--- output: github_document --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" ) ``` # R package mtt ![mtt package](man/figures/logo.png) This package was created to process log files obtained from multi-touch tables at the Leibniz-Institut für Wissensmedien (IWM). ## Installation It can be installed via `devtools::install_git("https://gitea.iwm-tuebingen.de/R/mtt.git")` If you get an error message, you probably need to install `git2r`first with `install.packages("git2r")`. The package depends on the following R packages * `dplyr` * `pbapply` * `XML` * `lubridate` so make sure they are installed as well. # Multi-Touch Table The multi-touch table at the Herzog-Anton-Ulrich-Museum (HAUM) in Braunschweig gives visitors of the Museum the opportunity to interact with about 70 artworks and 3 virtual cards containing information about the museum and its layout. The table was installed at the museum in October 2016 and since November 2016 log files from interactions of visitors of the museum have been collected. These log files are in an unstructured format and cannot be easily analyzed. The purpose of the following document is to describe how the data haven been transformed and which decisions have been made along the way. # Data structure The log files contain lines that indicate the beginning and end of possible activities that can be performed when interacting with the artworks on the table. The layout of the table looks like pictures have been tossed on a large table. Every artwork is visible at the start configuration. People can move the pictures on the table, they can be scaled and rotated. Additionally, the virtual picture cards can be flipped in order to find more information of the artwork on the "back" of the card. One has to press a little `i` for more information in one of the bottom corners of the card. On the back of the card two to six information cards can be found with a teaser text about a certain topic. These topic cards can be opened and a hypertext with detailed information opens. Within these hypertexts certain technical terms can be clicked for lay people to get more information. This also opens up a pop-up. The events encoded in the raw log files therefore have the following structure. ``` "Start Application" --> Start Application "Show Application" "Transform start" --> Move "Transform stop" "Show Info" --> Flip Card "Show Front" "Artwork/OpenCard" --> Open Topic "Artwork/CloseCard" "ShowPopup" --> Open Popup "HidePopup" ``` The right side shows what events can be extracted from these raw lines. The "Start Application" is not an event in the original sense since it only indicates if the table was started or maybe reset itself. This is not an interaction with the table and therefore not interesting in itself. All "Start Application" and "Show Application" are therefore excluded from the data when further processed and are only in the raw log files. # Parsing the raw log files The first step is to parse the raw log files that are stored by the application as text files in a rather unstructured format to a format that can be read by common statistics software packages. The data are therefore transferred to a spread sheet format. The following section describes what problems were encountered while doing this. ## Corrupt lines When reading the files containing the raw logs into R, a warning appears that says ``` Warning messages: incomplete final line found on '2016/2016_11_18-11_31_0.log' incomplete final line found on '2016/2016_11_18-11_38_30.log' incomplete final line found on '2016/2016_11_18-11_40_36.log' ... ``` When you open these files, it looks like the last line contains some binary content. It is unclear why and how this happens. So when reading the data, these lines were removed. A warning will be given that indicates how many files have been affected. ## Extracted variables from raw log files The following variables (columns in the data frame) are extracted from the raw log file: * `fileId`: Containing the zero-left-padded file name of the raw log file the data line has been extracted from * `folder`: The folder names in which the raw log files haven been organized in. For the HAUM data set, the data are sorted by year (folders 2016, 2017, 2018, 2019, 2020, 2021, 2022, and 2023). * `date`: Extracted timestamp from the raw log file in the format `yyyy-mm-dd hh:mm:ss`. * `timeMs`: Containing a timestamp in Milliseconds that restarts with every new raw log files. * `event`: Start and stop event tags. See above for possible values. * `item`: Identifier of the