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

Source: R/identify_shifts.R
identify_shifts.Rd

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.

Usage

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_tenure(), track_HR_change(), validation_report()

Examples

# Demo with `pq_data` example where Outlook Start and End times are imputed
# Use a small sample for faster runtime
pq_data_small <- dplyr::slice_sample(pq_data, prop = 0.1)

pq_data_small$WorkingStartTimeSetInOutlook <- "6:30"
pq_data_small$WorkingEndTimeSetInOutlook <- "23:30"

# Return plot
pq_data_small %>% identify_shifts()


# Return summary table
pq_data_small %>% identify_shifts(return = "table")
#> # A tibble: 1 × 3
#>   Shifts     WeekCount PersonCount
#>   <chr>          <int>       <int>
#> 1 6:30-23:30       690         279