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Tidy a dirty table's column names and category labels before masking

Source: R/clean_table.R
clean_table.Rd

Real custodian tables arrive with column names that are not valid R names ("Yield (t/ha)", "Site Name"), leading or trailing whitespace in names and factor / character values, and the occasional near-duplicate label ("north" vs "North" vs " north"). clean_table() makes the safe fixes - legalising names and trimming whitespace - loudly, and reports the unsafe ones (near-duplicate labels) without touching them, because merging two labels that only look alike is a judgement call masque must not make silently.

Usage

clean_table(df, clean = c("auto", "report", "off"), quiet = FALSE)

Arguments

df

A data frame.

clean

One of "auto" (default - legalise names, trim whitespace, report near-duplicates), "report" (legalise names, report what whitespace / near-duplicate changes would be made but apply none), or "off" (legalise names only, skip all other hygiene). Column-name legalisation is applied in every mode – an invalid name silently rewritten downstream corrupts the clone – and is surfaced as a masque_name_repaired warning; only the whitespace and near-duplicate handling is governed by the mode.

quiet

Logical. When FALSE (default) a cli summary of the fixes and advisories is printed. Set TRUE to suppress it (the report object is returned either way).

Value

An object of class masque_cleaning: a list with

  • data - the cleaned (or, under report / off, unchanged) data frame;

  • name_map - named character original -> clean for every column whose name changed (empty if none);

  • level_fixes - named list, one entry per column whose values were trimmed, each a named character original -> clean;

  • near_duplicates - a data frame of report-only label pairs (col, a, b, kind) that look like typos but were left untouched;

  • mode - the clean mode applied.

Details

The corrections are returned alongside the cleaned data so mask() can record them in the recipe and apply_recipe() can re-apply the identical cleaning to a fresh copy of the original. Cleaning is therefore part of the round-trip contract, not a destructive pre-step.

See also

mask(), propose_roles().

Examples

df <- data.frame(
  `Site Name` = c("north ", "north", "South"),
  `Yield (t/ha)` = c(3.1, 2.9, 5.0),
  check.names = FALSE
)
cl <- clean_table(df, quiet = TRUE)
#> Warning: Renamed 2 column name(s) that are not valid R names: `Site Name` -> `Site.Name`, `Yield (t/ha)` -> `Yield..t.ha.`. The map is recorded in the recipe and reversed on the round-trip.
names(cl$data)
#> [1] "Site.Name"    "Yield..t.ha."
cl$near_duplicates
#> [1] col  a    b    kind
#> <0 rows> (or 0-length row.names)