177 lines
7.9 KiB
R
177 lines
7.9 KiB
R
# 09_user-navigation.R
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#
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# content: (1) Read data
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# (2) Extract characteristics for cases
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# (3) Select features for navigation behavior
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# (4) Export data frames
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#
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# input: results/event_logfiles_2024-02-21_16-07-33.csv
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# output: results/dataframes_case_2019.RData
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# results/centrality_cases.RData
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#
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# last mod: 2024-03-22
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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source("R_helpers.R")
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#--------------- (1) Read data ---------------
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load("results/eventlogs_pre-corona_cleaned.RData")
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# Select one year to handle number of cases
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dat <- dat[as.Date(dat$date.start) > "2018-12-31" &
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as.Date(dat$date.start) < "2020-01-01", ]
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#--------------- (2) Extract characteristics for cases ---------------
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datcase <- aggregate(cbind(distance, scaleSize, rotationDegree) ~
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case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
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datcase$length <- aggregate(item ~ case, dat, length)$item
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eventtab <- aggregate(event ~ case, dat, table)["case"]
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eventtab$nmove <- aggregate(event ~ case, dat, table)$event[, "move"]
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eventtab$nflipCard <- aggregate(event ~ case, dat, table)$event[, "flipCard"]
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eventtab$nopenTopic <- aggregate(event ~ case, dat, table)$event[, "openTopic"]
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eventtab$nopenPopup <- aggregate(event ~ case, dat, table)$event[, "openPopup"]
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topictab <- aggregate(topic ~ case, dat, table)["case"]
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topictab$artist <- aggregate(topic ~ case, dat, table)$topic[, 1]
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topictab$details <- aggregate(topic ~ case, dat, table)$topic[, 2]
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topictab$extra_info <- aggregate(topic ~ case, dat, table)$topic[, 3]
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topictab$komposition <- aggregate(topic ~ case, dat, table)$topic[, 4]
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topictab$leben_des_kunstwerks <- aggregate(topic ~ case, dat, table)$topic[, 5]
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topictab$licht_und_farbe <- aggregate(topic ~ case, dat, table)$topic[, 6]
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topictab$technik <- aggregate(topic ~ case, dat, table)$topic[, 7]
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topictab$thema <- aggregate(topic ~ case, dat, table)$topic[, 8]
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datcase <- datcase |>
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merge(eventtab, by = "case", all = TRUE) |>
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merge(topictab, by = "case", all = TRUE)
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rm(eventtab, topictab)
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datcase$ntopiccards <- aggregate(topic ~ case, dat,
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function(x) ifelse(all(is.na(x)), NA,
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length(na.omit(x))), na.action =
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NULL)$topic
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datcase$ntopics <- aggregate(topic ~ case, dat,
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function(x) ifelse(all(is.na(x)), NA,
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length(unique(na.omit(x)))), na.action =
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NULL)$topic
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datcase$nitems <- aggregate(item ~ case, dat, function(x)
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length(unique(x)), na.action = NULL)$item
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datcase$npaths <- aggregate(path ~ case, dat, function(x)
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length(unique(x)), na.action = NULL)$path
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datcase$vacation <- aggregate(vacation ~ case, dat,
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function(x) ifelse(all(is.na(x)), 0, 1),
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na.action = NULL)$vacation
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datcase$holiday <- aggregate(holiday ~ case, dat,
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function(x) ifelse(all(is.na(x)), 0, 1),
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na.action = NULL)$holiday
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datcase$weekend <- aggregate(weekdays ~ case, dat,
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function(x) ifelse(any(x %in% c("Saturday", "Sunday")), 1, 0),
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na.action = NULL)$weekdays
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datcase$morning <- aggregate(date.start ~ case, dat,
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function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
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na.action = NULL)$date.start
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dat_split <- split(dat, ~ case)
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dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
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dat_minmax <- dplyr::bind_rows(dat_list)
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datcase$min_time <- aggregate(min_time ~ case, dat_minmax, unique)$min_time
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datcase$max_time <- aggregate(max_time ~ case, dat_minmax, unique)$max_time
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datcase$duration <- datcase$max_time - datcase$min_time
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datcase$min_time <- NULL
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datcase$max_time <- NULL
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artworks <- unique(dat$item)[!unique(dat$item) %in% c("501", "502", "503")]
