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Approximate String Matching and String Distance Functions
Implements an approximate string matching version of R's native 'match' function. Can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences.
Easy to use interfaces to a number of imputation methods that fit in the not-a-pipe operator of the 'magrittr' package.
Hash R Objects to Integers Fast
Apply 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.
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.
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 sparse version of the algorithm.
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.
Track Changes in Data
A function composition ('pipe') operator and extensible framework that allows for easy logging of changes in data.
Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP.
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.
Parsing, Applying, and Manipulating Data Cleaning Rules
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.