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.