plot_bivarGMM {evprof}

Plot Bivariate Gaussian Mixture Models

Description

Plot Bivariate Gaussian Mixture Models

Usage

plot_bivarGMM(
  sessions,
  models,
  profiles_names = seq(1, nrow(models)),
  points_size = 0.25,
  lines_size = 1,
  legend_nrow = 2,
  log = FALSE,
  start = getOption("evprof.start.hour")
)

Arguments

sessions

tibble, sessions data set in evprof standard format.

models

tibble, parameters of the clusters' GMM models obtained with function cluster_sessions (object models of the returned list)

profiles_names

names of profiles

points_size

size of scatter points in the plot

lines_size

size of lines in the plot

legend_nrow

number of rows in legend

log

logical, whether to transform ConnectionStartDateTime and ConnectionHours variables to natural logarithmic scale (base = exp(1)).

start

integer, start hour in the x axis of the plot.

Value

ggplot2 plot

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)
plot_points(sessions_day, start = 3)

# 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
)