5.1 Setup

Show the code
Show the code
source("_common.r")
Show the code
tar_load(c(
 course_and_uni_per_visit,
n_action,
n_action_type,
time_visit_wday,
time_visit_wday_fingerprint,
data_separated_filtered_date_uni_course,
data_separated_filtered
))

Was machen die Visitors eigentlich? Und wie oft?

5.2 Häufigkeiten

Für das Objekt n_action_type wurde die Spalte subtitle in den Langformat-Daten ausgewertet, s. Funktionsdefinition von count_user_action_type.

Show the code
n_action_type |>
  head(30)

Achtung: Es kann sinnvoller sein, alternativ zu dieser Analyse die Analyse auf Basis von eventcategory heranzuziehen. Dort werden alle Arten von Events berücksichtigt. Hier, in der vorliegenden, nur ausgewählte Events.

5.2.1 Nach bestimmten Kategorien

Show the code
n_action_type_counted <-
  n_action_type |>
  drop_na() |>
  count(category, sort = TRUE) |>
  mutate(prop = round(n / sum(n), 2))

n_action_type_counted |>
  gt()
category n prop
video 845813 0.84
click_slideChange 61934 0.06
visit_page 55551 0.06
Media item 17485 0.02
login 6550 0.01
in_media_search 3422 0.00
Search Results Count 2856 0.00
click_topic 2799 0.00
Medien 1646 0.00
logout 1495 0.00
Kanäle 1395 0.00
GESOA 1358 0.00
click_channelcard 848 0.00
Evaluation 183 0.00
Data protection 39 0.00

5.2.2 Nach Kategorien im Zeitverlauf

Show the code
n_action_type_per_month <-
  n_action_type |>
  select(nr, idvisit, category) |>
  ungroup() |>
  left_join(time_visit_wday |> ungroup()) |>
  select(-c(dow, hour, nr)) |>
  drop_na() |>
  mutate(month_start = floor_date(date_time, "month")) |>
  count(month_start, category)
Show the code
n_action_type_per_month

5.2.3 Nur die Top3-Kategorien im Zeitverlauf

5.2.3.1 idvisit

5.2.3.1.1 Absolutzahlen
Show the code
time_visit_wday |>
  head(30)
Show the code
n_action_type_per_month_top3 <-
  n_action_type |>
  select(nr, idvisit, category) |>
  ungroup() |>
  filter(category %in% c("video", "click_slideChange", "visit_page")) |>
  left_join(time_visit_wday |> ungroup()) |>
  select(-c(dow, hour, nr)) |>
  drop_na() |>
  mutate(month_start = floor_date(date_time, "month")) |>
  count(month_start, category)
Show the code
n_action_type_per_month_top3
Show the code
n_action_type_per_month_top3 |>
  ggplot(aes(x = month_start, y = n, color = category, group = category)) +

  # --- Highlight March–July (approx 1 Mar to 31 Jul) ---
  annotate(
    "rect",
    xmin = as.Date("2023-03-01"),
    xmax = as.Date("2023-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +

  annotate(
    "rect",
    xmin = as.Date("2024-03-01"),
    xmax = as.Date("2024-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +
  annotate(
    "rect",
    xmin = as.Date("2025-03-01"),
    xmax = as.Date("2025-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +

  # --- Highlight October–February (semester break or 2nd term) ---
  annotate(
    "rect",
    xmin = as.Date("2023-10-01"),
    xmax = as.Date("2024-02-28"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "orange"
  ) +
  # annotate("rect",
  #          xmin = as.Date("2024-10-01"), xmax = as.Date("2024-02-28"),
  #          ymin = -Inf, ymax = Inf, alpha = 0.2, fill = "orange") +
  annotate(
    "rect",
    xmin = as.Date("2024-10-01"),
    xmax = as.Date("2025-02-28"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "orange"
  ) +
  geom_line()

