Based on a SAS data step. This allows for row-wise dynamic building of data, iteratively importing slices of existing dataframes, conducting analyses, and exporting to a results frame. This is particularly useful for differential or time-series analyses, which are often not well suited to vector- based operations.

Documentation

Manual: datastepr.pdf
Vignette: Data Stepping

Maintainer: Brandon Taylor <brandon.taylor221 at gmail.com>

Author(s): Brandon Taylor

Install package and any missing dependencies by running this line in your R console:

install.packages("datastepr")

Depends R (>= 3.1.3)
Imports dplyr(>=0.5.0), lazyeval(>=0.1.10), R6(>=2.0.1), magrittr(>=1.5), tibble(>=1.1)
Suggests knitr, covr, rmarkdown, testthat
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Package datastepr
Materials
URL https://github.com/bramtayl/datastepr
Task Views
Version 0.0.2
Published 2016-08-20
License CC0
BugReports https://github.com/bramtayl/datastepr/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks datastepr check results
Package source datastepr_0.0.2.tar.gz