Learn R Programming

vvauditor

The vvauditor package is an R package designed to validate the integrity of data by providing a comprehensive set of assertion tests. These tests allow users to check for specific conditions or properties within a dataset, ensuring the accuracy and reliability of the data. The package aims to facilitate quality control checks in data analysis workflows and assists in identifying and correcting any errors or inconsistencies in the data.

Installation

To install the vvauditor package, you can use the install.packages() function in R:


install.packages("vvauditor")

Usage

Once the package is installed, you can load it into your R session using the library() function:

library(vvauditor)

Assertion Tests

The vvauditor package provides a range of assertion tests that can be applied to datasets. These tests include checks for missing values, outliers, distributional assumptions, and more. By incorporating these tests into your data analysis workflow, you can ensure the quality and reliability of your data.

For more information on the available assertion tests and how to use them, please refer to the package documentation.

Contributing

If you're interested in contributing to the vvauditor package, you can follow these steps:

Fork the vvauditor repository on GitHub.
Make your desired changes to the code.
Submit a pull request to the main repository.

Please refer to the contribution guidelines in the package repository for more information on how to contribute.

Copy Link

Version

Install

install.packages('vvauditor')

Monthly Downloads

12,601

Version

0.7.0

License

MIT + file LICENSE

Maintainer

Tomer Iwan

Last Published

February 10th, 2025

Functions in vvauditor (0.7.0)

create_numeric_details

Create numeric details csv

create_dataset_summary_table

Create dataset summary statistics table

create_subset_fields

Create subset fields

check_numeric_or_integer_type

Check for Numeric or Integer Type

regex_content_parameter

Construct Regex for Matching Function Parameter Content

check_non_zero_rows

Check for Non-Zero Rows

find_maximum_value

Find the maximum numeric value in a vector, ignoring non-numeric values

md_complete_cases

MD complete cases

find_common_columns

Find Common Columns Between Data Frames

retrieve_function_calls

retrieve_function_calls

get_distribution_statistics

Compute distribution statistics for a numeric vector

remove_duplicates_and_na

Remove Duplicates and NA Values from Input

get_first_element_class

Retrieve the class of the first element of a vector

Get values of column

check_zero_columns

Check for Columns with Only 0s

count_more_than_1

Count more than 1

regex_year_date

Generate regular expression of a year date.

identify_join_pairs

Identify Possible Join Pairs Between Data Frames

Generate regular expression of a time.

create_field_info

Create field info

Find pattern in R scripts

find_minimum_value

Find the minimum numeric value in a vector, ignoring non-numeric values

identify_outliers

Identify Outliers in a Data Frame Column

check_posixct_type

Check for POSIXct Type

run_all_assertions

Run All Data Validation Assertions

str_detect_in_file

Detect string in file

retrieve_functions_and_packages

Retrieve functions and packages

is_unique_column

Check if a column in a dataframe has unique values

retrieve_package_usage

Retrieve packages that are loaded in a script

return_assertions_message

Return Assertion Messages

return_mtcars_testfile

Read and return the mtcars testfile

create_data_types

Create data types tibble

create_categorical_details

Create categorical details csv

retrieve_string_assignments

retrieve_string_assignments

retrieve_sourced_scripts

retrieve_sourced_scripts

assert_type_consistency

Assert Type Consistency Between Data and Metadata

assert_logical_named

Assert Logical Value in Column

assert_field_consistency

Check if the fieldnames of the dataset are the same

assert_field_distinctness

Assert Field Uniqueness Consistency Between Data and Metadata

assert_missing_values

Assert Consistency of Missing Values in Data

assert_date_named

Assert Date Value in Column

assertion_message

Assert Message Based on Type

assert_field_existence

Assert Field Existence in New Data

assert_range_validation

Assert Range Validation for Data Fields

assert_no_duplicates_in_group

Assert No Duplicates in Group

check_duplicates

Check for Duplicate Rows in Selected Columns

check_na_columns

Check for columns with only NA values

calculate_category_percentages

Calculate the percentage of categories in a data vector

drop_na_column_names

Drop NA column names

check_double_columns

check double columns

duplicates_in_column

Duplicates in column

check_no_duplicates_in_group

Check for No Duplicates in Group

check_no_duplicate_rows

Check for No Duplicate Rows