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datcase$infocardOnly <- pbapply::pbsapply(dat_split, check_infocards, artworks = artworks)
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# Clean up NAs
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datcase$distance <- ifelse(is.na(datcase$distance), 0, datcase$distance)
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datcase$scaleSize <- ifelse(is.na(datcase$scaleSize), 1, datcase$scaleSize)
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datcase$rotationDegree <- ifelse(is.na(datcase$rotationDegree), 0, datcase$rotationDegree)
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datcase$artist <- ifelse(is.na(datcase$artist), 0, datcase$artist)
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datcase$details <- ifelse(is.na(datcase$details), 0, datcase$details)
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datcase$extra_info <- ifelse(is.na(datcase$extra_info), 0, datcase$extra_info)
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datcase$komposition <- ifelse(is.na(datcase$komposition), 0, datcase$komposition)
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datcase$leben_des_kunstwerks <- ifelse(is.na(datcase$leben_des_kunstwerks), 0, datcase$leben_des_kunstwerks)
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datcase$licht_und_farbe <- ifelse(is.na(datcase$licht_und_farbe), 0, datcase$licht_und_farbe)
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datcase$technik <- ifelse(is.na(datcase$technik), 0, datcase$technik)
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datcase$thema <- ifelse(is.na(datcase$thema), 0, datcase$thema)
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datcase$ntopics <- ifelse(is.na(datcase$ntopics), 0, datcase$ntopics)
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datcase$ntopiccards <- ifelse(is.na(datcase$ntopiccards), 0, datcase$ntopiccards)
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#--------------- (3) Select features for navigation behavior ---------------
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# Features for navigation types for MTT:
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# - Scanning / Overviewing:
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# * Proportion of artworks looked at is high
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# * Duration per artwork is low: "ave_duration_item" / datcase$duration
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# - Exploring:
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# * Looking at additional information is high
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# - Searching / Studying:
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# * Proportion of artworks looked at is low
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# * Opening few cards
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# datcase$nflipCard / mean(datcase$nflipCard) or median(datcase$nflipCard) is low
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# * but for most cards popups are opened:
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# datcase$nopenPopup / datcase$nflipCard is high
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# - Wandering / Flitting:
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# * Proportion of moves is high
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# * Duration per case is low:
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# datcase$duration / mean(datcase$duration) or median(datcase$duration)
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# * Duration per artwork is low: "ave_duration_item" / datcase$duration
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dattree <- data.frame(case = datcase$case,
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PropItems = datcase$nitems / length(unique(dat$item)),
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SearchInfo = (datcase$nopenTopic +
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datcase$nopenPopup) / datcase$length,
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PropMoves = datcase$nmove / datcase$length,
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PathLinearity = datcase$nitems / datcase$npaths,
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Singularity = datcase$npaths / datcase$length
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)
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# centrality <- pbapply::pbsapply(dattree$case, get_centrality, data = dat)
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# save(centrality, file = "results/centrality_cases.RData")
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load("results/centrality_cases.RData")
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dattree$BetweenCentrality <- centrality
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# Average duration per item
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dat_split <- split(dat[, c("item", "case", "path", "timeMs.start", "timeMs.stop")], ~ path)
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dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
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dat_minmax <- dplyr::bind_rows(dat_list)
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tmp <- aggregate(min_time ~ path, dat_minmax, unique)
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tmp$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
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tmp$duration <- tmp$max_time - tmp$min_time
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tmp$case <- aggregate(case ~ path, dat_minmax, unique)$case
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dattree$AvDurItem <- aggregate(duration ~ case, tmp, mean)$duration
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rm(tmp)
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# Indicator variable if table was used as info terminal only
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dattree$InfocardOnly <- factor(datcase$infocardOnly, levels = 0:1,
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labels = c("no", "yes"))
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# Add pattern to datcase; loosely based on Bousbia et al. (2009)
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dattree$Pattern <- "Dispersion"
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dattree$Pattern <- ifelse(dattree$PathLinearity > 0.8, "Scholar",
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dattree$Pattern)
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dattree$Pattern <- ifelse(dattree$PathLinearity <= 0.8 &
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dattree$BetweenCentrality >= 0.5, "Star",
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dattree$Pattern)
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dattree$Pattern <- factor(dattree$Pattern)
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dattree$AvDurItemNorm <- normalize(dattree$AvDurItem)
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#--------------- (4) Export data frames ---------------
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save(dat, datcase, dattree, file = "results/dataframes_case_2019.RData")
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