value_missing_table {dbGaPCheckup}R Documentation

Values Missing Table Awareness Function


This function checks for consistent usage of encoded values and missing value codes between the data dictionary and the data itself.


value_missing_table(DD.dict,, = NA)



Data dictionary.

Data set.

A user-defined vector of numerical missing value codes (e.g., -9999).


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 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.


A list, returned invisibly,with two components:

See Also




value_missing_table(DD.dict.B,, = c(-9999))
print(value_missing_table(DD.dict.B,, = c(-9999)))
results <- value_missing_table(DD.dict.B,, = c(-9999))

[Package dbGaPCheckup version 1.1.0 Index]