
|
||||
|
||||
artwork_list <- unlist(lut[lut$glossar_file == file, "artwork"])
|
||||
|
||||
for (i in seq_len(nrow(tmp))) {
|
||||
|
||||
if (tmp$event[i] == "Show Info") {
|
||||
|
||||
current_artwork <- tmp[i, "artwork"]
|
||||
j <- i
|
||||
k <- i
|
||||
|
||||
} else {
|
||||
|
||||
current_artwork <- current_artwork
|
||||
|
||||
}
|
||||
|
||||
if (tmp$event[i] == "Show Front" & tmp$artwork[i] == current_artwork) {
|
||||
# make sure artwork has not been closed, yet!
|
||||
k <- i
|
||||
}
|
||||
|
||||
if (tmp$artwork[i] == "glossar" &
|
||||
(current_artwork %in% artwork_list) &
|
||||
tmp$popup[i] == file & (j-k == 0)) {
|
||||
|
||||
tmp[i, "trace"] <- tmp[j, "trace"]
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
tmp[tmp$artwork == "glossar", c("artwork", "event", "popup", "trace")]
|
||||
|
||||
proportions(table(is.na(tmp$trace[tmp$artwork == "glossar"])))
|
||||
# --> finds about half of the glossar entries for small data set...
|
||||
|
||||
# REMEMBER: It can never be 100% correct, since it is always possible that
|
||||
# several cards are open and that they link to the same glossar entry
|
||||
|
||||
# How many glossar_files are only associated with one artwork?
|
||||
lut[sapply(lut$artwork, length) == 1, "glossar_file"]
|
||||
|
||||
# TODO: Fill in the ones that are associated with one artwork
|
||||
# --> Can't come up with something -- maybe ask Philipp???
|
||||
|
||||
# TODO: How to check if one of the former "Show Infos" is correct
|
||||
# --> Can't come up with something -- maybe ask Philipp???
|
||||
|
||||
Loading…
Reference in New Issue
Block a user