Skip to contents

Prepare Event Log Data for Network Estimation

Source: R/prepare_data.R
prepare_data.Rd

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(
  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

# \donttest{
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_data(df, actor = "student", action = "code",
                         time = "timestamp")
net <- build_network(prepared$sequence_data, method = "relative")
# }