Skip to contents

Prepare Survival Data from a Panel Dataset

Source: R/create_survival_prep.R
create_survival_prep.Rd

Converts a Standard Person Query (panel) dataset into a person-level survival table suitable for direct use with create_survival.

Each person's weekly rows are reduced to a single row containing:

  • time: the number of periods from the first observation until the event condition is first met. If the condition is never met, time equals the total number of observed periods (censored).

  • event: 1 if the condition was met at any point in the observation window, 0 if the person was censored (never met it).

Although rooted in survival analysis, the event_condition is typically a positive milestone in a workforce context — first use of a tool, first week as a power user, first week crossing a collaboration threshold. The resulting curve is therefore often better described as a time-to-adoption, conversion, or graduation curve. See create_survival for further framing guidance.

Usage

create_survival_prep(
  data,
  metric,
  event_condition = function(x) x > 0,
  hrvar = "Organization"
)

Arguments

data

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

metric

Character string containing the name of the metric column to evaluate the event condition on.

event_condition

A function that takes a numeric vector and returns a logical vector. Applied to metric to mark whether the event occurred in a given period. Defaults to function(x) x > 0 (any non-zero value is treated as the event).

hrvar

Character string containing the name of the HR attribute column to carry through into the output. The most recent (last observed) value per person is used. Defaults to "Organization". Set to NULL to omit.

Value

A data frame with one row per person containing:

  • PersonId

  • time: numeric — periods until event, or total observed periods if censored

  • event: integer — 1 (event) or 0 (censored)

  • The hrvar column, if supplied

See also

create_survival

Examples

# \dontrun{
# library(vivainsights)
# data("pq_data", package = "vivainsights")
#
# # Step 1: derive person-level survival data
# surv_data <- create_survival_prep(
#   data = pq_data,
#   metric = "Copilot_actions_taken_in_Teams",
#   event_condition = function(x) x > 0
# )
#
# # Step 2: visualise
# create_survival(
#   data = surv_data,
#   time_col = "time",
#   event_col = "event",
#   hrvar = "Organization"
# )
# }