Advanced and Fast Data Transformation

A C/C++ based package for advanced data transformation and statistical computing 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 statistical programming: A full set of fast statistical functions supporting grouped and weighted computations on vectors, matrices and data frames. Fast and programmable grouping, ordering, unique values / rows, factor generation and interactions. Fast and flexible functions for data manipulation 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) replacing and sweeping out of statistics, and (grouped, weighted) scaling / standardizing, between (averaging) and (quasi-)within (centering / demeaning) 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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1.3.2 by Sebastian Krantz, 18 days 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) | file LICENSE license

Imports Rcpp, lfe

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

Linking to Rcpp

System requirements: C++11

See at CRAN