| identify_nkw {wpa} | R Documentation |
Identify Non-Knowledge workers in a Person Query using Collaboration Hours
Description
This function scans a standard query output to identify employees with consistently low collaboration signals. Returns the % of non-knowledge workers identified by Organization, and optionally an edited data frame with non-knowledge workers removed, or the full data frame with the kw/nkw flag added.
Usage
identify_nkw(data, collab_threshold = 5, return = "data_summary")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
collab_threshold |
Positive numeric value representing the collaboration
hours threshold that should be exceeded as an average for the entire
analysis period for the employee to be categorized as a knowledge worker
("kw"). Default is set to 5 collaboration hours. Any versions after v1.4.3,
this uses a "greater than or equal to" logic ( |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"text": string. Returns a diagnostic message. -
"data_with_flag": data frame. Original input data with an additional column containing thekw/nkwflag. -
"data_clean": data frame. Data frame with non-knowledge workers excluded. -
"data_summary": data frame. A summary table by organization listing the number and % of non-knowledge workers.
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_outlier(),
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()