Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of data.table efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.
| Version: | 0.4.3 |
| Depends: | R (≥ 3.3.0), lubridate, stringr, Matrix, progress |
| Imports: | data.table |
| Suggests: | knitr, rmarkdown, kableExtra, pander, testthat (≥ 2.0.0) |
| Published: | 2020-02-12 |
| Author: | Emmanuel-Lin Toulemonde [aut, cre] |
| Maintainer: | Emmanuel-Lin Toulemonde <el.toulemonde at protonmail.com> |
| BugReports: | https://github.com/ELToulemonde/dataPreparation/issues |
| License: | GPL-3 | file LICENSE |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | dataPreparation results |
| Reference manual: | dataPreparation.pdf |
| Vignettes: |
Tutorial: discovering dataPreparation functionalities Tutorial: building train and test sets with the same characteristics |
| Package source: | dataPreparation_0.4.3.tar.gz |
| Windows binaries: | r-devel: dataPreparation_0.4.3.zip, r-release: dataPreparation_0.4.3.zip, r-oldrel: dataPreparation_0.4.3.zip |
| macOS binaries: | r-release: dataPreparation_0.4.3.tgz, r-oldrel: dataPreparation_0.4.3.tgz |
| Old sources: | dataPreparation archive |
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