cut_sessions: Cut outliers based on minimum and maximum limits of...

In evprof: Electric Vehicle Charging Sessions Profiling and Modelling

Cut outliers based on minimum and maximum limits of ConnectionHours and ConnectionStartDateTime variables

Description

Cut outliers based on minimum and maximum limits of ConnectionHours and ConnectionStartDateTime variables

Usage

cut_sessions(
  sessions,
  connection_hours_min = NA,
  connection_hours_max = NA,
  connection_start_min = NA,
  connection_start_max = NA,
  log = FALSE,
  start = getOption("evprof.start.hour")
)

Arguments

sessions	tibble, sessions data set in evprof standard format.
connection_hours_min	numeric, minimum of connection hours (duration). If NA the minimum value is considered.
connection_hours_max	numeric, maximum of connection hours (duration). If NA the maximum value is considered.
connection_start_min	numeric, minimum hour of connection start (hour as numeric). If NA the minimum value is considered.
connection_start_max	numeric, maximum hour of connection start (hour as numeric). If NA the maximum value is considered.
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

session dataframe

Examples

library(dplyr)
# Localize the outlying sessions above a certain threshold
california_ev_sessions %>%
  sample_frac(0.05) %>%
  plot_points(start = 3)

# For example sessions that start before 5 AM or that are
# longer than 20 hours are considered outliers
sessions_clean <- california_ev_sessions %>%
  sample_frac(0.05) %>%
  cut_sessions(
    start = 3,
    connection_hours_max = 20,
    connection_start_min = 5
  )
plot_points(sessions_clean, start = 3)

evprof documentation built on May 29, 2024, 2:36 a.m.