Split a filename column into metadata columns

Survey exports often encode metadata in the filename (e.g. "podracing_wave1_NA_2026.csv"). This splits a filename column on a separator into named metadata columns, dropping the file extension first. Replaces the brittle str_split(file, "_")[[1]][n] indexing this saves you.

Usage

parse_filename(
  data = NULL,
  col = "file",
  into,
  sep = "_",
  drop_ext = TRUE,
  remove = FALSE
)

Arguments

data

A data frame.

col

Name of the column holding the filename (string or unquoted). Defaults to "file".

into

Character vector of new column names, one per field you expect after splitting.

sep

Separator to split on (a fixed string, not a regex). Defaults to "_".

drop_ext

If TRUE (default), strip a trailing file extension before splitting.

remove

If TRUE, drop the original filename column. Defaults to FALSE so you keep the source reference.

Value

The data frame with the new metadata columns added.

Details

Pairs naturally with read_folder(), whose file column carries the source filename: split it once and you have event, wave, locale, year, etc. as real columns to group by. The file extension is stripped first (drop_ext), the split is on a fixed string (not a regex), and rows with fewer fields than into are padded with NA so a stray filename never derails the parse.

See also

read_folder().

Other import: read_folder(), select_prefix(), select_suffix()

Examples

df <- tibble::tibble(file = c("podracing_wave1_NA_2026.csv"))
parse_filename(df, into = c("survey", "wave", "locale", "year"))
#> # A tibble: 1 × 5
#>   file                        survey    wave  locale year 
#>   <chr>                       <chr>     <chr> <chr>  <chr>
#> 1 podracing_wave1_NA_2026.csv podracing wave1 NA     2026