smallsets: Visual Documentation for Data Preprocessing
Data practitioners regularly use the 'R' and 'Python' programming languages to
prepare data for analyses. Thus, they encode important data preprocessing decisions in
'R' and 'Python' code. The 'smallsets' package subsequently decodes these decisions into
a Smallset Timeline, a static, compact visualisation of data preprocessing decisions
(Lucchesi et al. (2022) <doi:10.1145/3531146.3533175>). The visualisation consists of
small data snapshots of different preprocessing steps. The 'smallsets' package builds this
visualisation from a user's dataset and preprocessing code located in an 'R', 'R Markdown',
'Python', or 'Jupyter Notebook' file. Users simply add structured comments with snapshot
instructions to the preprocessing code. One optional feature in 'smallsets' requires
installation of the 'Gurobi' optimisation software and 'gurobi' 'R' package, available
from <https://www.gurobi.com>. More information regarding the optional feature and
'gurobi' installation can be found in the 'smallsets' vignette.
| Version: |
2.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
callr, colorspace, flextable, ggplot2, ggtext, knitr, patchwork, plotrix, reticulate, rmarkdown |
| Suggests: |
gurobi, testthat (≥ 3.0.0) |
| Published: |
2023-12-05 |
| DOI: |
10.32614/CRAN.package.smallsets |
| Author: |
Lydia R. Lucchesi 
[aut, cre],
Petra M. Kuhnert [ths],
Jenny L. Davis [ths],
Lexing Xie [ths] |
| Maintainer: |
Lydia R. Lucchesi <Lydia.Lucchesi at anu.edu.au> |
| License: |
GPL (≥ 3) |
| URL: |
https://lydialucchesi.github.io/smallsets/,
https://github.com/lydialucchesi/smallsets |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
smallsets results |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.