Adapt Numerical Records to Fit (in)Equality Restrictions

Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the 'validate' package. The core algorithms have recently been moved to the 'lintools' package, refer to 'lintools' for a more basic interface and access to a version of the algorithm that works with sparse matrices.

Hash R Objects to Integers Fast

Apply an adaptation of the SuperFastHash algorithm to any R object. Hash whole R objects or, for vectors or lists, hash R objects to obtain a set of hash values that is stored in a structure equivalent to the input. See < http://www.azillionmonkeys.com/qed/hash.html> for a description of the hash algorithm.

Data Correction and Imputation Using Deductive Methods

Attempt to repair inconsistencies and missing values in data records by using information from valid values and validation rules restricting the data.

Split-Apply-Combine with Dynamic Groups

Estimate group aggregates, where one can set user-defined conditions that each group of records must satisfy to be suitable for aggregation. If a group of records is not suitable, it is expanded using a collapsing scheme defined by the user.

Synthesize Data Based on Empirical Quantile Functions and Rank Order Matching

Data is synthesized using a combination of inverse transform sampling from the empirical quantile functions for each variable, and then copying the rank order structure from the original dataset. The package also includes a number of functions to measure the utility of synthesized datasets.

Modify Data Using Externally Defined Modification Rules

Data cleaning scripts typically contain a lot of 'if this change that' type of statements. Such statements are typically condensed expert knowledge. With this package, such 'data modifying rules' are taken out of the code and become in stead parameters to the work flow. This allows one to maintain, document, and reason about data modification rules as separate entities.

Deductive Correction, Deductive Imputation, and Deterministic Correction

A collection of methods for automated data cleaning where all actions are logged.

Create Compact Hash Digests of R Objects

Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash', 'murmurhash', 'spookyhash', 'blake3', 'crc32c', 'xxh3_64', and 'xxh3_128' algorithms) permitting easy comparison of R language objects, as well as functions such as'hmac()' to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.

Processing 'Gen5' 2.06 Exported Data

A collection of functions for processing 'Gen5' 2.06 exported data. 'Gen5' is an essential data analysis software for BioTek plate readers < https://www.biotek.com/products/software-robotics-software/gen5-microplate-reader-and-imager-software/>. This package contains functions for data cleaning, modeling and plotting using exported data from 'Gen5' version 2.06. It exports technically correct data defined in (Edwin de Jonge and Mark van der Loo (2013) < https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf>) for customized analysis. It contains Boltzmann fitting for general kinetic analysis. See < https://www.github.com/yanxianUCSB/gen5helper> for more information, documentation and examples.

Locate Errors with Validation Rules

Errors in data can be located and removed using validation rules from package 'validate'. See also Van der Loo and De Jonge (2018) , chapter 7.