зеркало из https://github.com/microsoft/wpa.git
50 строки
2.3 KiB
Plaintext
50 строки
2.3 KiB
Plaintext
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/identify_churn.R
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\name{identify_churn}
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\alias{identify_churn}
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\title{Identify employees who have churned from the dataset}
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\usage{
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identify_churn(data, n1 = 6, n2 = 6, return = "message", flip = FALSE)
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}
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\arguments{
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\item{data}{A Person Query as a data frame. Must contain a \code{PersonId}.}
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\item{n1}{A numeric value specifying the number of weeks at the beginning of the period
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that defines the measured employee set. Defaults to 6.}
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\item{n2}{A numeric value specifying the number of weeks at the end of the period
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to calculate whether employees have churned from the data. Defaults to 6.}
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\item{return}{String specifying what to return. Defaults to "message", with options to
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return a character string ("text") or the \code{PersonId} of employees who have been identified
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as churned ("data").}
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\item{flip}{Logical, defaults to FALSE. This determines whether to reverse the logic of identifying the
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non-overlapping set. If set to \code{TRUE}, this effectively identifies new-joiners, or those
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who were not present in the first n weeks of the data but were present in the final n weeks.}
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}
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\description{
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This function identifies and counts the number of employees who have churned from
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the dataset by measuring whether an employee who is present in the first \code{n} (n1) weeks
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of the data is present in the last \code{n} (n2) weeks of the data.
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}
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\details{
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An additional use case of this function is the ability to identify "new-joiners" by using
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the argument \code{flip}.
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If an employee is present in the first \code{n} weeks of the data but not present in the last
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\code{n} weeks of the data, the function considers the employee as churned. As the measurement period
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is defined by the number of weeks from the start and the end of the passed data frame, you
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may consider filtering the dates accordingly before running this function.
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Another assumption that is in place is that any employee whose \code{PersonId} is not available in the
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data has churned. Note that there may be other reasons why an employee's \code{PersonId} may not
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be present, e.g. maternity/paternity leave, Workplace Analytics license has been removed,
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shift to a low-collaboration role (to the extent that he/she becomes inactive).
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}
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\examples{
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\dontrun{
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sq_data \%>\% identify_churn(n1 = 3, n2 = 3, return = "message")
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}
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}
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