Left-join a norm table (e.g. concreteness, age of acquisition, valence)

The connector for semantic dimensions: the norm data themselves are fetched separately (licensing varies), then merged here so the matcher can equate on them. The join is deterministic and identical across engines.

Usage

merge_norms(lexicon, norms, on = "word", columns = NULL)

Arguments

lexicon

A lexicon data frame.

norms

A data frame or the path to a CSV with a word column and norms.

on

The join column (default "word").

columns

Optional norm columns to keep.

Value

lexicon with the norm columns appended, in the lexicon's own row and column order. Rows with no matching norm get NA.

Details

The result is the lexicon itself with the norm columns appended, and the key is looked up positionally rather than through merge(). That is what makes the two engines agree by construction, with nothing to repair afterwards, because merge() and pandas.merge were measured to diverge in three ways, each of them silent: R hoists the by column to position 1 while pandas keeps the left frame's order, so the column order differed whenever on was not already first; R disambiguates a colliding column name with .x/.y and pandas with _x/_y, and either way a dimension the design matches on disappears under a name nothing looks for; and merge(sort = FALSE) leaves the row order unspecified, so x's order is not carried through. A positional lookup has none of those degrees of freedom: the output is the input plus columns, in both engines. A colliding name is now an error instead.

The key is trimmed and case-folded on both sides. Only the norm table's side was normalised before, so a lexicon holding Dog matched nothing and the design carried on with an all-NA dimension. Because both engines agreed on that wrong answer, no parity test could have caught it. The lexicon's own spelling is preserved rather than folded in place: word is the byte-order tie-break behind every selection, so the join must not rewrite it.