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Compute stem vital status for specified individuals

compute_stem_vital_status.Rd

Derives a per-census vital status ("alive", "dead", "presumed_dead", or NA) for each specified stem, applying the same evidence hierarchy and retroactive correction used in the bulk workflow_stem_status.R. Intended for incremental updates after new measurements or observations are added, and as a backend for the feature wizard Shiny app.

Usage

compute_stem_vital_status(
  individual_ids,
  add_data = FALSE,
  dry_run = TRUE,
  dead_patterns = c("\\bdead\\b", "\\bmort\\b"),
  missing_patterns = c("\\bmissing\\b", "\\bnon\\s*vu\\b", "\\bdisparu\\b",
    "\\bmanquant\\b", "suppos[eé]\\s*mort"),
  alive_patterns = c("\\balive\\b", "\\bvivant\\b"),
  con = NULL
)

Arguments

individual_ids

Integer vector of individual IDs (id_n / id_data_individuals).

add_data

Logical. If TRUE, upsert the computed statuses into the database: existing stem_status records for these individuals are deleted, then the new statuses are inserted. Default FALSE.

dry_run

Logical. When add_data = TRUE, preview the upsert without committing any changes. Default TRUE.

dead_patterns

Character vector of case-insensitive regexes identifying "dead" observation text.

missing_patterns

Character vector of case-insensitive regexes identifying "missing" observation text.

alive_patterns

Character vector of case-insensitive regexes identifying "alive" observation text.

con

Database connection. Defaults to call.mydb().

Value

A tibble with one row per individual \(\times\) census:

id_n

Individual ID

id_table_liste_plots

Plot ID

id_sub_plots

Census subplot ID (used for DB linking)

plot_name

Plot name

census_name

Census label, e.g. "census_1"

census_date

Date of census

stem_vital_status

"alive", "dead", "presumed_dead", or NA (before first measurement)

missing

Logical — stem was reported missing at this census

evidence_source

Human-readable summary of evidence used

Returned invisibly when add_data = TRUE and dry_run = FALSE.

Evidence sources (in priority order)

  1. observations trait: regex patterns on free-text notes (dead_patterns, missing_patterns, alive_patterns)

  2. flag2_rainfor: non-"k" = dead; "k" = presumed_dead / missing

  3. stem_diameter: \(\ge 2\) consecutive missing censuses = dead; 1 consecutive missing census = presumed_dead

  4. flag1_rainfor: presence is an alive indicator (informational; does not override stronger dead evidence)

Retroactive correction

If a stem is classified as "presumed_dead" at census N but has a recorded diameter at a later census N+k, that earlier status is corrected to "alive" (the stem was just temporarily unmeasured).

Examples

if (FALSE) { # \dontrun{
con <- call.mydb()

# Compute status for a few stems (read-only, no DB write)
result <- compute_stem_vital_status(individual_ids = c(101, 102, 103), con = con)
print(result)

# Compute and preview what would be written to the DB
compute_stem_vital_status(
  individual_ids = c(101, 102, 103),
  add_data = TRUE,
  dry_run  = TRUE,
  con      = con
)

# Compute and commit to the DB
compute_stem_vital_status(
  individual_ids = c(101, 102, 103),
  add_data = TRUE,
  dry_run  = FALSE,
  con      = con
)
} # }