Package 'qualitycontrol'

Amyotrophic lateral sclerosis Example dataset

Description

An Amyotrophic lateral sclerosis related example dataset.

Usage

als_data

Format

A list

• subjidSubject ID

• p1ALSFRS-R 1

• p2ALSFRS-R 2

• p3ALSFRS-R 3

• p4ALSFRS-R 4

• p5ALSFRS-R 5

• p6ALSFRS-R 6

• p7ALSFRS-R 7

• p8ALSFRS-R 8

• p9ALSFRS-R 9

• x1rALSFRS-R R1

• x2rALSFRS-R R2

• x3rALSFRS-R R3

• age_at_baselineAge at baseline

• age_at_onsetAge at onsite

• onsetRegion of onset

• baseline_dateBaseline date3

• death_dateDeath date

---

An example dataset containing a Quality Control mapping

Description

An example dataset containing a Quality Control mapping

Usage

als_data_qc_mapping

Format

A list of 3 tibbles.

• missingTable with all the 'missing' tests.

• inconsistenciesTable with all the 'inconsistencies' tests.

• rangeTable with all the 'out of range' tests.

---

QC dataset using a specific variable mapping

Description

QC dataset using a specific variable mapping

Usage

qc_data(data, qc_mapping, output_file = NULL)

Arguments

data	A data frame, data frame extension (e.g. a tibble) to be quality controlled.
qc_mapping	A list of data frame or data frame extension (e.g. a tibble) specifying the tests. Each data frame row represents a test to the data.
output_file	(optional) File path ended in .xlsx or .xls. If is not null, findings table to be written to this path.

Value

A data frame containing all the findings.

Examples

qc_data(als_data, als_data_qc_mapping)

---

Read Quality Control mapping file

Description

read_qc_mapping reads an .xlsx file that contains the QC mapping.

Usage

read_qc_mapping(path)

Arguments

path

excel file path to be read. Each tab should contain 3 tabs with the names missing, inconsistencies and range. Each tab will correspond to one QC mapping table. QC mapping excel file should contain 3 tabs: missing: columns should be named as "qc_type", "variable" and 'type". inconsistencies: columns should be named as "qc_type", "variable1", "type1", "relation", "variable2" and "type2". range: columns should be named as "qc_type", "variable", "type", "lower_value", "upper_value" and "categories". The columns specified above should contain specific values: qc_type: "missing", "duplicated", "inconsistent_values" and "range" variable, variable1, variable2: variable name that is included in data. type, type1, type2: "numeric", text", "categorical", "date" relation: expected relation between variable1 and variable2 which can be "greater_than", "greater_than_or_equal", "lower_than", "lower_than_or_equal" or "equal". lower_value, upper_value: expected numeric values representing ranges categories: expected variable categories

Value

A list containing all the QC mapping tables

---

Test if variable values are duplicated

Description

Test if variable values are duplicated

Usage

test_duplicated(data, variable)

Arguments

data	data to be tested.
variable	The variable to be tested.

Value

A data frame containing all the findings regarding the applied test.

Examples

test_duplicated(als_data, 'subjid')

---

Test the inconsistencies between variables on a dataset

Description

Test the inconsistencies between variables on a dataset

Usage

test_inconsistencies(data, variable1, variable2, relation)

Arguments

data	data to be tested.
variable1	The variable to be tested.
variable2	The variable to be tested.
relation	String such as 'greater_than', 'greater_than_or_equal' 'lower_than_or_equal' and 'lower_than'.

Value

A data frame containing all the findings regarding the applied test.

Examples

test_inconsistencies(als_data, 'baseline_date', 'death_date', relation = 'lower_than')
test_inconsistencies(als_data, 'age_at_baseline', 'age_at_onset', relation = 'greater_than')

---

Test the variable missingness on a dataset

Description

Test the variable missingness on a dataset

Usage

test_missing(data, variable)

Arguments

data	data to be tested.
variable	The variable to be tested.

Value

A data frame containing all the findings regarding the applied test.

Examples

test_missing(als_data, 'p8')
test_missing(als_data, 'p1')

---

Test the range of a variable on a dataset

Description

Test the range of a variable on a dataset

Usage

test_range(
  data,
  variable,
  type,
  categories = NULL,
  lower_value = NULL,
  upper_value = NULL
)

Arguments

data	data to be tested.
variable	The variable to be tested.
type	String such as 'categorical', 'date' or 'numeric'
categories	Only to be filled if type is 'categorical'. String of categories.
lower_value	Only to be filled if type is 'numeric' or 'date'. Can be numeric or string.
upper_value	Only to be filled if type is 'numeric' or 'date'. Can be numeric or string.

Value

A data frame containing all the findings regarding the applied test.

Examples

test_range(als_data, 'onset', c('bulbar','respiratory', 'spinal'), type = 'categorical')
test_range(als_data, 'age_at_baseline', lower_value = 20, upper_value = 100, 
type = 'numeric')
test_range(als_data, 'age_at_onset', lower_value = 20, upper_value = 100,
type = 'numeric')
test_range(als_data, 'baseline_date', lower_value = '2000-01-01', upper_value = '2022-01-01', 
type = 'date')
test_range(als_data, 'death_date', lower_value = '2000-01-01', upper_value = '2022-01-01',
 type = 'date')

---