different items. This is a three-digit (left-padded) number. The numbers of the items correspond to the folder names in `/ContentEyevisit/eyevisit_cards_light/` and were orginally taken from the museums catalogue. * `popup`: Name of the pop-up opened. This is only interesting for "openPopup" events. * `topic`: The number of the topic card that has been opened at the back of the item card. See below for a more detailed description what these numbers mean. * `x`: Value of x-coordinate in pixel on the 4K-Display ($3840 \times 2160$). * `y`: Value of y-coordinate in pixel. * `scale`: Number in 128 bit that indicates how much the card has been scaled. * `rotation`: Degree of rotation from start configuration. ## Variables after "closing of events" The raw log data consist of start and stop events for each event type. After preprocessing four event types are extracted: `move`, `flipCard`, `openTopic`, and `openPopup`. Except for the `move` events, which can occur at any time when interacting with an item card on the table, the events have a hierarchical order: An item card first needs to be flipped (`flipCard`), then the topic cards on the back of the card can be opened (`openTopic`), and finally pop-ups on these topic cards can be opened (`openPopup`). This implies that the event `openPopup` can only be present for a certain item, if the card has already been flipped (i.e., an event `flipCard` for the same item has already occured). After preprocessing, the data frame is now in a wide format with columns for the start and the stop of each event and contains the following variables: * `fileId.start` / `fileId.stop`: See above. * `date.start` / `date.stop`: See above. * `folder`: Containing the folder name (see above). * `case`: A numerical variable indicating cases in the data. A "case" indicates an interaction interval and could be defined in different ways. Right now a new case begins, when no event occurred when no new path started for 20 seconds or longer. * `path`: A path is defined as one interaction with one item. A path can either start with a `flipCard` event or when an item has been touched for the first time within this case. A path ends with the item card being flipped close again or with the last movement of the card within this case. One case can contain several paths with the same item when the item is flipped open and flipped close again several times within a short time. * `glossar`: An indicator variable with values 0/1 that tracks if a pop-up has been opened from the glossar folder. These pop-ups can be assigned to the wrong item since it is not possible to do this algorithmically. It is possible that two items are flipped open that could both link to the same pop-up from a glossar. The indicator variable is left as a variable, so that these pop-ups can be easily deleted from the data. Right now, glossar entries can be ignored completely by setting an argument and this is done by default. Using the pop-ups from the glossar will need a lot more love, before it behaves satisfactorily. * `event`: Indicating the event. Can take tha values `move`, `flipCard`, `openTopic`, and `openPopup`. * `item`: Identifier of the different artworks and information cards. This is a three-digit (left-padded) number. See above. * `timeMs.start` / `timeMs.stop`: See above. * `duration`: Calculated by $timeMs.stop - timeMs.start$ in Milliseconds. Needs to be adjusted for events spanning more than one log file by a factor of $60,000 \times \text{number of logfiles}$. See below for details. * `topic`: See above. * `popup`: See above. * `x.start` / `x.stop`: See above. * `y.start` / `y.stop`: See above. * `distance`: Euclidean distande calculated from $(x.start, y.start)$ and $(x.stop, y.stop)$. * `scale.start` / `scale.stop`: See above. * `scaleSize`: Relative scaling of item card, calculated by $\frac{scale.stop}{scale.start}$. * `rotation.start` / `rotation.stop`: See above. * `rotationDegree`: Difference of rotation from $rotation.stop$ to $rotation.start$. ## How unclosed events are handled Events do not necessarily need to be completed. A person can, e.g., leave the table and not flip the item card close again. For `flipCard`, `openTopic`, and `openPopup` the data frame contains `NA` when the event does not complete. For `move` events it happens quite often that a start event follows a start event and a stop event follows a stop event. Technically a move event cannot *not* be finished and the number of events without a start or stop indicate that the time resolution was not sufficient to catch all these