Stacks the historical monthly LFS files (`"LFS_HIST"`, 1976 to 2005) and the current LFS files (`"LFS"`, 2006 onward) into one lazy table with a curated common set of variables, so that long time series can be pulled with a single query.
Arguments
- lang
`"eng"` (default) or `"fra"` for the labels.
- sources
The series to include, by default both.
- refresh
`FALSE` (default) opens what is already loaded. `"auto"` first calls `get_pumf(<source>, refresh = "auto")` for each series in `sources`, which loads every available version not yet in its database (for example a newly released LFS month), then opens the timeline. When everything is loaded this only checks the list of available versions. The first call loads all of LFS_HIST (360 monthly files from Borealis) and all LFS years from StatCan, which takes hours. If a version fails to load, the warning says so and the timeline opens with what is there.
- cache_path
Root cache directory. Defaults to `getOption("canpumf.cache_path", tempdir())`.
Value
A lazy `dplyr::tbl()` over the view `lfs_timeline`. Categorical columns are factors. [label_pumf_columns()] and [pumf_var_labels()] work on it. Release it with [close_pumf()].
Details
The two series keep their own DuckDB files. This function attaches both **read-only** to an in-memory DuckDB and returns a view over them, so it never blocks (and is never blocked by) other readers. By default it reads only what is already loaded. Load data first with, for example, `get_pumf("LFS_HIST", "1995")` or `get_pumf("LFS", "2015")`, or pass `refresh = "auto"` to bring both series up to date before opening the timeline.
The harmonised table has these columns: * `SOURCE` (`"LFS_HIST"` or `"LFS"`), `SURVYEAR` and `SURVMNTH`. * Numeric variables in plain units. The hours and wage variables of the current LFS files carry implied decimals (tenths of hours, cents), which are removed here. `FINALWT` is `FWEIGHT` in LFS_HIST. * Categorical variables whose codes are the same in both series (e.g. `PROV`, `AGE_12`, `COWMAIN`, `EFAMTYPE`). These carry the current LFS labels. * Recoded variables: - `LFSSTAT`: the three unemployed categories of LFS_HIST are collapsed. - `GENDER_SEX`: LFS_HIST `SEX` and the current `SEX`/`GENDER`, on the `GENDER` scale, as in [add_lfs_GENDER_SEX()]. - `MARSTAT`: four categories (married or common-law, single, widowed, separated or divorced). The files before November 1999 only have these four. - `CMA`: Montreal, Toronto, Vancouver or other. It is `NA` before 1987, when LFS_HIST does not identify CMAs. - `SCHOOLN`: non-student, full-time or part-time student. - `AGYOWNK`: the four age groups of the youngest child in the current files. - `NAICS_18`: industry in the 18 groups of LFS_HIST. The 21 current groups are merged. - `EDUC`: the 1990 onward classification (LFS_HIST `EDUC90`). It is `NA` before 1990, whose categories do not map onto it. - `WHYPT`: reasons for part-time work from 1997 (`WHYPTNEW`).
Occupation, immigration and the LFS_HIST-only family and spouse variables are not part of the harmonised table. Use [get_pumf()] on each series for those.
Rebasing: LFS_HIST weights are rebased to different Censuses by period (1987-1995 to 2001, 1996-2000 to 2006, 2001-2005 to 2011; 1976-1986 are not rebased). Levels can therefore jump at the seams between periods and at 2006.
Examples
# \donttest{
tl <- get_lfs_timeline()
#> LFS timeline: LFS_HIST 1976-01..2005-12 (360 versions); LFS 2006..2026-08 (28 versions).
if (!is.null(tl)) {
tl |>
dplyr::filter(SURVMNTH == 6L) |>
dplyr::group_by(SURVYEAR, LFSSTAT) |>
dplyr::summarise(persons = sum(FINALWT, na.rm = TRUE), .groups = "drop") |>
dplyr::collect()
close_pumf(tl)
}
# }