2.1 Setup

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library(tidyverse)
library(lubridate)
library(gt)
library(visdat)
# library(data.table)
# library(collapse)
library(targets)
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source("_common.r")
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tar_load(c(n_action_searches_interactions, data_prepped, time_spent))
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n_actions_searches_interactions <-
  data_prepped |>
  select(
    idvisit,
    fingerprint,
    any_of(c(
      "searches",
      "actions",
      "interactions",
      "referrertype",
      "referrername",
      "language",
      "devicetype",
      "devicemodel",
      "operatingsystem",
      "browsername"
    ))
  )

2.2 Unique IDs, Fingerprints, Mean searches, Mean actions

Auswertung - der Anzahlen der uniquen visitids und uniquen Fingerprints - Mittelwerte der Anzahl der Suchen und Aktionen pro Besuch

2.2.1 idivisit und fingerprint jeweils unique

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n_actions_searches_interactions |>
  as.data.frame() |>
  summarise(
    idvisit_n = length(unique(idvisit)),
    fingerprint_n = length(unique(fingerprint)),
    actions_mean = mean(as.integer(actions), na.rm = TRUE),
    searches_mean = mean(as.integer(searches), na.rm = TRUE)
  )
Hinweis

Es gibt etwa doppelt so viele Besucher wie unique Nutzer.

2.3 Referrer Type pro Visit

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n_actions_searches_interactions |>
  count(referrertype, sort = TRUE)

2.4 Referrer Type Name pro Visit

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n_actions_searches_interactions |>
  count(referrername, sort = TRUE)

2.5 devicemodel

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n_actions_searches_interactions |>
  count(devicemodel, sort = TRUE) |>
  slice_head(n = 10)

2.6 operatingsystem

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n_actions_searches_interactions |>
  count(operatingsystem, sort = TRUE) |>
  slice_head(n = 10)

2.7 browsername

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n_actions_searches_interactions |>
  count(browsername, sort = TRUE) |>
  slice_head(n = 10)

Die Mac-User scheinen besonders aktiv zu sein auf HaNS.