events accurately. Double start and stop `move` events have therefore been deleted from the data set. ## Additional meta data For the HAUM data, I added meta data on state holidays and school vacations. This led to the following additional variables: * `holiday` * `vacations` # Problems and how I handled them This lists some problems with the log data that required decisions. These decisions influence the outcome and maybe even the data quality. Hence, I tried to document how I handled these problems and explain the decisions I made. ## Weird behavior of `timeMs` and neg. `duration` values `timeMs` resets itself every time a new log file starts. This means that the durations of events spanning more than one log file must be adjusted. Instead of just calculating $timeMs.stop - timeMs.start$, `timeMs.start` must be subtracted from the maximum duration of the log file where the event started ($600,000 ms$) and the `timeMs.stop` must be added. If the event spans more than two log files, a multiple of $600,000$ must be taken, e.g. for three log files it must be: $2 \times 600,000 - timeMs.start + timeMs.stop$ and so on. ```{r timems, echo = FALSE, results = FALSE, fig.show = TRUE} # Read data datraw <- read.table("../../MDS/2023ss/60100_master_thesis/analysis/code/results/raw_logfiles_2024-02-21_16-07-33.csv", sep = ";", header = TRUE) plot(timeMs ~ as.factor(fileId), datraw[1:5000,], xlab = "fileId") ``` The boxplot shows that we have a continuous range of values within one log file but that `timeMs` does not increase over log files. I kept `timeMs.start` and `timeMs.stop` and also `fileId.start` and `fileId.stop` in the data frame, so it is clear when events span more than one log file. ## Left padding of file IDs The file names of the raw log files are automatically generated and contain a timestamp. This timestamp is not well formed. First, it contains an incorrect month. The months go from 0 to 11 which means, that the file name `2016_11_15-12_12_57.log` was collected on December 15, 2016 at 12:12 pm. Another problem is that the file names are not zero left padded, e.g., `2016_11_15-12_2_57.log`. This file was collected on December 15, 2016 at 12:02 pm and therefore before the file above. But most sorting algorithms, will sort these files in the order shown below. In order to preprocess the data and close events that belong together, the data need to be sorted by events and artworks repeatedly. In order to get them back in the correct time order, it is necessary to order them based on three variables: `fileId.start`, `date.start` and `timeMs.start`. The file IDs therefore need to sort in the correct order (again see below for example). I zero left padded the log file names within the data frame using it as an identifier. These "file names" do not correspond exactly to the original raw log file names. This needs to be kept in mind when doing any kind of matching etc. ``` ## what it looked like before left padding # 1422 ../data/haum_logs_2016-2023/_2016b/2016_11_15-12_2_57.log 2016-12-15 12:12:56 599671 Transform start 076 076.xml NA 2092.25 2008.00 0.3000000 13.26874254 # 1423 ../data/haum_logs_2016-2023/_2016b/2016_11_15-12_12_57.log 2016-12-15 12:12:57 621 Transform start 076 076.xml NA 2092.25 2008.00 0.3000000 13.26523465 # 1424 ../data/haum_logs_2016-2023/_2016b/2016_11_15-12_12_57.log 2016-12-15 12:12:57 677 Transform stop 076 076.xml NA 2092.25 2008.00 0.2997736 13.26239605 # 1425 ../data/haum_logs_2016-2023/_2016b/2016_11_15-12_12_57.log 2016-12-15 12:12:57 774 Transform start 076 076.xml NA 2092.25 2008.00 0.2999345 13.26239605 # 1426 ../data/haum_logs_2016-2023/_2016b/2016_11_15-12_12_57.log 2016-12-15 12:12:57 850 Transform stop 076 076.xml NA 2092.25 2008.00 0.2997107 13.26223362 # 1427 ../data/haum_logs_2016-2023/_2016b/2016_11_15-12_2_57.log 2016-12-15 12:12:57 599916 Transform stop 076 076.xml NA 2092.25 2008.00 0.2997771 13.26523465 ## what it looks like now # 1422 2016_11_15-12_02_57.log 2016-12-15 12:12:56 599671 Transform start 076 076.xml NA 2092.25 2008.00 0.3000000 13.26874254 # 1423 2016_11_15-12_02_57.log 2016-12-15 12:12:57 599916 Transform stop 076 076.xml NA 2092.25 2008.00 0.2997771 13.26523465 # 1424 2016_11_15-12_12_57.log 2016-12-15 12:12:57 621 Transform start 076 076.xml NA 2092.25 2008.00 0.3000000 13.26523465 # 1425 2016_11_15-12_12_57.log 2016-12-15 12:12:57 677 Transform stop 076 076.xml NA 2092.25 2008.00 0.2997736 13.26239605 # 1426 2016_11_15-12_12_57.log 2016-12-15 12:12:57 774 Transform start 076 076.xml NA 2092.25 2008.00 0.2999345 13.26239605 # 1427 