value_missing_table {dbGaPCheckup} | R Documentation |
Values Missing Table Awareness Function
Description
This function checks for consistent usage of encoded values and missing value codes between the data dictionary and the data itself.
Usage
value_missing_table(DD.dict, DS.data, non.NA.missing.codes = NA)
Arguments
DD.dict |
Data dictionary. |
DS.data |
Data set. |
non.NA.missing.codes |
A user-defined vector of numerical missing value codes (e.g., -9999). |
Details
For each variable, we have three sets of possible values: the set D of all the unique values observed
in the data, the set V of all the values explicitly encoded in the VALUES columns of the data dictionary, and
the set M of the missing value codes defined by the user via the non.NA.missing.codes
argument.
This function examines various intersections of these three sets, providing awareness
checks to the user about possible issues of concern. While ideally all defined values in set V should
be observed in the data (e.g., in set D), it is not necessarily an error if one does not. This function
checks for:
(A) In Set M and Not in Set D: If the user defines a missing value code that is not present in the data.
(B) In Set V and Not in Set D: If a VALUES entry defines an encoded code value, but that code value is not present in the data.
(C) In Set M and Not in Set V: If the user defines a missing value code that is not defined in a VALUES entry.
(D) M in Set D and Not in Set V: If a defined global missing value code is present in the data for a given variable, but that variable does not have a corresponding VALUES entry.
(E) (Set V values that are not in Set M) that are NOT in Set D = (Set V not in M) not in D: If a VALUES entry is not defined as a missing value code AND is not detected in the data.
Value
A list, returned invisibly,with two components:
"report"Tibble containing: (1) Name (Name of the function) and (2) Information (Details of all potential flagged variables).
"tb"Tibble with detailed information used to construct the Information.
See Also
Examples
data(ExampleB)
value_missing_table(DD.dict.B, DS.data.B, non.NA.missing.codes = c(-9999))
print(value_missing_table(DD.dict.B, DS.data.B, non.NA.missing.codes = c(-9999)))
results <- value_missing_table(DD.dict.B, DS.data.B, non.NA.missing.codes = c(-9999))
results$report$Information$details