Specify mappings that can make dataframes compatible with an iface specification

When a function uses ivalidate() internally to check a dataframe conforms to the input it can attempt to rescue an incorrectly formatted dataframe. This is a pretty advanced idea and is not generally recommended.

Usage

imapper(...)

Arguments

...

a set of dplyr::mutate() specifications that when applied to a dataframe will rename or otherwise fix missing columns

Value

a set of mappings

Details

This function is expected to be used only in the context of a .imap = imapper(...) parameter to an ivalidate() call to make sure that certain columns are present or are a set value. Anything provided here will overwrite existing dataframe columns and its use is likely to make function behaviour obtuse. It may be deprecated in the future. The ... input expressions should almost certainly check for the values already existing before overwriting them.

If you are considering using this for replacing missing values check using the default(...) iface type definition instead.

Examples

x = function(df = iface(col1 = integer ~ "an integer column" ), ...) {
  df = ivalidate(df,...)
}
input=tibble::tibble(col2 = c(1,2,3)) 
# This fails because col1 is missing
try(x(input))
#> Error : missing columns in the `df` parameter of `x(...)`.
#> missing: col1
#> consider renaming / creating missing columns before calling `x(...)`
#> 
# This fixes it for this input
x(input, .imap=imapper(col1 = col2))