identify_outlier: Identify metric outliers over a date interval

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

Identify metric outliers over a date interval

Description

This function takes in a selected metric and uses z-score (number of standard deviations) to identify outliers across time. There are applications in this for identifying weeks with abnormally low collaboration activity, e.g. holidays. Time as a grouping variable can be overridden with the group_var argument.

Usage

identify_outlier(
  data,
  group_var = "MetricDate",
  metric = "Collaboration_hours"
)

Arguments

data	A Standard Person Query dataset in the form of a data frame.
group_var	A string with the name of the grouping variable. Defaults to Date.
metric	Character string containing the name of the metric, e.g. "Collaboration_hours"

Value

Returns a data frame with MetricDate (if grouping variable is not set), the metric, and the corresponding z-score.

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_privacythreshold(), identify_shifts(), identify_tenure(), track_HR_change(), validation_report()

Examples

identify_outlier(pq_data, metric = "Collaboration_hours")

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