assess_quality: Assess quality of a dataset

In eHDPrep: Quality Control and Semantic Enrichment of Datasets

Assess quality of a dataset

Description

Provides information on the quality of a dataset. Assesses dataset's completeness, internal consistency, and entropy.

Usage

assess_quality(data, id_var, consis_tbl, plot = TRUE)

Arguments

data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
id_var	An unquoted expression which corresponds to a variable (column) in data which identifies each row (sample).
consis_tbl	Optional consistency table.
plot	Logical. Should completeness plots be printed? Defaults to TRUE. Plots are only displayed when a graphics device is active.

Details

Wraps several quality assessment functions from eHDPrep and returns a nested list with the following structure:

completeness

- A list of completeness assessments:

1. Tibble of variable (column) completeness (via variable_completeness)

2. Tibble of row (sample) completeness (via row_completeness)

3. Plot of row and variable completeness (via plot_completeness)

4. Completeness heatmap (via completeness_heatmap)

5. A function which creates a clean canvas before plotting the completeness heatmap.

internal_inconsistency

- Tibble of internal inconsistencies, if any are present and if a consistency table is supplied (via identify_inconsistency).

vars_with_zero_entropy

- Names of variables (columns) with zero entropy (via zero_entropy_variables)

Value

Nested list of quality measurements

Consistency Table Requirements

Table must have exactly five character columns. The columns should be ordered according to the list below which describes the values of each column:

1. First column name of data values that will be subject to consistency checking. String. Required.

2. Second column name of data values that will be subject to consistency checking. String. Required for a two-variable check; NA for a single-variable check (see below).

3. Logical test to compare columns one and two. One of: ">",">=", "<","<=","==", "!=". String. Optional if columns 4 and 5 have non-NA values.

4. Either a single character string or a colon-separated range of numbers which should only appear in column A. Optional if column 3 has a non-NA value.

5. Either a single character string or a colon-separated range of numbers which should only appear in column B given the value/range specified in column 4. Optional if column 3 has a non-NA value.

Each row should detail one test to make. Therefore, either column 3 or columns 4 and 5 must contain non-NA values.

Single-variable checks. A rule may also constrain one variable against fixed values, rather than comparing two variables. Such a rule has NA in column 2, the boundary in column 4, and NA in column 5. Two forms are available:

• Comparison against a value: column 3 holds the logical test and column 4 the value to compare against. For example "age", NA, ">=", "70", NA requires every value of age to be at least 70.

• Permitted values: column 3 is NA and column 4 holds either a colon-separated numeric range or a single category. For example "age", NA, NA, "0:120", NA requires every value of age to lie between 0 and 120, and "sex", NA, NA, "Male", NA requires every value of sex to be "Male".

Unlike two-variable rules, single-variable rules may use columns 3 and 4 together. Missing (NA) values are never reported as inconsistent by a single-variable check, as they cannot be assessed against a boundary.

See Also

Other high level functionality: apply_quality_ctrl(), impute_missing_values(), review_quality_ctrl(), semantic_enrichment()

Examples

# general example
data(example_data)
res <- assess_quality(example_data, patient_id)

# example of internal consistency checks on more simple dataset
# describing bean counts
require(tibble)

# creating `data`:
beans <- tibble::tibble(red_beans = 1:15,
blue_beans = 1:15,
total_beans = 1:15*2,
red_bean_summary = c(rep("few_beans",9), rep("many_beans",6)))

# creating `consis_tbl`
bean_rules <- tibble::tribble(~varA, ~varB, ~lgl_test, ~varA_boundaries, ~varB_boundaries,
"red_beans", "blue_beans", "==", NA, NA,
"red_beans", "total_beans", "<=", NA,NA,
"red_beans", "red_bean_summary", NA, "1:9", "few_beans",
"red_beans", "red_bean_summary", NA, "10:15", "many_beans")

# add some inconsistencies
beans[1, "red_bean_summary"] <- "many_beans"
beans[1, "red_beans"] <- 10

res <- assess_quality(beans, consis_tbl = bean_rules)

# variable completeness table
res$completeness$variable_completeness

# row completeness table
res$completeness$row_completeness

# show completeness of rows and variables as a bar plot
res$completeness$completeness_plot

# show dataset completeness in a clustered heatmap
res$completeness$plot_completeness_heatmap(res$completeness)

# show any internal inconsistencies
res$internal_inconsistency

# show any variables with zero entropy
res$vars_with_zero_entropy

eHDPrep documentation built on Oct. 21, 2026, 5:10 p.m.