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Get Labour Force Survey PUMF data from a shared longitudinal DuckDB

Source: R/lfs_pipeline.R
lfs_get_pumf.Rd

Manages a single `LFS.duckdb` file that accumulates all downloaded LFS versions. Each call either retrieves already-loaded data or downloads, parses, labels, and appends a new version.

Usage

lfs_get_pumf(
  version = NULL,
  lang = "eng",
  cache_path = getOption("canpumf.cache_path", tempdir()),
  refresh = FALSE,
  redownload = FALSE,
  read_only = TRUE
)

Arguments

version

LFS version string (`"YYYY"` or `"YYYY-MM"`), or `NULL` to report database state and return the full table.

lang

`"eng"` (default) or `"fra"`.

cache_path

Root cache directory.

refresh

`FALSE` (default), `TRUE` (re-parse and re-label the specified version from the cached raw files), or `"auto"` (download all versions not yet in the database). `refresh = TRUE` requires a non-NULL `version`.

redownload

If `TRUE`, delete the cached zip and extracted content for the specified version and re-download from StatCan before rebuilding. Implies `refresh = TRUE`. Requires a non-NULL `version`.

read_only

Open the DuckDB connection in read-only mode (default `TRUE`). Pass `FALSE` to allow write access to the LFS DuckDB.

Value

A lazy `dplyr::tbl()`, or `invisible(NULL)` when `version = NULL` and no data has been loaded.

Details

**Version types**: - `"YYYY"` (e.g. `"2023"`) — annual file released by StatCan after year-end. - `"YYYY-MM"` (e.g. `"2024-06"`) — monthly file for the current year.

When an annual file for year Y is loaded and monthly files for that year are already in the database, the monthly rows are replaced (supersession). Conversely, if an annual for year Y is already loaded, requesting a monthly for that year returns the annual data filtered to that month without re-downloading.

**Connection note**: the returned `tbl` holds an open DuckDB connection. Loading a second version (i.e. calling `lfs_get_pumf` again while holding the first result) requires the first tbl's connection to be closed first. Use [close_pumf()] or `dplyr::collect()` the result before the next call.