mungebits2)

Description Usage Arguments Value Note See Also Examples

A mungebit which affects multiple columns identically and independently can be abstracted into a column transformation. This function allows one to specify what happens to an individual column, and the mungebit will be the resulting column transformation applied to an arbitrary combination of columns.

1	column_transformation(transformation, nonstandard = FALSE)

`transformation`	function. The function's first argument will receive an atomic vector derived from some `data.frame`. If the `transformation` has a `name` argument, it will receive the column name. Any other arguments will be received as the `list(...)` from calling the function produced by `column_transformation`.
`nonstandard`	logical. If `TRUE`, nonstandard evaluation support will be provided for the derived function, so it will be possible to capture the calling expression for each column. By default `FALSE`. Note this will slow the transformation by 0.1ms on each column.

a function which takes a data.frame and a vector of column names (or several other formats, see standard_column_format) and applies the transformation.

The function produced by calling column_transformation will not run independently. It must be used a train or predict function for a mungebit.

multi_column_transformation, standard_column_format

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doubler <- column_transformation(function(x) { 2 * x })
# doubles the Sepal.Length column in the iris dataset
iris2 <- mungebit$new(doubler)$run(iris, c("Sepal.Length"))