gggda: A 'ggplot2' Extension for Geometric Data Analysis
A variety of multivariable data summary statistics and
constructions have been proposed, either to generalize univariable analogs
or to exploit multivariable properties.
Notable among these are the bivariate peelings surveyed by Green
(1981, ISBN:978-0-471-28039-2),
the bag-and-bolster plots proposed by Rousseeuw &al (1999)
<doi:10.1080/00031305.1999.10474494>, and the minimum spanning trees used by
Jolliffe (2002) <doi:10.1007/b98835> to represent high-dimensional
relationships among data in a low-dimensional plot.
Additionally, biplots of singular value–decomposed tabular data, such as
from principal components analysis, make use of vectors, calibrated axes,
and other representations of variable elements to complement point markers
for case elements; see Gabriel (1971) <doi:10.1093/biomet/58.3.453> and
Gower & Harding (1988) <doi:10.1093/biomet/75.3.445> for original proposals.
Because they treat the abscissa and ordinate as commensurate or the data
elements themselves as point masses or unit vectors, these multivariable
tools can be thought of as belonging to geometric data analysis; see Podani
(2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux &
Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. 'gggda' extends
Wickham's (2010) <doi:10.1198/jcgs.2009.07098> layered grammar of graphics
with statistical transformation ("stat") and geometric construction ("geom")
layers for many of these tools, as well as convenience coordinate systems
to emphasize intrinsic geometry of the data.
Version: |
0.1.0 |
Depends: |
R (≥ 3.3.0), ggplot2 |
Imports: |
rlang, tidyr, dplyr, magrittr, scales, labeling, ddalpha |
Suggests: |
gridExtra, MASS, Hmisc, tibble, mlpack, testthat, knitr, rmarkdown |
Published: |
2025-07-09 |
Author: |
Jason Cory Brunson
[aut, cre],
Emily Paul [ctb],
John Gracey [aut] |
Maintainer: |
Jason Cory Brunson <cornelioid at gmail.com> |
BugReports: |
https://github.com/corybrunson/gggda/issues |
License: |
GPL-3 |
URL: |
https://github.com/corybrunson/gggda,
https://corybrunson.github.io/gggda/ |
NeedsCompilation: |
no |
Materials: |
README NEWS |
CRAN checks: |
gggda results |
Documentation:
Downloads:
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