Meant for use in a data analysis pipeline, this function applies a predicate generating function to each of the columns indicated. It will then use these predicates to check every element of those columns. If any of these predicate applications yield FALSE, this function will raise an error, effectively terminating the pipeline early. If there are no FALSES, this function will just return the data that it was supplied for further use in later parts of the pipeline.
insist(
data,
predicate_generator,
...,
success_fun = success_continue,
error_fun = error_stop,
skip_chain_opts = FALSE,
obligatory = FALSE,
defect_fun = defect_append,
description = NA
)
By default, the data
is returned if dynamically created
predicate assertion is TRUE and and error is thrown if not. If a
non-default success_fun
or error_fun
is used, the
return values of these function will be returned.
A data frame
A function that is applied to each of the column vectors selected. This will produce, for every column, a true predicate function to be applied to every element in the column vectors selected
Comma separated list of unquoted expressions.
Uses dplyr's select
to select
columns from data.
Function to call if assertion passes. Defaults to
returning data
.
Function to call if assertion fails. Defaults to printing a summary of all errors.
If TRUE, success_fun
and error_fun
are used even if assertion is called within a chain.
If TRUE and assertion failed the data is marked as defective.
For defective data, all the following rules are handled by
defect_fun
function.
Function to call when data is defective. Defaults to skipping assertion and storing info about it in special attribute.
Custom description of the rule. Is stored in result reports and data.
For examples of possible choices for the success_fun
and
error_fun
parameters, run help("success_and_error_functions")
assert
verify
insist_rows
assert_rows