121 lines
4.0 KiB
R
121 lines
4.0 KiB
R
# 05_check-traces.R
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#
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# content: (1) Look at broken trace
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# (2) Function to find broken traces
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# (3) DFG for complete data
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# (4) Export data frame for analyses
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#
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# input: results/event_logfiles_2024-02-21_16-07-33.csv
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# results/raw_logfiles_2024-02-21_16-07-33.csv
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# output: results/eventlogs_pre-corona_cleaned.RData
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# results/eventlogs_pre-corona_cleaned.csv
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# ../../thesis/figures/dfg_complete_WFnet_R.pdf
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#
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# last mod: 2024-03-23
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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#--------------- (1) Look at broken trace ---------------
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datraw <- read.table("results/raw_logfiles_2024-02-21_16-07-33.csv",
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header = TRUE, sep = ";")
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datlogs <- read.table("results/event_logfiles_2024-02-21_16-07-33.csv",
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colClasses = c("character", "character", "POSIXct",
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"POSIXct", "character", "integer",
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"numeric", "character", "character",
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rep("numeric", 3), "character",
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"character", rep("numeric", 11),
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"character", "character"),
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sep = ";", header = TRUE)
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artwork <- "176"
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fileId <- c('2017_06_16-13_49_00.log', '2017_06_16-13_59_00.log')
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path <- 106098
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datraw[datraw$item == artwork & datraw$fileId %in% fileId, ]
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datlogs[datlogs$path == path, ]
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#--------------- (2) Function to find broken traces ---------------
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tmp <- datlogs[datlogs$event != "move", ]
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check_traces <- function(data) {
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datagg <- aggregate(event ~ path, data,
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function(x) ifelse("openPopup" %in% x, T, F))
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paths <- datagg$path[datagg$event]
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datcheck <- data[data$path %in% paths, c("path", "event")]
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datcheck <- datcheck[!duplicated(datcheck), ]
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datcheck <- datcheck[order(datcheck$path), ]
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retval <- NULL
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for (path in unique(datcheck$path)) {
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check <- !all(as.character(datcheck$event[datcheck$path == path]) ==
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c("flipCard", "openTopic", "openPopup"))
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retval <- rbind(retval, data.frame(path, check))
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}
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retval
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}
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check <- check_traces(tmp)
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check[check$check, ]
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#--------------- (3) DFG for complete data ---------------
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tmp <- datlogs[datlogs$path != 106098, ]
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tmp$start <- tmp$date.start
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tmp$complete <- tmp$date.stop
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alog <- bupaR::activitylog(tmp,
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case_id = "path",
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activity_id = "event",
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resource_id = "item",
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timestamps = c("start", "complete"))
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dfg <- processmapR::process_map(alog,
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type_nodes = processmapR::frequency("relative", color_scale = "Greys"),
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sec_nodes = processmapR::frequency("absolute"),
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type_edges = processmapR::frequency("relative", color_edges = "#FF6900"),
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sec_edges = processmapR::frequency("absolute"),
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rankdir = "LR",
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render = FALSE)
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processmapR::export_map(dfg,
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file_name = paste0("../../thesis/figures/dfg_complete_WFnet_R.pdf"),
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file_type = "pdf")
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rm(tmp)
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#--------------- (4) Export data frame for analyses ---------------
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datlogs$event <- factor(datlogs$event, levels = c("move", "flipCard",
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"openTopic",
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"openPopup"))
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datlogs$topic <- factor(datlogs$topic)
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datlogs$weekdays <- factor(weekdays(datlogs$date.start),
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levels = c("Montag", "Dienstag", "Mittwoch",
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"Donnerstag", "Freitag", "Samstag",
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"Sonntag"),
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labels = c("Monday", "Tuesday", "Wednesday",
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"Thursday", "Friday", "Saturday",
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"Sunday"))
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# Select data pre Corona
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dat <- datlogs[as.Date(datlogs$date.start) < "2020-03-13", ]
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# Remove corrupt trace
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dat <- dat[dat$path != 106098, ]
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save(dat, file = "results/eventlogs_pre-corona_cleaned.RData")
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write.table(dat,
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file = "results/eventlogs_pre-corona_cleaned.csv",
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sep = ";",
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quote = FALSE,
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row.names = FALSE)
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