validate - Data Validation Infrastructure
Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6 and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.
Last updated 18 days ago
data-cleaningvalidation
12.27 score 408 stars 9 dependents 472 scripts 2.0k downloadseditrules - Parsing, Applying, and Manipulating Data Cleaning Rules
Please note: active development has moved to packages 'validate' and 'errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package.
Last updated 6 months ago
6.82 score 21 stars 1 dependents 106 scripts 738 downloadsdcmodify - 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.
Last updated 6 months ago
6.22 score 10 stars 55 scripts 301 downloadserrorlocate - 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) <doi:10.1002/9781118897126>, chapter 7.
Last updated 7 months ago
data-cleaningerrorsinvalidation
6.07 score 22 stars 53 scripts 353 downloadslintools - Manipulation of Linear Systems of (in)Equalities
Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution, projecting a vector on the convex polytope described by a system of (in)equations, simplify systems by removing spurious columns and rows and collapse implied equalities, test if a matrix is totally unimodular, compute variable ranges implied by linear (in)equalities.
Last updated 6 months ago
5.19 score 4 stars 2 dependents 13 scripts 393 downloadsvalidatetools - Checking and Simplifying Validation Rule Sets
Rule sets with validation rules may contain redundancies or contradictions. Functions for finding redundancies and problematic rules are provided, given a set a rules formulated with 'validate'.
Last updated 6 months ago
data-cleaningrulesvalidation
4.42 score 15 stars 35 scripts 178 downloadsvalidatesuggest - Generate Suggestions for Validation Rules
Generate suggestions for validation rules from a reference data set, which can be used as a starting point for domain specific rules to be checked with package 'validate'.
Last updated 1 years ago
data-cleaningvalidation
4.40 score 5 stars 5 scripts 124 downloadsdeducorrect - Deductive Correction, Deductive Imputation, and Deterministic Correction
A collection of methods for automated data cleaning where all actions are logged. NOTE: active development has moved to the 'deductive' package.
Last updated 6 months ago
4.12 score 8 stars 33 scripts 459 downloadsdeductive - 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.
Last updated 6 months ago
data-cleaning
3.81 score 13 stars 9 scripts 305 downloads