mungebits2)

Description Usage Arguments Value Note See Also Examples

A mungebit which takes a fixed group of columns and produces a new group of columns (or a single new column) can be abstracted into a multi-column transformation. This functions allows one to specify what happens to a fixed list of columns, and the mungebit will be the resulting multi-column transformation applied to an arbitrary combination of columns. An arity-1 multi-column transformation with a single output column equal to its original input column is simply a column_transformation. 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	multi_column_transformation(transformation, nonstandard = FALSE)

`transformation`	function. The function's first argument will receive atomic vectors derived from some `data.frame`. Any other arguments will be received as the `list(...)` from calling the function produced by `multi_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 multi_column_transformation will not run independently. It must be used a train or predict function for a mungebit.

column_transformation, standard_column_format

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divider <- multi_column_transformation(function(x, y) { x / y })
# Determines the ratio of Sepal.Length and Sepal.Width in the iris dataset.
iris2 <- mungebit$new(divider)$run(iris, c("Sepal.Length", "Sepal.Width"), "Sepal.Ratio")