What are efficient ways to row-wise apply a function `f`

over a DataFrame? Looking through the DataFrames.jl documentation, I have found plenty of examples of column-wise aggregation. However, say that I want to take one or more columns, apply a function to each row, and output an array-like structure containing the results for each row, similar to Julia’s native vectorized dot notation for arrays:

`f.(df[:A]) # something like this`

Thus far, I have been doing as follows:

`f.(vec(convert(Array, df[:A])))`

Is this a reasonable way to go about it?