identify_shifts: Identify shifts based on outlook time settings for work day...

In vivainsights: Analyze and Visualize Data from 'Microsoft Viva Insights'

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

Description

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")

vivainsights documentation built on April 3, 2025, 9:25 p.m.