A Pipeline Toolkit for Reproducible Computation at Scale

A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website < https://ropensci.github.io/drake/> and the online manual < https://ropenscilabs.github.io/drake-manual/>.


Reference manual

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5.4.0 by William Michael Landau, 2 months ago


Report a bug at https://github.com/ropensci/drake/issues

Browse source code at https://github.com/cran/drake

Authors: William Michael Landau [aut, cre], Alex Axthelm [ctb], Jasper Clarkberg [ctb], Kirill Müller [ctb], Ben Marwick [rev], Peter Slaughter [rev], Eli Lilly and Company [cph]

Documentation:   PDF Manual  

Task views: High-Performance and Parallel Computing with R

GPL-3 license

Imports codetools, evaluate, digest, dplyr, formatR, fs, igraph, magrittr, parallel, pkgconfig, purrr, R6, R.utils, rlang, stats, storr, stringi, tibble, tidyselect, utils, withr

Suggests abind, bindr, callr, clustermq, crayon, DBI, downloader, future, future.apply, grDevices, ggplot2, ggraph, knitr, lubridate, MASS, methods, networkD3, RSQLite, rprojroot, testthat, txtq, rmarkdown, styler, visNetwork, webshot

See at CRAN