This function scans a standard query output for weeks where collaboration hours is far outside the mean for any individual person in the dataset. Returns a list of weeks that appear to be inactive weeks and optionally an edited dataframe with outliers removed.

As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset.

identify_inactiveweeks(data, sd = 2, return = "text")

## Arguments

data

A Standard Person Query dataset in the form of a data frame.

sd

The standard deviation below the mean for collaboration hours that should define an outlier week. Enter a positive number. Default is 1 standard deviation.

return

String specifying what to return. This must be one of the following strings:

• "text"

• "data_cleaned"

• "data_dirty"

See Value for more information.

## Value

Returns an error message by default, where 'text' is returned. When 'data_cleaned' is passed, a dataset with outlier weeks removed is returned as a dataframe. When 'data_dirty' is passed, a dataset with outlier weeks is returned as a dataframe.

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_nkw(), identify_outlier(), 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()