4.1 Setup

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source("_common.r")
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tar_load(c(
  data_separated_filtered,
  time_duration,
  time_spent,
  time_spent_w_course_university,
  time_spent_fingerprint
))

4.2 Berechnungsgrundlage der Verweildauer

Die Verweildauer wurde berechnet als Differenz zwischen kleinstem und größtem Datumszeitwert (POSixct) eines Visits (also pro Wert der Variablen idvisit), vgl. Funktion diff_time. Diese Variable heißt time_diff im Objekt time_spent.

Dabei wird das Objekt data_separated_filtered herangezogen, vgl. die Definition es Targets “time_spent” in der Targets-Pipeline.

4.3 Vorverarbeitung

Die Visit-Zeit wurde auf 600 Min. trunkiert/begrenzt.

4.3.1 idvisit

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time_spent |>
  head(30)
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time_spent <-
  time_spent |>
  # compute time (t) in minutes (min):
  mutate(t_minutes = as.numeric(time_diff, units = "mins")) |>
  filter(t_minutes < 600)

4.3.2 fingerprint

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time_spent_fingerprint |>
  head(30)
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time_spent_fingerprint <-
  time_spent_fingerprint |>
  # compute time (t) in minutes (min):
  mutate(t_minutes = as.numeric(time_diff, units = "mins")) |>
  filter(t_minutes < 600)

4.3.3 fingerprint unqiue

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time_spent_fingerprint |>
  head(30)
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time_spent_fingerprint_unique <-
  time_spent_fingerprint |>
  distinct(fingerprint, .keep_all = TRUE) |>
  # compute time (t) in minutes (min):
  mutate(t_minutes = as.numeric(time_diff, units = "mins")) |>
  filter(t_minutes < 600)

4.4 Verweildauer-Statistiken in Sekunden

Die Verweildauer ist im Folgenden dargestellt auf Grundlage oben dargestellter Berechnungsgrundlage (in Sekunden).

4.4.1 idvisit

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time_spent |>
  summarise(
    mean_time_diff = round(mean(time_diff), 2),
    sd_time_diff = sd(time_diff),
    min_time_diff = min(time_diff), # shortest duration
    max_time_diff = max(time_diff) # longest
  )

4.4.2 fingerprint

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time_spent_fingerprint |>
  summarise(
    mean_time_diff = round(mean(time_diff), 2),
    sd_time_diff = sd(time_diff),
    min_time_diff = min(time_diff), # shortest duration
    max_time_diff = max(time_diff) # longest
  )

4.4.3 fingerprint unqiue

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time_spent_fingerprint_unique |>
  summarise(
    mean_time_diff = round(mean(time_diff), 2),
    sd_time_diff = sd(time_diff),
    min_time_diff = min(time_diff), # shortest duration
    max_time_diff = max(time_diff) # longest
  )

4.5 Verweildauer auf Basis der Variable visitduration

4.5.1 Für alle Daten

Alternativ zur Berechnung der Verweildauer steht eine Variable, visitduration zur Verfügung, die (offenbar) die Dauer des Visits misst bzw. messen soll.

Allerdings resultieren substanziell andere Werte, wenn man diese Variable heranzieht zur Berechnung der Verweildauer, vgl. Target time_duration in der Targets-Pipeline.

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time_duration |>
  head(30)
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time_duration |>
  summarise(duration_sec_avg = mean(visitduration_sec, na.rm = TRUE)) |>
  mutate(duration_min_avg = duration_sec_avg / 60)

4.5.2 Für unique idvisits

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time_duration |>
  distinct(idvisit, .keep_all = TRUE) |>
  summarise(duration_sec_avg = mean(visitduration_sec, na.rm = TRUE)) |>
  mutate(duration_min_avg = duration_sec_avg / 60)

4.5.3 Für unique fingerprints

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time_duration |>
  distinct(fingerprint, .keep_all = TRUE) |>
  summarise(duration_sec_avg = mean(visitduration_sec, na.rm = TRUE)) |>
  mutate(duration_min_avg = duration_sec_avg / 60)

