wpa (version 1.4.3)

identify_shifts_wp: Identify shifts based on binary activity

Description

This function uses the Hourly Collaboration query and computes binary activity to identify the 'behavioural' work shift. This is a distinct method to identify_shifts(), which instead uses outlook calendar settings for start and end time of work day to identify work shifts. The two methods can be compared to gauge the accuracy of existing Outlook settings.

Usage

identify_shifts_wp(
  data,
  signals = c("email", "IM"),
  active_threshold = 1,
  start_hour = 9,
  end_hour = 17,
  return = "plot"
)

Arguments

data

A data frame containing data from the Hourly Collaboration query.

signals

Character vector to specify which collaboration metrics to use:

• a combination of signals, such as c("email", "IM") (default)

• "email" for emails only

• "IM" for Teams messages only

• "unscheduled_calls" for Unscheduled Calls only

• "meetings" for Meetings only

active_threshold

A numeric value specifying the minimum number of signals to be greater than in order to qualify as active. Defaults to 0.

start_hour

A character vector specifying start hours, e.g. "0900"

end_hour

A character vector specifying finish hours, e.g. "1700"

return

String specifying what to return. This must be one of the following strings:

• "plot"

• "table"

• "data"

See Value for more information.

Value

A different output is returned depending on the value passed to the return argument:

• "plot": ggplot object. A bar plot for the weekly count of shifts.

• "table": data frame. A summary table for the count of shifts.

• "data: data frame. Input data appended with the following columns:

  • Start

  • End

  • DaySpan

  • Shifts

See Also

Other Data Validation: check_query(), extract_hr(), flag_ch_ratio(), flag_em_ratio(), flag_extreme(), flag_outlooktime(), hr_trend(), hrvar_count_all(), hrvar_count(), identify_churn(), identify_holidayweeks(), identify_inactiveweeks(), identify_nkw(), identify_outlier(), identify_privacythreshold(), identify_query(), identify_shifts(), identify_tenure(), remove_outliers(), standardise_pq(), subject_validate_report(), subject_validate(), track_HR_change(), validation_report()

Other Working Patterns: flex_index(), identify_shifts(), plot_flex_index(), workpatterns_area(), workpatterns_classify_bw(), workpatterns_classify_pav(), workpatterns_classify(), workpatterns_hclust(), workpatterns_rank(), workpatterns_report()

Examples

# NOT RUN {
# Return plot
em_data %>% identify_shifts_wp()

# Return table
em_data %>% identify_shifts_wp(return = "table")

# }