5.2.3.1.2 Relative Anteile
Show the code
n_action_type_per_month_top3 |>
  ggplot(aes(
    x = month_start,
    y = n,
    fill = category # Changed from 'color' to 'fill'
  )) +
  # Use geom_area() and set position="fill" to stack and normalize to 100%
  geom_area(position = "fill") +

  # Optional: Customize the y-axis to show percentages
  scale_y_continuous(labels = scales::label_percent()) +

  labs(
    y = "Proportion",
    fill = "Category"
  )

5.2.3.2 fingerprint unique

5.2.3.2.1 Absolutzahlen
Show the code
time_visit_wday_fingerprint |>
  head(30)
Show the code
n_action_type_per_month_top3_fingerprint <-
  n_action_type |>
  select(nr, fingerprint, category) |>
  distinct(fingerprint, .keep_all = TRUE) |>
  ungroup() |>
  filter(category %in% c("video", "click_slideChange", "visit_page")) |>
  left_join(time_visit_wday_fingerprint |> ungroup()) |>
  select(-c(dow, hour, nr)) |>
  drop_na() |>
  mutate(month_start = lubridate::floor_date(date_time, "month")) |>
  count(month_start, category)
Show the code
n_action_type_per_month_top3_fingerprint
Show the code
n_action_type_per_month_top3_fingerprint |>
  ggplot(aes(x = month_start, y = n, color = category, group = category)) +
  # --- Highlight March–July (approx 1 Mar to 31 Jul) ---
  annotate(
    "rect",
    xmin = as.Date("2023-03-01"),
    xmax = as.Date("2023-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +

  annotate(
    "rect",
    xmin = as.Date("2024-03-01"),
    xmax = as.Date("2024-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +
  annotate(
    "rect",
    xmin = as.Date("2025-03-01"),
    xmax = as.Date("2025-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +

  # --- Highlight October–February (semester break or 2nd term) ---
  annotate(
    "rect",
    xmin = as.Date("2023-10-01"),
    xmax = as.Date("2024-02-28"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "orange"
  ) +
  # annotate("rect",
  #          xmin = as.Date("2024-10-01"), xmax = as.Date("2024-02-28"),
  #          ymin = -Inf, ymax = Inf, alpha = 0.2, fill = "orange") +
  annotate(
    "rect",
    xmin = as.Date("2024-10-01"),
    xmax = as.Date("2025-02-28"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "orange"
  ) + 

  geom_line()

5.2.3.2.2 Relative Anteile
Show the code
n_action_type_per_month_top3_fingerprint |>
  ggplot(aes(
    x = month_start,
    y = n,
    fill = category # Changed from 'color' to 'fill'
  )) +
  # Use geom_area() and set position="fill" to stack and normalize to 100%
  geom_area(position = "fill") +

  # Optional: Customize the y-axis to show percentages
  scale_y_continuous(labels = scales::label_percent()) +

  labs(
    y = "Proportion",
    fill = "Category"
  )

5.2.4 Top3 - Pro Kurs

5.2.4.1 Alle Kurse

Show the code
n_action_type_course_uni <-
  n_action_type |>
  left_join(course_and_uni_per_visit |> mutate(idvisit = as.integer(idvisit)))
Show the code
n_action_type_per_month_top3_per_course <-
  n_action_type_course_uni |>
  filter(category %in% c("video", "click_slideChange", "visit_page")) |>
  drop_na() |>
  mutate(month_start = floor_date(actiondetails_0_timestamp, "month")) |>
  count(course, month_start, category)
Show the code
n_action_type_per_month_top3_per_course |>
  ggplot(aes(x = month_start, y = n, color = category, group = category)) +
  facet_wrap(~course, ncol = 3, scales = "free_y") +
  # --- Highlight March–July (approx 1 Mar to 31 Jul) ---
  annotate(
    "rect",
    xmin = as.Date("2023-03-01"),
    xmax = as.Date("2023-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +

  annotate(
    "rect",
    xmin = as.Date("2024-03-01"),
    xmax = as.Date("2024-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +
  annotate(
    "rect",
    xmin = as.Date("2025-03-01"),
    xmax = as.Date("2025-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +

