Check values in data frame according to dic attributes

Checks if values in variables are valid according to the 'values' and 'type' dictionary attributes. Invalid values can be replaced with a specified value. An overview of invalid values can be reported.

Usage

check_values(
  data,
  replace = NULL,
  return = TRUE,
  report = TRUE,
  include_missing = FALSE,
  integer_as_numeric = TRUE
)

Arguments

data: A data frame.
replace: Value which replaces invalid values (e.g., NA). If NULL, no replacement is done.
return: If TRUE, a data frame is returned with replaced values. If FALSE, no data frame is returned.
report: If TRUE, an overview of invalid values will be given.
include_missing: If TRUE, missing values (provided as 'missing' in the dic file) will be considered as valid values. If FALSE, missing values will be considered as invalid values.
integer_as_numeric: If TRUE, type 'integer' will be handled as 'numeric'. That is, only values outside the minimum and the maximum of the provided valid values will be considered invalid. If FALSE, only values not included in the provided valid values will be considered invalid.

Value

A data frame with replaced values if replaces is not NULL.

Details

This function only checks variables that have dic attributes. Variables without dic attributes are ignored.

By default, integer variables are treated as float variables. That is, only values outside the provided valid values are considered invalid. If integer_as_float is set to FALSE, only values not included in the provided valid values are considered invalid.

If include_missing is set to TRUE, values provided as 'missing' in the dic file are considered valid values.

Examples

check_values(ex_itrf, return = FALSE)