identify_outlier: Identify metric outliers over a date interval

In wpa: Tools for Analysing and Visualising Viva Insights Data

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 = "Date", 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 Date (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_all(), hrvar_count(), hrvar_trend(), identify_churn(), identify_holidayweeks(), identify_inactiveweeks(), identify_nkw(), identify_privacythreshold(), identify_query(), identify_shifts_wp(), identify_shifts(), identify_tenure(), remove_outliers(), standardise_pq(), subject_validate_report(), subject_validate(), track_HR_change(), validation_report()

Examples

identify_outlier(sq_data, metric = "Collaboration_hours")

wpa documentation built on Aug. 21, 2023, 5:11 p.m.