  # --- Highlight October–February (semester break or 2nd term) ---
  annotate(
    "rect",
    xmin = as.Date("2023-10-01"),
    xmax = as.Date("2024-02-28"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "orange"
  ) +
  # annotate("rect",
  #          xmin = as.Date("2024-10-01"), xmax = as.Date("2024-02-28"),
  #          ymin = -Inf, ymax = Inf, alpha = 0.2, fill = "orange") +
  annotate(
    "rect",
    xmin = as.Date("2024-10-01"),
    xmax = as.Date("2025-02-28"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "orange"
  ) + 
  geom_line() +
  theme(legend.position = "bottom") +
  scale_x_date(date_labels = "%b %Y")

5.2.4.2 CTA1 und GESOA

Show the code
n_action_type_per_month_top3_per_course |> 
  filter(course %in% c("cta1", "gesoa")) |> 
  ggplot(aes(x = month_start, y = n, color = category, group = category)) +
  # --- Highlight March–July (approx 1 Mar to 31 Jul) ---
  annotate(
    "rect",
    xmin = as.Date("2023-03-01"),
    xmax = as.Date("2023-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +

  annotate(
    "rect",
    xmin = as.Date("2024-03-01"),
    xmax = as.Date("2024-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +
  annotate(
    "rect",
    xmin = as.Date("2025-03-01"),
    xmax = as.Date("2025-07-31"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "skyblue"
  ) +

  # --- Highlight October–February (semester break or 2nd term) ---
  annotate(
    "rect",
    xmin = as.Date("2023-10-01"),
    xmax = as.Date("2024-02-28"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "orange"
  ) +
  # annotate("rect",
  #          xmin = as.Date("2024-10-01"), xmax = as.Date("2024-02-28"),
  #          ymin = -Inf, ymax = Inf, alpha = 0.2, fill = "orange") +
  annotate(
    "rect",
    xmin = as.Date("2024-10-01"),
    xmax = as.Date("2025-02-28"),
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.2,
    fill = "orange"
  ) + 
  facet_wrap(~course, ncol = 3, scales = "free_y") +
  geom_line() +
  theme(legend.position = "bottom") +
  scale_x_date(date_labels = "%b %Y")

Show the code
n_action_type_per_month_top3_per_course |> 
  filter(course %in% c("cta1", "gesoa")) |> 
  ggplot(aes(x = month_start, y = n, fill = category, group = category)) +
  facet_wrap(~course, ncol = 3, scales = "free_y") +
  geom_area(position = "fill") +
  theme(legend.position = "bottom") +
  scale_x_date(date_labels = "%b %Y") +
  scale_y_continuous(labels = scales::label_percent())

5.2.5 eventcategory

5.2.5.1 Insgesamt

Für folgende Analyse wurde eine andere Variable als oben herangezogen, nämlich eventcategory. Dadurch resultieren etwas andere Ergebnisse - ausführlichere.

Show the code
data_eventcategory <-
  data_separated_filtered |>
  filter(type == "eventcategory")
Show the code
data_separated_filtered_count <-
  data_eventcategory |> 
count(value, sort = TRUE) |>
  mutate(prop = n / sum(n))

data_separated_filtered_count
Show the code
data_separated_filtered_count |>
  ggtexttable()

Show the code
#data_separated_filtered_count |>
#  writexl::write_xlsx(path = "obj/data_separated_filtered_count.xlsx")

