gnu: Add r-janitor.
* gnu/packages/cran.scm (r-janitor): New variable.
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@ -28942,6 +28942,46 @@ novels, ready for text analysis. These novels are \"Sense and Sensibility\",
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and \"Persuasion\".")
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and \"Persuasion\".")
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(license license:expat)))
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(license license:expat)))
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(define-public r-janitor
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(package
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(name "r-janitor")
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(version "2.1.0")
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(source (origin
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(method url-fetch)
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(uri (cran-uri "janitor" version))
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(sha256
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(base32
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"09nqm957m2f54y2l30619b58x4i7gxwvr2lwg5kly5xy1ya1a1nn"))))
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(properties `((upstream-name . "janitor")))
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(build-system r-build-system)
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(propagated-inputs
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(list r-dplyr
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r-lifecycle
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r-lubridate
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r-magrittr
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r-purrr
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r-rlang
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r-snakecase
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r-stringi
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r-stringr
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r-tidyr
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r-tidyselect))
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(native-inputs (list r-knitr))
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(home-page "https://github.com/sfirke/janitor")
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(synopsis "Simple tools for examining and cleaning dirty data")
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(description
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"The main janitor functions can: perfectly format @code{data.frame column}
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names; provide quick counts of variable combinations (i.e., frequency tables
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and crosstabs); and isolate duplicate records. Other janitor functions nicely
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format the tabulation results. These tabulate-and-report functions
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approximate popular features of SPSS and Excel. This package follows the
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principles of the \"tidyverse\" and works well with the pipe function
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@code{%>%}. janitor was built with beginning-to-intermediate R users in mind
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and is optimized for user-friendliness. Advanced R users can already do
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everything covered here, but with janitor they can do it faster and save their
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thinking for the fun stuff.")
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(license license:expat)))
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(define-public r-tokenizers
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(define-public r-tokenizers
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(package
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(package
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(name "r-tokenizers")
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(name "r-tokenizers")
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