2016_11_15-12_12_57.log 2016-12-15 12:12:57 850 Transform stop 076 076.xml NA 2092.25 2008.00 0.2997107 13.26223362 ``` ## Timestamps repeat The timestamps in the `date` variable record year, month, day, hour, minute and seconds. Since one second is not a very short time interval for a move on a touch display, this is not fine grained enough to bring events into the correct order, meaning there are events from the same log file having the same timestamp and even events from different log files having the same timestamp. The log files get written about every 10 minutes (which can easily be seen when looking at the file names of the raw log files). So in order to get events in the correct order, it is necessary to first order by file ID, within file ID then sort by timestamp `date` and then within these more coarse grained timestamps sort be `timeMs`. But as explained above, `timeMs` can only be sorted within one file ID, since they do not increase consistently over log files, but have a new setoff for each raw log file. ## x,y-coordinates outside of display range The display of the Multi-Touch-Table is a 4K-display with 3840 x 2160 pixels. When you plot the start and stop coordinates, the display is clearly distinguishable. However, a lot of points are outside of the display range. This can happen, when the art objects are scaled and then moved to the very edge of the table. Then it will record pixels outside of the table. These are actually valid data points and I will leave them as is. ```{r xycoord} datlogs <- read.table("../../MDS/2023ss/60100_master_thesis/analysis/code/results/event_logfiles_2024-02-21_16-07-33.csv", sep = ";", header = TRUE) par(mfrow = c(1, 2)) plot(y.start ~ x.start, datlogs) abline(v = c(0, 3840), h = c(0, 2160), col = "blue", lwd = 2) plot(y.stop ~ x.stop, datlogs) abline(v = c(0, 3840), h = c(0, 2160), col = "blue", lwd = 2) aggregate(cbind(x.start, x.stop, y.start, y.stop) ~ 1, datlogs, mean) ``` ## Pop-ups from glossar cannot be assigned to a specific item All the information, pictures and texts for the topics and pop-ups are stored in `/data/haum/ContentEyevisit/eyevisit_cards_light/`. Among other things, each folder contains XML-files with the information about any technical terms that can be opened from the hypertexts on the topic cards. Often these information are item dependent and then the corresponding XML-file is in the folder for this item. Sometimes, however, more general terms can be opened. In order to avoid multiple files containing the same information, these were stored in a folder called `glossar` and get accessed from there. The raw log files only contain the path to this glossar entry and did not record from which item it was accessed. I tried to assign these glossar entries to the correct items. The (very heuristic) approach was this: 1. Create a lookup table with all XML-file names (possible pop-ups) from the glossar folder and what items possibly call them. This was stored as an `RData` object for easier handling but should maybe be stored in a more interoperable format. 2. I went through all possible pop-ups in this lookup table and stored the items that are associated with it. 3. I created a sub data frame without move events (since they can never be associated with a pop-up) and went through every line and looked up if an item and a topic card had been opened. If this was the case and a glossar entry came up before the item was closed again, I assigned this item to the glossar entry. This is heuristic since it is possible that several topic cards from different items are opened simultaneously and the glossar pop-up could be opened from either one (it could even be more than two, of course). In these cases the item that was opened closest to the glossar pop-up has been assigned, but this can never be completely error free. And this heuristic only assigns a little more than half of the glossar entries. Since my heuristic only looks for the last item that has been opened and if this item is a possible candidate it misses all glossar pop-ups where another item has been opened in between. This is still an open TODO to write a more elaborate algorithm. All glossar pop-ups that do not get matched with an item are removed from the data set with a warning if the argument `glossar = TRUE` is set. Otherwise the glossar entries will be ignored completely. ## Assign a `case` variable based on "time heuristic" One thing needed in order to work with the data set and use it for machine learning algorithms like process mining, is a variable that tries to identify