identify_tenure {wpa}

Tenure calculation based on different input dates, returns data summary table or histogram

Description

This function calculates employee tenure based on different input dates. identify_tenure uses the latest Date available if user selects "Date", but also have flexibility to select a specific date, e.g. "1/1/2020".

Usage

identify_tenure(
  data,
  end_date = "Date",
  beg_date = "HireDate",
  maxten = 40,
  return = "message"
)

Arguments

data

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

end_date

A string specifying the name of the date variable representing the latest date. Defaults to "Date".

beg_date

A string specifying the name of the date variable representing the hire date. Defaults to "HireDate".

maxten

A numeric value representing the maximum tenure. If the tenure exceeds this threshold, it would be accounted for in the flag message.

return

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

• "message"

• "text"

• "plot"

• "data_cleaned"

• "data_dirty"

• "data"

See Value for more information.

Value

A different output is returned depending on the value passed to the return argument:

• "message": message on console with a diagnostic message.

• "text": string containing a diagnostic message.

• "plot": 'ggplot' object. A line plot showing tenure.

• "data_cleaned": data frame filtered only by rows with tenure values lying within the threshold.

• "data_dirty": data frame filtered only by rows with tenure values lying outside the threshold.

• "data": data frame with the PersonId and a calculated variable called TenureYear is returned.

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_outlier(), identify_privacythreshold(), identify_query(), identify_shifts(), identify_shifts_wp(), remove_outliers(), standardise_pq(), subject_validate(), subject_validate_report(), track_HR_change(), validation_report()

Examples

library(dplyr)
# Add HireDate to sq_data
sq_data2 <-
  sq_data %>%
  mutate(HireDate = as.Date("1/1/2015", format = "%m/%d/%Y"))

identify_tenure(sq_data2)