Package 'AutoViz'

Title: Automated Data Visualization and Exploratory Dashboarding
Description: 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.
Authors: Vinodhkumar Obli Rajendran [aut, cre], Keerthi Aaradhana [aut]
Maintainer: Vinodhkumar Obli Rajendran <[email protected]>
License: MIT + file LICENSE
Version: 1.0.0
Built: 2026-09-14 19:38:41 UTC
Source: https://github.com/cran/AutoViz

Help Index

Run an automated EDA workflow

Description

Run an automated EDA workflow

Usage

autoviz(data, outcome = NULL, time = NULL, max_plots = 12)

Arguments

data

A data frame.
outcome

Optional outcome variable.
time

Optional date/time variable.
max_plots

Maximum number of recommendations shown.

Value

An object of class autoviz.

Generate an HTML dashboard

Description

Creates a self-contained HTML representation of the automated EDA results. The dashboard is deliberately independent of Shiny and can be saved or embedded in reports.

Usage

vb_dashboard(data, outcome = NULL, time = NULL, title = "AutoViz Dashboard")

Arguments

data

A data frame.
outcome

Optional outcome variable.
time

Optional date/time variable.
title

Dashboard title.

Value

An htmltools tag object.

Assess missing values

Description

Assess missing values

Usage

vb_missing(data)

Arguments

data

A data frame.

Value

A data frame ranked by missing-value percentage.

Detect simple numeric outliers using the IQR rule

Description

Detect simple numeric outliers using the IQR rule

Usage

vb_outliers(data, multiplier = 1.5)

Arguments

data

A data frame.
multiplier

IQR multiplier. Defaults to 1.5.

Value

A data frame with outlier counts and thresholds.

Create a recommended visualization

Description

Create a recommended visualization

Usage

vb_plot(
  data,
  x,
  y = NULL,
  type = c("bar", "histogram", "density", "scatter", "line", "boxplot")
)

Arguments

data

A data frame.
x

Variable used on the x axis.
y

Optional y variable.
type

Plot type: bar, histogram, density, scatter, line, or boxplot.

Value

A ggplot object.

Profile a rectangular dataset

Description

Returns a compact machine-readable profile describing dimensions, variable classes, missingness, and cardinality.

Usage

vb_profile(data)

Arguments

data

A data frame.

Value

A data frame with one row per variable.

Recommend visualizations from a dataset

Description

Uses variable types, cardinality, and optional outcome/time variables to generate a ranked set of practical visualization recommendations.

Usage

vb_recommend(data, outcome = NULL, time = NULL, max_pairs = 10)

Arguments

data

A data frame.
outcome

Optional outcome variable name.
time

Optional date/time variable name.
max_pairs

Maximum number of numeric relationships to recommend.

Value

A data frame containing recommended plot specifications.

Summarize a dataset

Description

Produces variable-level descriptive statistics suitable for automated EDA.

Usage

vb_summary(data)

Arguments

data

A data frame.

Value

A data frame with summary information.