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Identify metric outliers over a date interval

Source: R/identify_outlier.R
identify_outlier.Rd

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")
#> # A tibble: 23 × 3
#>    MetricDate Collaboration_hours  zscore
#>    <date>                   <dbl>   <dbl>
#>  1 2024-04-28                22.8 -0.419 
#>  2 2024-05-05                22.7 -0.806 
#>  3 2024-05-12                23.9  2.22  
#>  4 2024-05-19                23.2  0.422 
#>  5 2024-05-26                23.5  1.14  
#>  6 2024-06-02                23.1  0.169 
#>  7 2024-06-09                23.0 -0.0570
#>  8 2024-06-16                23.5  1.18  
#>  9 2024-06-23                23.0 -0.0483
#> 10 2024-06-30                23.3  0.791 
#> # ℹ 13 more rows