Advanced and Fast Data Transformation

A C/C++ based package for advanced data transformation in R that is extremely fast, flexible and parsimonious to code with and programmer friendly. It is well integrated with 'dplyr', 'plm' and 'data.table'. --- Key Features: --- (1) Advanced data programming: A full set of fast statistical functions supporting grouped and/or weighted computations on vectors, matrices and data.frames. Fast (ordered) and reusable grouping, quick data conversions, and quick select, replace or add data.frame columns. (2) Advanced aggregation: Fast and easy multi-data-type, multi-function, weighted, parallelized and fully customized data aggregation. (3) Advanced transformations: Fast (grouped, weighted) replacing and sweeping out of statistics, scaling, centering, higher-dimensional centering, complex linear prediction and partialling-out. (4) Advanced time-computations: Fast (sequences of) lags / leads, and (lagged / leaded, iterated) differences and growth rates on (unordered) time-series and panel data. Multivariate auto, partial and cross- correlation functions for panel data. Panel data to (ts-)array conversions. (5) List Processing: (Recursive) list search / identification, extraction / subsetting, data-apply, and row-binding / unlisting in 2D. (6) Advanced data exploration: Fast (grouped, weighted, panel-decomposed) summary statistics for complex multilevel / panel data.


Reference manual

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1.1.0 by Sebastian Krantz, 8 days ago

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Authors: Sebastian Krantz [aut, cre] , Matt Dowle [ctb] , Arun Srinivasan [ctb] , Simen Gaure [ctb] , Dirk Eddelbuettel [ctb] , R Core Team and contributors worldwide [ctb] , Martyn Plummer [cph] , 1999-2016 The R Core Team [cph]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, lfe

Suggests dplyr, plm, data.table, ggplot2, scales, vars, knitr, rmarkdown, testthat, microbenchmark

Linking to Rcpp

System requirements: C++11

See at CRAN