Classify sessions into user profiles — set

Joins all sub-sets from the list, adding a new column Profile

Usage

set_profiles(sessions_clustered = list(), clusters_definition = list())

Arguments

sessions_clustered: list of tibbles with sessions clustered (sessionsobject of the output from function cluser_sessions()) from each sub-set
clusters_definition: list of tibbles with clusters definitions (direct output from function define_clusters()) of each sub-set

Value

tibble

Examples

library(dplyr)

# Select working day sessions (`Timecycle == 1`) that
# disconnect the same day (`Disconnection == 1`)
sessions_day <- california_ev_sessions %>%
  divide_by_timecycle(
    months_cycles = list(1:12), # Not differentiation between months
    wdays_cycles = list(1:5, 6:7) # Differentiation between workdays/weekends
  ) %>%
  divide_by_disconnection(
    division_hour = 10, start = 3
  ) %>%
  filter(
    Disconnection == 1, Timecycle == 1
  ) %>%
  sample_frac(0.05)
#> The considered time-cycles are:
#> 
#> 
#> |Timecycle |months |wdays |
#> |:---------|:------|:-----|
#> |1         |1-12   |1-5   |
#> |2         |1-12   |6-7   |

# Identify two clusters
sessions_clusters <- cluster_sessions(
  sessions_day, k=2, seed = 1234, log = TRUE
)

# Plot the clusters found
plot_bivarGMM(
  sessions = sessions_clusters$sessions,
  models = sessions_clusters$models,
  log = TRUE, start = 3
)


# Define the clusters with user profile interpretations
clusters_definitions <- define_clusters(
  models = sessions_clusters$models,
  interpretations = c(
    "Connections during working hours",
    "Connections during all day (high variability)"
  ),
  profile_names = c("Workers", "Visitors"),
  log = TRUE
)

# Classify each session to the corresponding user profile
sessions_profiles <- set_profiles(
  sessions_clustered = list(sessions_clusters$sessions),
  clusters_definition = list(clusters_definitions)
)