con_inadmissible_vocabulary: Detects variable levels not specified in standardized...

In dataquieR: Data Quality in Epidemiological Research

Detects variable levels not specified in standardized vocabulary

Description

For each categorical variable, value lists should be defined in the metadata. This implementation will examine, if all observed levels in the study data are valid.

Indicator

Usage

con_inadmissible_vocabulary(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  threshold_value = 0,
  meta_data = item_level,
  meta_data_v2
)

Arguments

resp_vars	variable list the name of the measurement variables
study_data	data.frame the data frame that contains the measurements
label_col	variable attribute the name of the column in the metadata with labels of variables
item_level	data.frame the data frame that contains metadata attributes of study data
threshold_value	numeric from=0 to=100. a numerical value ranging from 0-100.
meta_data	data.frame old name for item_level
meta_data_v2	character path to workbook like metadata file, see prep_load_workbook_like_file for details. ALL LOADED DATAFRAMES WILL BE PURGED, using prep_purge_data_frame_cache, if you specify meta_data_v2.

Details

Algorithm of this implementation:

• Remove missing codes from the study data (if defined in the metadata)

• Interpretation of variable specific VALUE_LABELS as supplied in the metadata.

• Identification of measurements not corresponding to the expected categories. Therefore two output data frames are generated:

  • on the level of observation to flag each undefined category, and

  • a summary table for each variable.

• Values not corresponding to defined categories are removed in a data frame of modified study data

Value

a list with:

• SummaryData: data frame summarizing inadmissible categories with the columns:

  • Variables: variable name/label

  • OBSERVED_CATEGORIES: the categories observed in the study data

  • DEFINED_CATEGORIES: the categories defined in the metadata

  • NON_MATCHING: the categories observed but not defined

  • NON_MATCHING_N: the number of observations with categories not defined

  • NON_MATCHING_N_PER_CATEGORY: the number of observations for each of the unexpected categories

  • GRADING: indicator TRUE/FALSE if inadmissible categorical values were observed (more than indicated by the threshold_value)

• SummaryTable: data frame for the dataquieR pipeline reporting the number and percentage of inadmissible categorical values

• ModifiedStudyData: study data having inadmissible categories removed

• FlaggedStudyData: study data having cases with inadmissible categories flagged

See Also

Online Documentation

Examples

## Not run: 
sdt <- data.frame(DIAG = c("B050", "B051", "B052", "B999"),
                  MED0 = c("S01XA28", "N07XX18", "ABC", NA), stringsAsFactors = FALSE)
mdt <- tibble::tribble(
~ VAR_NAMES, ~ DATA_TYPE, ~ STANDARDIZED_VOCABULARY_TABLE, ~ SCALE_LEVEL, ~ LABEL,
"DIAG", "string", "<ICD10>", "nominal", "Diagnosis",
"MED0", "string", "<ATC>", "nominal", "Medication"
)
con_inadmissible_vocabulary(NULL, sdt, mdt, label_col = LABEL)
prep_load_workbook_like_file("meta_data_v2")
il <- prep_get_data_frame("item_level")
il$STANDARDIZED_VOCABULARY_TABLE[[11]] <- "<ICD10GM>"
il$DATA_TYPE[[11]] <- DATA_TYPES$INTEGER
il$SCALE_LEVEL[[11]] <- SCALE_LEVELS$NOMINAL
prep_add_data_frames(item_level = il)
r <- dq_report2("study_data", dimensions = "con")
r <- dq_report2("study_data", dimensions = "con",
     advanced_options = list(dataquieR.non_disclosure = TRUE))
r

## End(Not run)

dataquieR documentation built on Jan. 8, 2026, 5:08 p.m.