5.2.5.2 Pro Universität

Sortiert nach Häufigkeiten pro Hochschule

Show the code
data_separated_filtered_date_uni_course_top5_uni <-
  data_separated_filtered_date_uni_course |>
  compute_prop_eventcategory_per_uni_course()

data_separated_filtered_date_uni_course_top5_uni |>
  kable(digits = 2)
university value n total_n prop
evhn videoplayer_click 696 798 0.87
evhn click_slideChange 29 798 0.04
evhn click_videocard 18 798 0.02
evhn select_transcript_text_for_llm_context 12 798 0.02
evhn click_button 8 798 0.01
hnu videoplayer_click 182 358 0.51
hnu verify_option_wrong 48 358 0.13
hnu click_button 31 358 0.09
hnu static_questionaire 14 358 0.04
hnu static_questionaire_difficulty 14 358 0.04
hs-ansbach videoplayer_click 27 59 0.46
hs-ansbach click_button 16 59 0.27
hs-ansbach click_channelcard_search_course_acronym 3 59 0.05
hs-ansbach click_transcript_word 3 59 0.05
hs-ansbach click_videocard 3 59 0.05
hswt videoplayer_click 89318 99330 0.90
hswt click_button 3403 99330 0.03
hswt click_slideChange 3018 99330 0.03
hswt click_videocard 904 99330 0.01
hswt login 685 99330 0.01
iqw videoplayer_click 8 21 0.38
iqw click_button 4 21 0.19
iqw click_channelcard 2 21 0.10
iqw in_media_search 2 21 0.10
iqw in_media_search_results 2 21 0.10
th-nuernberg videoplayer_click 270771 376939 0.72
th-nuernberg clear_transcript_text_for_llm_context 60995 376939 0.16
th-nuernberg click_slideChange 23573 376939 0.06
th-nuernberg click_button 4655 376939 0.01
th-nuernberg click_videocard 3038 376939 0.01
th-owl videoplayer_click 3 4 0.75
th-owl click_mediaitem 1 4 0.25
thi videoplayer_click 75079 77182 0.97
thi click_button 632 77182 0.01
thi click_videocard 495 77182 0.01
thi click_transcript_word 365 77182 0.00
thi login 228 77182 0.00
Show the code
data_separated_filtered_date_uni_course_top5_uni |>
  # --- Visualization ---
  ggplot(aes(
    x = reorder(value, prop), # Reorder bars by proportion within each facet
    y = prop,
    fill = value # Use 'value' for color
  )) +

  # Bar chart
  geom_col() +

  # Separate plot for each university
  facet_wrap(~university, scales = "free_y", ncol = 1) +

  # Flip coordinates for better readability of long category names
  coord_flip() +

  # Clean up labels and aesthetics
  scale_y_continuous(labels = label_percent()) +
  labs(
    title = "Top 5 'eventcategory' Values by University",
    subtitle = "Proportion of total events within each university",
    x = "Event Category (Value)",
    y = "Proportion (%)",
    fill = "Category"
  ) +
  # Use a minimal theme
  theme_minimal() +
  # Remove the legend since the categories are on the y-axis
  theme(legend.position = "none")

Show the code
data_separated_filtered_date_uni_course_top5_uni |>
  ungroup() |>
  mutate(value = fct_lump(value, 5)) |>
  ggplot(aes(x = university, y = n, fill = value)) +
  geom_col(position = "fill") +

  # Ensure the Y-axis range is correct
  scale_y_continuous(
    labels = scales::label_percent()
  ) +

  labs(
    title = "Top 5 Event Category Proportions by University",
    y = "Proportion of Total Events (%)",
    x = "University",
    fill = "Event Category"
  ) +
  theme_minimal()

5.2.5.3 Pro Kurs

Show the code
data_separated_filtered_date_uni_course_top5_course <-
  data_separated_filtered_date_uni_course |>
  compute_prop_eventcategory_per_uni_course(group_var = course)
Show the code
data_separated_filtered_date_uni_course_top5_course |>
  # --- Visualization ---
  mutate(value = fct_lump(value, 5)) |>
  ggplot(aes(
    x = course, # Reorder bars by proportion within each facet
    y = prop,
    fill = value # Use 'value' for color
  )) +

