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author | Lars-Dominik Braun <lars@6xq.net> | 2021-08-06 09:59:27 +0200 |
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committer | Lars-Dominik Braun <lars@6xq.net> | 2021-08-06 09:59:27 +0200 |
commit | 196f171c556acb1c94d719b261287eaf23e5116c (patch) | |
tree | 38089547360f00ac4004c066d4baa0d55ba1517c /gnu | |
parent | 0d72f24ac084acf9d69e147a692e5d8bcb2ea21b (diff) | |
download | guix-196f171c556acb1c94d719b261287eaf23e5116c.tar guix-196f171c556acb1c94d719b261287eaf23e5116c.tar.gz |
gnu: Add r-datasaurus.
* gnu/packages/statistics.scm (r-datasaurus): New variable.
Diffstat (limited to 'gnu')
-rw-r--r-- | gnu/packages/statistics.scm | 33 |
1 files changed, 33 insertions, 0 deletions
diff --git a/gnu/packages/statistics.scm b/gnu/packages/statistics.scm index 7b50d287f2..5c744f664a 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))) |