Provides automated exploratory data analysis, visualization recommendation, summary statistics, missing-value assessment, outlier screening, and HTML dashboard generation for rectangular data. The package automatically identifies variable types and common analytical relationships and recommends appropriate visualization methods based on data structure, cardinality, and analytical objectives. It provides a transparent, reproducible workflow for data profiling and visualization that can be used independently or as a component within interactive applications.
AutoViz 1.0.0 is an R package for automated exploratory data analysis, visualization recommendation, and lightweight HTML dashboard generation.
AutoViz is not a replacement for Shiny. Shiny is a framework for building interactive web applications. AutoViz is an analytical layer that inspects a rectangular dataset and recommends useful visualizations and diagnostics.
Install the released package from CRAN when available:
install.packages("AutoViz")
Install the development version from GitHub:
remotes::install_github("autoviz-r/AutoViz")
library(AutoViz)
av <- autoviz(iris)
av
vb_profile(iris)
vb_summary(iris)
vb_missing(iris)
vb_outliers(iris)
vb_recommend(iris)
vb_plot(iris, "Species", type = "bar")
dashboard <- vb_dashboard(iris, title = "Iris Data Dashboard")
htmltools::save_html(dashboard, "autoviz-dashboard.html")
The package follows a simple pipeline:
data -> profiling -> diagnostics -> recommendation -> visualization/dashboard
The core workflow does not require Shiny. Shiny can be used separately when a user wants to wrap AutoViz outputs in a larger interactive application.
Version 1.0.0 focuses on rectangular data, transparent rule-based recommendations, standard ggplot2 visualizations, and a lightweight HTML dashboard. It intentionally avoids opaque machine-learning-driven chart selection so that recommendations remain reproducible and explainable.
MIT.