mungebits2)

Description Usage Arguments Examples

Mungebits are atomic data transformations of a data.frame that, loosely speaking, aim to modify "one thing" about a variable or collection of variables. This is pretty loosely defined, but examples include dropping variables, mapping values, discretization, etc.

1 2	mungebit_initialize(train_function = base::identity, predict_function = train_function, enforce_train = TRUE, nse = FALSE)

`train_function`	function. This specifies the behavior to perform on the dataset when preparing for model training. A value of NULL specifies that there should be no training step, i.e., the data should remain untouched.
`predict_function`	function. This specifies the behavior to perform on the dataset when preparing for model prediction. A value of NULL specifies that there should be no prediction step, i.e., the data should remain untouched.
`enforce_train`	logical. Whether or not to flip the trained flag during runtime. Set this to FALSE if you are experimenting with or debugging the mungebit.
`nse`	logical. Whether or not we expect to use non-standard evaluation with this mungebit. Non-standard evaluation allows us to obtain the correct R expression when using `substitute` from within the body of a train or predict function for the mungebit. By default, `FALSE`, meaning non-standard evaluation will not be available to the train and predict functions, but this ability can be switched on at a slight speed detriment (2-3x prediction slowdown for the fastest functions, somewhat negligible for slower functions).

mb <- mungebit$new(column_transformation(function(column, scale = NULL) {
  # `trained` is a helper provided by mungebits indicating TRUE or FALSE
  # according as the mungebit has been run on a dataset.
  if (!trained) {
    cat("Column scaled by ", input$scale, "\n")
  } else {
    # `input` is a helper provided by mungebits. We remember the
    # the `scale` so we can re-use it during prediction.
    input$scale <- scale
  }
  column * input$scale
}))

# We make a lightweight wrapper to keep track of our data so
# the mungebit can perform side effects (i.e., modify the data without an
# explicit assignment <- operator).
irisp <- list2env(list(data = iris))
#mb$run(irisp, 'Sepal.Length', 2)

#head(mp$data[[1]] / iris[[1]])
# > [1] 2 2 2 2 2 2
#mb$run(mp, 'Sepal.Length')
# > Column scaled by 2
#head(mp$data[[1]] / iris[[1]])
# > [1] 4 4 4 4 4 4