diff --git a/code/00_current-anaylsis.R b/code/00_current-anaylsis.R
index 2db5af9..ec0fae9 100644
--- a/code/00_current-anaylsis.R
+++ b/code/00_current-anaylsis.R
@@ -29,7 +29,7 @@ library(bupaverse)
 
 #--------------- (1) Read data ---------------
 
-dat <- read.table("../data/haum/event_logfiles_glossar_2023-10-29_10-26-42.csv",
+dat <- read.table("../data/haum/event_logfiles_glossar_2023-11-03_17-46-28.csv",
                   sep = ";", header = TRUE)
 dat$date <- as.POSIXct(dat$date)
 dat$date.start <- as.POSIXct(dat$date.start)
@@ -312,10 +312,10 @@ table(tmp$artwork)
 
 # select the ones most often (I am aiming for 10...)
 barplot(table(tmp$artwork))
-abline(h = 12000, col = "red")
-which(table(tmp$artwork) > 12000)
+abline(h = 14000, col = "red")
+which(table(tmp$artwork) > 14000)
 
-often080 <- names(which(table(tmp$artwork) > 12000))
+often080 <- names(which(table(tmp$artwork) > 14000))
 
 alog <- activitylog(dat[dat$artwork %in% often080, ],
                     case_id = "case",
diff --git a/code/01_preprocessing_haum.R b/code/01_preprocessing_haum.R
index ea8e5f3..4df1f24 100644
--- a/code/01_preprocessing_haum.R
+++ b/code/01_preprocessing_haum.R
@@ -42,9 +42,9 @@ datraw <- read.table("../data/haum/raw_logfiles_2023-10-25_16-20-45.csv",
 
 datlogs <- create_eventlogs(datraw,
            xmlpath = "../data/haum/ContentEyevisit/eyevisit_cards_light/",
-           glossar = TRUE)
+           glossar = TRUE, save = TRUE)
 
-artworks <- unique(na.omit(datlogs$artwork))
+artworks <- unique(datlogs$artwork)
 topics <- extract_topics(artworks, xmlfiles = paste0(artworks, ".xml"),
                          xmlpath = "../data/haum/ContentEyevisit/eyevisit_cards_light/")