Get record counts by time period

Aggregates records in an OMOP CDM table by time period (year, quarter, or month) and returns disclosure-safe counts. The date column is auto-detected from the table schema if not specified. This is useful for understanding temporal trends in data coverage and identifying gaps or spikes in data collection.

Usage

ds.omop.date.counts(
  table,
  date_col = NULL,
  granularity = "year",
  cohort_table = NULL,
  window = NULL,
  scope = c("per_site", "pooled"),
  pooling_policy = "strict",
  symbol = "omop",
  conns = NULL,
  execute = TRUE
)

Arguments

table

Character; the CDM table name (e.g., "condition_occurrence", "drug_exposure").

date_col

Character; the date column to aggregate by, or NULL for automatic detection based on the table's standard date column (default: NULL).

granularity

Character; the time granularity for aggregation: "year" (default), "quarter", or "month".

cohort_table

Character; name of a server-side cohort temp table for filtering, or NULL (default: NULL).

window

List with start/end date strings for temporal filtering, or NULL (default: NULL).

scope

Character; "per_site" (default) or "pooled".

pooling_policy

Character; "strict" (default) or "pooled_only_ok".

symbol

Character; the session symbol (default: "omop").

conns

DSI connection object(s) or NULL to use the session default.

execute

Logical; if FALSE, return a dry-run result containing only the generated call code (default: TRUE).

Value

A dsomop_result object with $per_site (named list of data frames with columns period and count_value), $pooled (combined counts when pooled), and $meta.

Examples

if (FALSE) { # \dontrun{
monthly <- ds.omop.date.counts("condition_occurrence",
                                granularity = "month",
                                scope = "pooled")
monthly$pooled
} # }