vizintel: Visualization Intelligence Toolkit
Provides automated data profiling and visualization recommendation
tools for exploratory data analysis (EDA). The package inspects a data frame,
classifies each variable as numeric, categorical, or datetime, and computes
distributional statistics including skewness, excess kurtosis, Shapiro-Wilk
normality test p-values (Royston 1995) <doi:10.2307/2986146>, and cardinality.
Based on these profiles, the package scores and ranks candidate chart types
(histogram, density, boxplot, violin, bar, lollipop, scatter, hexbin, line,
heatmap) using an internal taxonomy that accounts for variable types, analysis
goal, skewness, cardinality, and data size. Additional functions audit existing
ggplot objects for common visualization integrity problems such as truncated
axes, lie factors, overplotting, and dual y-axes following principles described
in Tufte (1983, ISBN:0961392142) and Wilke (2019)
<https://clauswilke.com/dataviz/>. The package also checks visualizations for
accessibility issues and suggests alternative chart types for a given plot.
| Version: |
0.1.1 |
| Imports: |
dplyr, e1071, ggplot2, gridExtra, rlang |
| Suggests: |
colorspace, hexbin, patchwork, testthat (≥ 3.0.0) |
| Published: |
2026-10-31 |
| DOI: |
10.32614/CRAN.package.vizintel (may not be active yet) |
| Author: |
Pooja Thirumal [aut, cre],
Ronesha Navaneethan [aut],
Amrit Pal [ctb] (Supervisor) |
| Maintainer: |
Pooja Thirumal <poojathirumal70 at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README |
| CRAN checks: |
vizintel results |
Documentation:
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