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branch master updated: gnu: Add r-datasaurus.
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guix-commits |
Subject: |
branch master updated: gnu: Add r-datasaurus. |
Date: |
Fri, 06 Aug 2021 04:00:03 -0400 |
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lbraun pushed a commit to branch master
in repository guix.
The following commit(s) were added to refs/heads/master by this push:
new 196f171 gnu: Add r-datasaurus.
196f171 is described below
commit 196f171c556acb1c94d719b261287eaf23e5116c
Author: Lars-Dominik Braun <lars@6xq.net>
AuthorDate: Fri Aug 6 09:59:27 2021 +0200
gnu: Add r-datasaurus.
* gnu/packages/statistics.scm (r-datasaurus): New variable.
---
gnu/packages/statistics.scm | 33 +++++++++++++++++++++++++++++++++
1 file changed, 33 insertions(+)
diff --git a/gnu/packages/statistics.scm b/gnu/packages/statistics.scm
index 7b50d28..5c744f6 100644
--- a/gnu/packages/statistics.scm
+++ b/gnu/packages/statistics.scm
@@ -13,6 +13,7 @@
;;; Copyright © 2018 Alex Branham <alex.branham@gmail.com>
;;; Copyright © 2020 Tim Howes <timhowes@lavabit.com>
;;; Copyright © 2021 Bonface Munyoki Kilyungi <me@bonfacemunyoki.com>
+;;; Copyright © 2021 Lars-Dominik Braun <lars@6xq.net>
;;;
;;; This file is part of GNU Guix.
;;;
@@ -6310,3 +6311,35 @@ the machinery described in the paper \"Learning
interactions via hierarchical
group-lasso regularization\" (JCGS 2015, Volume 24, Issue 3).
Michael Lim & Trevor Hastie (2015)")
(license license:gpl2)))
+
+(define-public r-datasaurus
+ (package
+ (name "r-datasaurus")
+ (version "0.1.4")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (cran-uri "datasauRus" version))
+ (sha256
+ (base32
+ "1w1yhwwrmh95bklacz44wjwynxd8cj3z8b9zvsnzmk18m5a4k0fl"))))
+ (properties `((upstream-name . "datasauRus")))
+ (build-system r-build-system)
+ (native-inputs `(("r-knitr" ,r-knitr)))
+ (home-page
+ "https://github.com/lockedata/datasauRus")
+ (synopsis "Datasets from the Datasaurus Dozen")
+ (description
+ "The Datasaurus Dozen is a set of datasets with the same summary
+statistics. They retain the same summary statistics despite having radically
+different distributions. The datasets represent a larger and quirkier object
+lesson that is typically taught via Anscombe's Quartet (available in the
+'datasets' package). Anscombe's Quartet contains four very different
+distributions with the same summary statistics and as such highlights the value
+of visualisation in understanding data, over and above summary statistics. As
+well as being an engaging variant on the Quartet, the data is generated in a
+novel way. The simulated annealing process used to derive datasets from the
+original Datasaurus is detailed in \"Same Stats, Different Graphs: Generating
+Datasets with Varied Appearance and Identical Statistics through Simulated
+Annealing\" @url{doi:10.1145/3025453.3025912}.")
+ (license license:expat)))
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