4.6 Verweildauer-Statistiken in Minuten

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time_spent_summary <- 
time_spent |>
  mutate(time_diff_minutes = time_length(time_diff, unit = "minute")) |>
  summarise(
    mean_time_diff = round(mean(time_diff_minutes), 2),
    sd_time_diff = sd(time_diff_minutes),
    min_time_diff = min(time_diff_minutes), # shortest duration
    max_time_diff = max(time_diff_minutes) # longest
  )

time_spent_summary |> 
  gt()
mean_time_diff sd_time_diff min_time_diff max_time_diff
29.86 39.92699 0 476.15
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small_padding_theme <- ggpubr::ttheme(
  tbody.style = tbody_style(size = 8), # Smaller font size can help
  colnames.style = colnames_style(size = 9, face = "bold"),
  padding = unit(c(2, 2), "mm") # Reduce horizontal and vertical padding
)
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ggpubr::ggtexttable(
  time_spent_summary,
  rows = NULL,
  theme = small_padding_theme
)

4.7 Visualisierung der Verweildauer

4.7.1 Binwidth=10 Minutes

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time_spent |>
  mutate(time_diff_minutes = time_diff / 60) |>
  ggplot(aes(x = time_diff_minutes)) + # minutes
  geom_histogram(binwidth = 10) +
  #scale_x_time() +
  theme_minimal() +
  labs(y = "n", x = "Verweildauer in HaNS pro Visit in d:h:m") +
  scale_x_time(breaks = pretty_breaks()) +
  geom_vline(xintercept = median(time_spent$time_diff) / 60, color = "blue", linetype = "dashed") +
  annotate("label", x = median(time_spent$time_diff) / 60, 
           y = Inf, label = "Median", vjust = 1.5, color = "blue")

4.7.2 Bin width= 20 Minutes

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time_spent |>
  mutate(time_diff_minutes = time_diff / 60) |>
  ggplot(aes(x = time_diff_minutes)) + # minutes
  geom_histogram(binwidth = 20) +
  theme_minimal() +
  labs(
    y = "n",
    x = "Verweildauer",
    title = "Verweildauer in HaNS pro Visit in d:h:m"
  ) +
  scale_x_time(breaks = pretty_breaks()) +
  annotate("label", x = median(time_spent$time_diff) / 60, 
           y = Inf, label = "Median", vjust = 1.5, color = "blue")

4.7.3 Zeitdauer begrenzt auf 1-120 Min.

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time_spent2 <-
  time_spent |>
  filter(time_diff > 1, time_diff < 120)

time_spent2 |>
  ggplot(aes(x = time_diff)) +
  geom_histogram(binwidth = 10) +
  theme_minimal() +
  labs(
    y = "n",
    x = "Verweildauer in HaNS pro Visit in Minuten",
    title = "Verweildauer begrenzt auf 1-120 Minuten",
    caption = "bindwidth = 10 Min."
  ) +
  annotate("label", x = median(time_spent$time_diff) / 60, 
           y = Inf, label = "Median", vjust = 1.5, color = "blue")

4.7.4 Veränderung der Verweildauer im Zeitverlauf

4.7.4.1 Monat

Die Einheit von time_spent ist Sekunden.

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time_spent_by_month <-
  time_spent |>
  mutate(month_start = floor_date(time_min, "month")) |>
  mutate(
    month_name = lubridate::month(month_start, label = TRUE, abbr = FALSE),
    month_num = lubridate::month(month_start, label = FALSE),
    year = lubridate::year(month_start)
  ) |>
  group_by(month_num, year) |>
  summarise(
    time_spent_month_avg = mean(time_diff, na.rm = TRUE),
    time_spent_month_sd = sd(time_diff, na.rm = TRUE)
  ) |>
  arrange(year, month_num)

time_spent_by_month
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time_spent_by_month |>
  mutate(
    time_spent_month_avg = round(time_spent_month_avg, 2),
    time_spent_month_sd = round(time_spent_month_sd, 2)
  ) |>
  ggtexttable()

