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Extract actor time range information from dyadic data

Source: R/get_actor_time_info.R
get_actor_time_info.Rd

get_actor_time_info analyzes a longitudinal dyadic dataset to determine when each actor enters and exits the network. Entry is defined as the first time period in which an actor appears in any interaction (as either sender or receiver), and exit as the last time period.

Usage

get_actor_time_info(dyad_data, actor1, actor2, time)

Arguments

dyad_data

A data.frame containing longitudinal dyadic observations. Must include columns for two actors and time periods. Will be coerced to data.frame if a tibble or data.table is provided.

actor1

Character string specifying the column name for the first actor in each dyad.

actor2

Character string specifying the column name for the second actor in each dyad.

time

Character string specifying the column name for time periods.

Value

A data.frame with three columns containing actor-level time information:

  • actor: Character vector of unique actor identifiers found in either actor1 or actor2 columns

  • min_time: The earliest time period in which each actor appears in the data (entry point)

  • max_time: The latest time period in which each actor appears in the data (exit point)

Actors are ordered as they appear in the aggregation, not alphabetically or by time.

Details

The function performs the following operations:

Data processing:

  1. Combines actor1 and actor2 columns into a single nodal format

  2. Aggregates by actor to find minimum and maximum time periods

  3. Returns a clean data.frame with one row per unique actor

Use cases:

Main usage in this package is to:

  • Preparing actor existence information for the actor_pds parameter in netify()

Assumptions:

  • An actor is considered "present" in any time period where they appear in the data, regardless of their role (sender/receiver)

  • Missing values in time periods are ignored when calculating min/max

  • Actors must appear in at least one non-missing time period

Note

The function assumes that presence in the data indicates network participation. If actors can be temporarily absent from the network while still being considered members, this function will not capture such gaps.

Author

Shahryar Minhas, Ha Eun Choi