prepare: Prepare Event Log Data for Network Estimation

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Prepare Event Log Data for Network Estimation

Description

Converts event log data (actor, action, time) into wide sequence format suitable for build_network. Automatically parses timestamps, detects sessions from time gaps, and handles tie-breaking.

Usage

prepare(
  data,
  actor,
  action,
  time = NULL,
  order = NULL,
  session = NULL,
  time_threshold = 900,
  custom_format = NULL,
  is_unix_time = FALSE,
  unix_time_unit = c("seconds", "milliseconds", "microseconds")
)

Arguments

data	Data frame with event log columns.
actor	Character or character vector. Column name(s) identifying who performed the action (e.g. "student" or c("student", "group")). If missing, all data is treated as one actor.
action	Character. Column name containing the action/state/code.
time	Character or NULL. Column name containing timestamps. Supports ISO8601, Unix timestamps (numeric), and 40+ date/time formats. If NULL, row order defines the sequence. Default: NULL.
order	Character or NULL. Column name for tie-breaking when timestamps are identical. If NULL, original row order is used. Default: NULL.
session	Character, character vector, or NULL. Column name(s) for explicit session grouping (e.g. "course" or c("course", "semester")). When combined with time, sessions are further split by time gaps. Default: NULL.
time_threshold	Numeric. Maximum gap in seconds between consecutive events before a new session starts. Only used when time is provided. Default: 900 (15 minutes).
custom_format	Character or NULL. Custom strptime format string for parsing timestamps. Default: NULL (auto-detect).
is_unix_time	Logical. If TRUE, treat numeric time values as Unix timestamps. Default: FALSE (auto-detected for numeric columns).
unix_time_unit	Character. Unit for Unix timestamps: "seconds", "milliseconds", or "microseconds". Default: "seconds".

Value

A list with class "nestimate_data" containing:

sequence_data

Data frame in wide format (one row per session, columns T1, T2, ...).

long_data

The processed long-format data with session IDs.

meta_data

Session-level metadata (session ID, actor).

time_data

Parsed time values in wide format (if time provided).

statistics

List with total_sessions, total_actions, max_sequence_length, unique_actors, etc.

See Also

build_network, prepare_onehot

Examples

df <- data.frame(
  student = rep(1:3, each = 5),
  code = sample(c("read", "write", "test"), 15, replace = TRUE),
  timestamp = seq.POSIXt(as.POSIXct("2024-01-01"), by = "min", length.out = 15)
)
prepared <- prepare(df, actor = "student", action = "code",
                    time = "timestamp")
net <- build_network(prepared$sequence_data, method = "relative")

Nestimate documentation built on April 20, 2026, 5:06 p.m.