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 weighted computations on vectors, matrices and data frames. Fast (ordered) and programmable grouping, factor generation, manipulation of data frames and data object conversions. (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 / standardizing, centering (i.e. between and within transformations), higher-dimensional centering (i.e. multiple fixed effects transformations), linear prediction and partialling-out. (4) Advanced time-computations: Fast (sequences of) lags / leads, and (lagged / leaded, iterated, quasi-, log-) 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 generalized 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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1.2.1 by Sebastian Krantz, a month ago

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Browse source code at

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  

Task views: Econometrics, Official Statistics & Survey Methodology, Time Series Analysis

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