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time_spent_by_month_name <-
  time_spent |>
  mutate(month_start = lubridate::floor_date(time_min, "month")) |>
  mutate(
    month_name = lubridate::month(month_start, label = TRUE, abbr = FALSE),
    month_num = lubridate::month(month_start, label = FALSE),
    year = lubridate::year(month_start)
  ) |>
  group_by(month_start, year) |>
  summarise(
    time_spent_month_avg = mean(time_diff, na.rm = TRUE),
    time_spent_month_sd = sd(time_diff, na.rm = TRUE)
  )

time_spent_by_month_name |>
  ggplot(aes(x = month_start, y = time_spent_month_avg)) +
  geom_line(group = 1, color = "grey60") +
  scale_y_time(labels = scales::time_format("%H:%M:%S")) +
  labs(x = "Datum", y = "Durchschnittliche Verweildauer pro Visit (in h:m:s)") +
  # --- 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_point()

4.7.4.2 Jahr

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time_spent_by_year <-
  time_spent |>
  mutate(month_start = lubridate::floor_date(time_min, "month")) |>
  mutate(
    month_name = lubridate::month(month_start, label = TRUE, abbr = FALSE),
    month_num = lubridate::month(month_start, label = FALSE),
    year = year(month_start)
  ) |>
  group_by(year) |>
  summarise(
    time_spent_avg = mean(time_diff, na.rm = TRUE),
    time_spent_sd = sd(time_diff, na.rm = TRUE)
  )

time_spent_by_year
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time_spent_by_year <-
  time_spent_by_year |>
  mutate(year_date = lubridate::ymd(paste0(year, "-01-01"))) # MAKE Date class

rect_data <- comp_semester_rects(time_spent_by_year, col_date = "year_date")

time_spent_by_year |>
  ggplot(aes(x = year_date, y = time_spent_avg)) +
  scale_x_date(date_labels = "%Y") +
  geom_line(group = 1, color = "grey60") +
  geom_rect(
    data = rect_data |> mutate(xmin = as.Date(xmin), xmax = as.Date(xmax)), # ensure Date
    aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
    fill = "grey",
    alpha = 0.2,
    inherit.aes = FALSE
  ) +
  geom_point() +
  scale_y_time(labels = scales::time_format("%H:%M")) +
  labs(x = "Jahr", y = "Durchschnittliche Verweildauer pro Jahr (h:m)")

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# ...existing code...

4.7.4.3 Woche

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time_spent_by_week_name <-
  time_spent |>
  mutate(week_start = lubridate::floor_date(time_min, "week")) |>
  mutate(week_num = lubridate::week(week_start), year = year(week_start)) |>
  group_by(week_start, year) |>
  summarise(
    time_spent_week_avg = mean(time_diff, na.rm = TRUE),
    time_spent_week_sd = sd(time_diff, na.rm = TRUE)
  )

time_spent_by_week_name |>
  ggplot(aes(x = week_start, y = time_spent_week_avg)) +
  scale_y_time(labels = scales::time_format("%H:%M")) +
  labs(x = "Datum", y = "Durchschnittliche Verweildauer pro Woche (h:m)") +

  # --- 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(group = 1, color = "grey60") +
  geom_point() +
  geom_smooth(method = "loess", se = FALSE, color = "blue")

4.8 Zusammenhang von Lehrveranstaltung und Verweildauer

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time_spent_w_course_university_summary <-
  time_spent_w_course_university |>
  group_by(floor_date_month) |>
  summarise(
    distinct_courses_n = n_distinct(course),
    diff_time_mean = mean(time_diff, na.rm = TRUE),
    n = n()
  )

time_spent_w_course_university_summary
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time_spent_w_course_university_summary |>
  ggplot(aes(x = distinct_courses_n, y = diff_time_mean)) +
  geom_point() +
  scale_y_time(labels = scales::time_format("%M:%S")) +
  labs(
    y = "Average visit duration (m:s)",
    x = "No. of distinct courses per month"
  )

4.9 Zusammenhang von Lehrveranstaltung und Anzahl Visits

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time_spent_w_course_university_summary |>
  ggplot(aes(x = distinct_courses_n, y = n)) +
  geom_point() +
  labs(y = "No. of visits per month", x = "No. of distinct courses per month")