  # Bar chart
  geom_col(position = "fill") +
  coord_flip()

5.2.5.4 Jahr

Show the code
data_separated_filtered_date_uni_course_top5_date <-
  data_separated_filtered_date_uni_course |>
  compute_prop_eventcategory_per_uni_course(group_var = month)
Show the code
data_separated_filtered_date_uni_course_top5_date |>
  # --- Visualization ---
  mutate(value = fct_lump(value, 5)) |>
  ggplot(aes(
    x = month, # Reorder bars by proportion within each facet
    y = prop,
    fill = value # Use 'value' for color
  )) +

  # Bar chart
  geom_col(position = "fill")

Show the code
data_separated_filtered_date_uni_course_top5_date |>

  mutate(value = fct_lump(value, 5)) |>
  # 2. Visualization
  ggplot(aes(
    x = month,
    y = prop, # Plot the calculated proportion
    fill = value # Use 'value' for the fill (stacking segments)
  )) +

  # Use geom_area() with position = "fill" to ensure the areas stack to 100%
  geom_area(position = "fill") +

  # Customize the Y-axis to show percentages
  scale_y_continuous(labels = scales::label_percent()) +

  # Customize the X-axis for dates
  scale_x_datetime(
    breaks = pretty_breaks(n = 6), # Request roughly 6 "nice" breaks
    date_labels = "%b %Y" # Label format: e.g., "Mar 2024"
  ) +
  # Labels and Theme
  labs(
    title = "Relative Frequency of Top Event Categories Over Time",
    subtitle = "Showing the monthly composition of the top 5 events",
    x = "Month",
    y = "Proportion of Total Events (%)",
    fill = "Event Category"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

5.2.6 User-Typen nach Aktivitäten

Was ist die Hauptaktivität pro User? - Verteilung

5.2.6.1 idvisit

Show the code
n_action_type_distro <-
  n_action_type |>
  group_by(idvisit) |>
  summarise(category_max = max(category, na.rm = TRUE)) |>
  count(category_max)

n_action_type_distro
Show the code
n_action_type_distro |>
  ggplot(aes(x = n, y = category_max)) +
  geom_col()

5.2.6.2 fingerprint

Show the code
n_action_type_distro_fingerpr <-
  n_action_type |>
  group_by(fingerprint) |>
  summarise(category_max = max(category, na.rm = TRUE)) |>
  count(category_max)

n_action_type_distro
Show the code
n_action_type_distro_fingerpr |>
  ggplot(aes(x = n, y = category_max)) +
  geom_col()

5.3 Verteilung der Nutzer-Aktionen

Show the code
n_action_type_counted <-
  n_action_type |>
  count(category, sort = TRUE)

5.3.1 Insgesamt - Rohwerte

Show the code
n_action_type_counted |>
  ggplot(aes(y = reorder(category, n), x = n)) +
  geom_col() +
  geom_bar_text() +
  labs(
    x = "User-Aktion",
    y = "Aktion",
    title = "Anzahl der User-Aktionen nach Kategorie"
  ) +
  theme_minimal() +
  scale_x_continuous(labels = scales::comma)

5.3.2 Insgesamt - Log-Skalierung

Show the code
n_action_type_counted |>
  ggplot(aes(y = reorder(category, n), x = n)) +
  geom_col() +
  geom_bar_text() +
  labs(
    x = "Anzahl der User-Aktionen",
    y = "Aktion",
    title = "Anzahl der User-Aktionen nach Kategorie",
    caption = "Log10-Skala"
  ) +
  theme_minimal() +
  scale_x_log10()

5.3.3 Pro Kurs - Rohwerte

Show the code
n_action_type_course_uni_counted <-
  n_action_type_course_uni |>
  group_by(course) |>
  count(category, sort = TRUE) |>
  drop_na()
Show the code
n_action_type_course_uni_counted |>
  ggplot() +
  aes(y = category, x = log(n, base = 10)) +
  geom_col() +
  facet_wrap(~course)