Import Individual Data with Transaction Support

Imports individual tree data into the database using transactions for safety. Inserts into data_individuals table and optionally data_ind_measures_feat for traits. Supports dry-run mode for preview without committing.

Usage

import_individual_data(
  individuals_data,
  features_data = NULL,
  validation = NULL,
  method = NULL,
  con = NULL,
  dry_run = FALSE,
  progress = TRUE,
  ask_confirmation = TRUE
)

Arguments

individuals_data

Data frame containing individual data (required)

features_data

Data frame containing trait/feature data (optional)

validation

Validation result from validate_individual_data() (optional but recommended)

method

Method type (e.g., "1ha-IRD")

con

Database connection (optional, will create if NULL)

dry_run

Logical: If TRUE, preview changes without committing (default FALSE)

progress

Logical: If TRUE, show progress messages (default TRUE)

ask_confirmation

Logical: If TRUE, ask user to confirm before importing (default TRUE)

Value

List with import results:

success

Logical: TRUE if import succeeded

n_individuals

Number of individuals imported

n_features

Number of feature records imported

plot_names

Unique plots affected

username

Username who performed import

dry_run

Was this a dry-run?

message

Summary message

Pre-requisites

1. **Taxonomy must be standardized** - Use `match_taxonomic_names()` or Shiny app 2. **Plots must exist** - Import plots first if needed 3. **Column mapping** - Use `map_individual_columns()` 4. **Validation passed** - Use `validate_individual_data()`

Database Tables

- **data_individuals**: Stores core individual data - **data_ind_measures_feat**: Stores trait measurements

Examples

if (FALSE) { # \dontrun{
# Complete workflow
# 1. Map columns
mapped <- map_individual_columns(individuals, features)

# 2. Validate
validation <- validate_individual_data(
  individuals_data = mapped$individuals,
  features_data = mapped$features,
  method = "1ha-IRD"
)

if (!validation$valid) {
  stop("Fix validation errors first!")
}

# 3. Dry run
preview <- import_individual_data(
  individuals_data = validation$cleaned_data$individuals,
  features_data = validation$cleaned_data$features,
  validation = validation,
  dry_run = TRUE
)

# 4. Actual import
result <- import_individual_data(
  individuals_data = validation$cleaned_data$individuals,
  features_data = validation$cleaned_data$features,
  validation = validation,
  dry_run = FALSE
)
} # }