# Missingness

## Run missingness check

    library(DrugExposureDiagnostics)
    library(dplyr)
    library(DT)

    # acetaminophen concept id is 1125315
    acetaminophen <- 1125315
    cdm <- mockDrugExposure()
    acetaminophen_checks <- executeChecks(
      cdm = cdm,
      ingredients = acetaminophen,
      checks = "missing"
    )

## Overall missingness

This shows the missingness of the drug records summarised on ingredient level.

    datatable(acetaminophen_checks$missingValuesOverall,
      rownames = FALSE
    )

Column | Description
--- | ---
ingredient_concept_id | Concept ID of ingredient.
ingredient | Name of drug ingredient.
variable | the variable for which missingness was assessed.
n_records | Number of records for ingredient concept. If n_records is the same as n_sample this means that there are more records but the number was cut at the pre-specified sample number for efficiency reasons.
n_sample | The pre-specified maximum sample. If n_records is smaller than the sample it means that sampling was ignored because the total number of records was already too small.
n_records_not_missing_value | The number of records for which there is no missingness in the variable of interest.
n_records_missing_value | The number of records with missing values for the variable of interest.
proportion_records_missing_value | The proportion of records with missing values for the variable of interest.
result_obscured | TRUE if count has been suppressed due to being below the minimum cell count, otherwise FALSE.

## Missingness by drug concept

This shows the missingness on drug concept level.

    datatable(acetaminophen_checks$missingValuesByConcept,
      rownames = FALSE
    )

Column | Description
--- | ---
drug_concept_id | ID of the drug concept.
drug | Name of the drug concept.
ingredient_concept_id | Concept ID of ingredient.
ingredient | Name of drug ingredient.
variable | the variable for which missingness was assessed.
n_records | Number of records for drug concept. If n_records is the same as n_sample this means that there are more records but the number was cut at the pre-specified sample number for efficiency reasons.
n_sample | The pre-specified maximum sample. If n_records is smaller than the sample it means that sampling was ignored because the total number of records was already too small.
n_records_not_missing_value | The number of records for which there is no missingness in the variable of interest.
n_records_missing_value | The number of records with missing values for the variable of interest.
proportion_records_missing_value | The proportion of records with missing values for the variable of interest.
result_obscured | TRUE if count has been suppressed due to being below the minimum cell count, otherwise FALSE.