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authorGlenn Johnson <glenn-johnson@uiowa.edu>2021-01-18 12:53:23 -0600
committerGitHub <noreply@github.com>2021-01-18 12:53:23 -0600
commita693a56459f74f9d51a4328de3867a6ed5b0d8a6 (patch)
treef6dce4d0ab031e029d50169332d47782a66d7976
parent42748f389fcaf02f43957659c0564e62dce8f11f (diff)
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add version 2.1.0 to r-janitor (#21120)
-rw-r--r--var/spack/repos/builtin/packages/r-janitor/package.py34
1 files changed, 21 insertions, 13 deletions
diff --git a/var/spack/repos/builtin/packages/r-janitor/package.py b/var/spack/repos/builtin/packages/r-janitor/package.py
index a64578ccc4..6166db41e0 100644
--- a/var/spack/repos/builtin/packages/r-janitor/package.py
+++ b/var/spack/repos/builtin/packages/r-janitor/package.py
@@ -7,30 +7,38 @@ from spack import *
class RJanitor(RPackage):
- """The main janitor functions can: perfectly format data.frame column
- names; provide quick one- and two-variable tabulations (i.e., frequency
- tables and crosstabs); and isolate duplicate records. Other janitor
- functions nicely format the tabulation results. These
- tabulate-and-report functions approximate popular features of SPSS and
- Microsoft Excel. This package follows the principles of the "tidyverse"
- and works well with the pipe function %>%. janitor was built with
- beginning-to-intermediate R users in mind and is optimized for
- user-friendliness. Advanced R users can already do everything covered
- here, but with janitor they can do it faster and save their thinking
- for the fun stuff."""
+ """Simple Tools for Examining and Cleaning Dirty Data
+
+ The main janitor functions can: perfectly format data.frame column names;
+ provide quick one- and two-variable tabulations (i.e., frequency tables and
+ crosstabs); and isolate duplicate records. Other janitor functions nicely
+ format the tabulation results. These tabulate-and-report functions
+ approximate popular features of SPSS and Microsoft Excel. This package
+ follows the principles of the "tidyverse" and works well with the pipe
+ function %>%. janitor was built with beginning-to-intermediate R users in
+ mind and is optimized for user-friendliness. Advanced R users can already
+ do everything covered here, but with janitor they can do it faster and save
+ their thinking for the fun stuff."""
homepage = "https://github.com/sfirke/janitor"
url = "https://cloud.r-project.org/src/contrib/janitor_0.3.0.tar.gz"
list_url = "https://cloud.r-project.org/src/contrib/Archive/janitor"
+ version('2.1.0', sha256='d60615940fbe174f67799c8abc797f27928eca4ac180418527c5897a4aaad826')
version('1.2.0', sha256='5e15a2292c65c5ddd6160289dec2604b05a813651a2be0d7854ace4548a32b8c')
version('1.1.1', sha256='404b41f56e571fab4c95ef62e79cb4f3bb34d5bb6e4ea737e748ff269536176b')
version('0.3.0', sha256='5e4d8ef895ed9c7b8fa91aeb93e25c009366b4c5faaf3d02265f64b33d4a45f4')
depends_on('r@3.1.2:', type=('build', 'run'))
depends_on('r-dplyr@0.7.0:', type=('build', 'run'))
- depends_on('r-tidyr@0.7.0:', type=('build', 'run'))
+ depends_on('r-dplyr@1.0.0:', when='@2.1.0:', type=('build', 'run'))
+ depends_on('r-lifecycle', when='@2.1.0:', type=('build', 'run'))
+ depends_on('r-lubridate', when='@2.1.0:', type=('build', 'run'))
depends_on('r-magrittr', type=('build', 'run'))
- depends_on('r-snakecase@0.9.2:', when='@1.1.0:', type=('build', 'run'))
depends_on('r-purrr', when='@1.1.0:', type=('build', 'run'))
depends_on('r-rlang', when='@1.1.0:', type=('build', 'run'))
+ depends_on('r-stringi', when='@2.1.0:', type=('build', 'run'))
+ depends_on('r-stringr', when='@2.1.0:', type=('build', 'run'))
+ depends_on('r-snakecase@0.9.2:', when='@1.1.0:', type=('build', 'run'))
+ depends_on('r-tidyselect@1.0.0:', when='@2.1.0:', type=('build', 'run'))
+ depends_on('r-tidyr@0.7.0:', type=('build', 'run'))