Identify shifts based on outlook time settings for work day start and end time

This function uses outlook calendar settings for start and end time of work day to identify work shifts. The relevant variables are WorkingStartTimeSetInOutlook and WorkingEndTimeSetInOutlook.

identify_shifts(data, return = "plot")

Arguments

data

A data frame containing data from the Hourly Collaboration query.

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 Shifts columns.

See also

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

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

Examples

# Return plot
dv_data %>% identify_shifts()


# Return summary table
dv_data %>% identify_shifts(return = "table")
#> # A tibble: 2 × 3
#>   Shifts      WeekCount PersonCount
#>   <chr>           <int>       <int>
#> 1 07:00-23:00       832          64
#> 2 06:00-18:00        45           5