| identify_outlier {wpa} | R Documentation |
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 |
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(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_privacythreshold(),
identify_query(),
identify_shifts(),
identify_shifts_wp(),
identify_tenure(),
remove_outliers(),
standardise_pq(),
subject_validate(),
subject_validate_report(),
track_HR_change(),
validation_report()
Examples
identify_outlier(sq_data, metric = "Collaboration_hours")