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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: | 1.1.1 |
Depends: | R (≥ 3.6.0) |
Imports: | data.table, lubridate, stringr, Matrix, progress |
Suggests: | testthat (≥ 2.0.0) |
Published: | 2023-07-04 |
DOI: | 10.32614/CRAN.package.dataPreparation |
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 |
Package source: | dataPreparation_1.1.1.tar.gz |
Windows binaries: | r-devel: dataPreparation_1.1.1.zip, r-release: dataPreparation_1.1.1.zip, r-oldrel: dataPreparation_1.1.1.zip |
macOS binaries: | r-release (arm64): dataPreparation_1.1.1.tgz, r-oldrel (arm64): dataPreparation_1.1.1.tgz, r-release (x86_64): dataPreparation_1.1.1.tgz, r-oldrel (x86_64): dataPreparation_1.1.1.tgz |
Old sources: | dataPreparation archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.