a case. A case variable will structure the data frame in a way that navigation behavior can actually be investigated. However, we do not know if several people are standing around the table interacting with it or just one very active person. The simplest way to define a case variable is to just use a time limit between events. This means that when the table has not been interacted with for, e.g., 20 seconds than it is assumed that a person moved on and a new person started interacting with the table. This is the easiest heuristic and implemented at the moment. Process mining shows that this simple approach works in a way that the correct process gets extracted by the algorithm. In order to investigate user behavior on a more fine grained level, it will be necessary to come up with a more elaborate approach. A better, still simple approach, could be to use this kind of time limit and additionally look at the distance between items interacted with within one time window. When items are far apart it seems plausible that more than one person interacted with them. Very short time lapses between events on different items could also be an indicator that more than one person is interacting with the table. ## Assign a `path` variable The `path` variable is supposed to show one interaction trace with one artwork. Meaning it starts when an artwork is touched or flipped and stops when it is closed again. It is easy to assign a path from flipping a card over opening (maybe several) topics and pop-ups for this artwork card until closing this card again. But one would like to assign the same path to move events surrounding this interaction. Again, this is not possible in an algorithmic way but only heuristically. Again, I used a time cutoff for this. First, if a `move` event occurs, it is checked, if the same item has been flipped less than 20 seconds beforehand. If yes, the same path indicator is assigned to this `move`. If not, temporarily a new "move indicator" is assigned. Then, a "backward pass" is applied, where it is checked if the same item is opened less than 20 seconds _after_ the event occurs. If yes, that path indicator is assigned. For all the remaining moves, a new path number is assigned. This corresponds to items being moved without being flipped. ## A `move` event does not record any change Most of the events in the log files are move events. Additionally, many of these move events are recorded but they do not indicate any change, meaning the only difference is the timestamp. All other variables indicating moves like `x.start` and `x.stop`, `rotation.start` and `rotation.stop` etc. do not show _any_ change. They represent about 2/3 of all move events. These events are probably short touches of the table without an actual interaction. They were therefore removed from the data set. ## Card indices go from 0 to 7 (instead of 0 to 5 as expected) In the beginning I thought that the number for topics was the index of where the card was presented on the back of the item. But this is not correct. It is the number of the topic. There are eight topics in total: ``` Indices for topics: 0 artist 1 thema 2 komposition 3 leben des kunstwerks 4 details 5 licht und farbe 6 extra info 7 technik ``` On the back of items, there can be between 2 to 6 topic cards. Several of these topic cards can be about the same topic, e.g., there can be two topic cards assigned to the topic `thema`. It is impossible to find out if the same topic card was opened several times or if different topic cards with the same topic were opened from the same item. See example below for item "001". ```{r topics, echo = FALSE} devtools::load_all() items <- sprintf("%03d", unique(datlogs$item)) topics <- extract_topics(items, xmlfiles = paste0(items, ".xml"), xmlpath = "../../MDS/2023ss/60100_master_thesis/analysis/data/haum/ContentEyevisit/eyevisit_cards_light/") head(topics) ``` ## New artworks "504" and "505" starting October 2022 When I read in the complete data frame for the first time, all of the sudden there were 72 instead of 70 items. It seems like these two artworks appear on October 21, 2022. ```{r newitems} summary(as.Date(datraw[datraw$item %in% c("504", "505"), "date"])) ``` The artworks seem to be have updated in general after October 21, 2022. The following table shows which items were presented in which years. ```{r years} xtabs(~ item + lubridate::year(date.start), datlogs) ``` It shows that the artworks haven been updated after the Corona pandemic. I think, the table was also moved to a different location at that point.