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Transform Employment Records into Temporal Segments (Core Function)

Source: R/vecshift.R
vecshift.Rd

Processes employment contract records with temporal boundaries to create continuous temporal segments that track employment status over time. This core function handles the event-based transformation logic, identifying unemployment periods (when no contracts are active), single employment periods, and overlapping employment situations.

Usage

vecshift(dt)

Arguments

dt

A data.table containing employment contract records with the following required columns:

  • id: Contract identifier (unique key for each employment contract)

  • cf: Person identifier (e.g., fiscal code)

  • inizio: Contract start date (Date or numeric)

  • fine: Contract end date (Date or numeric)

  • prior: Employment type indicator (0 or negative for part-time, positive for full-time)

Value

A data.table with temporal segments containing:

  • cf: Person identifier

  • inizio: Segment start date

  • fine: Segment end date

  • arco: Number of overlapping contracts (0 = unemployment)

  • prior: Employment type for the segment (0 = part-time, 1 = full-time)

  • id: Contract ID (0 for unemployment periods)

  • durata: Duration of the segment in days

Details

The function implements precise date logic for temporal processing:

  • Employment contracts are inclusive of both start and end dates

  • A person works ON both inizio and fine dates

  • End events are created at fine date

  • Unemployment periods are identified (arco=0) and adjusted: inizio+1 and fine-1

  • This ensures correct temporal boundaries for all segments

The algorithm creates events for each contract start (+1) and end (-1 at fine), then uses cumulative sums to track overlapping contracts. Unemployment segments are identified (arco=0) and their dates are adjusted to represent the actual unemployment period.

Note

This function performs only the core temporal transformation. For employment status classification, use classify_employment_status separately or use process_employment_pipeline for a complete processing pipeline.

See also

classify_employment_status for status classification details process_employment_pipeline for a complete processing pipeline

Examples

if (FALSE) { # \dontrun{
library(data.table)
# Create sample employment data
dt <- data.table(
  id = 1:3,
  cf = c("ABC123", "ABC123", "DEF456"),
  inizio = as.Date(c("2023-01-01", "2023-06-01", "2023-02-01")),
  fine = as.Date(c("2023-05-31", "2023-12-31", "2023-11-30")),
  prior = c(1, 0, 1)  # 1 = full-time, 0 = part-time
)

# Transform to temporal segments (pure transformation)
result <- vecshift(dt)
print(result)

# Apply status classification separately
result_with_status <- classify_employment_status(result)
print(result_with_status)

# Use complete pipeline for integrated processing
result_pipeline <- process_employment_pipeline(dt)
print(result_pipeline)
} # }