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AutoViz: Automated Exploratory Data Visualization

Overview

AutoViz provides a transparent, rule-based workflow for exploratory analysis. It profiles variables, quantifies missingness, screens numeric outliers, and creates visualization recommendations.

Basic workflow

av <- autoviz(iris)
av
#> <AutoViz>
#> Rows: 150  Columns: 5 
#> Recommended visualizations: 7 
#> 
#>  priority    plot            x            y                 reason
#>         1     bar      Species         <NA> Categorical frequency.
#>         2 scatter Sepal.Length  Sepal.Width  Numeric relationship.
#>         2 scatter Sepal.Length Petal.Length  Numeric relationship.
#>         2 scatter Sepal.Length  Petal.Width  Numeric relationship.
#>         2 scatter  Sepal.Width Petal.Length  Numeric relationship.
#>         2 scatter  Sepal.Width  Petal.Width  Numeric relationship.
#>         2 scatter Petal.Length  Petal.Width  Numeric relationship.

Profiling

vb_profile(iris)
#>                  variable   class        type n_unique missing missing_pct
#> Sepal.Length Sepal.Length numeric     numeric       35       0           0
#> Sepal.Width   Sepal.Width numeric     numeric       23       0           0
#> Petal.Length Petal.Length numeric     numeric       43       0           0
#> Petal.Width   Petal.Width numeric     numeric       22       0           0
#> Species           Species  factor categorical        3       0           0

Recommendations

vb_recommend(iris)
#>   priority    plot            x            y                 reason
#> 1        1     bar      Species         <NA> Categorical frequency.
#> 2        2 scatter Sepal.Length  Sepal.Width  Numeric relationship.
#> 3        2 scatter Sepal.Length Petal.Length  Numeric relationship.
#> 4        2 scatter Sepal.Length  Petal.Width  Numeric relationship.
#> 5        2 scatter  Sepal.Width Petal.Length  Numeric relationship.
#> 6        2 scatter  Sepal.Width  Petal.Width  Numeric relationship.
#> 7        2 scatter Petal.Length  Petal.Width  Numeric relationship.

A plot

vb_plot(iris, "Species", type = "bar")

HTML dashboard

dash <- vb_dashboard(iris, title = "Iris Dashboard")
dash

Iris Dashboard

Automated exploratory data analysis generated by AutoViz 1.0.0.

150

Rows

5

Columns

0

Missing values

7

Recommendations

Visualization recommendations

priority plot x y reason
1 bar Species NA Categorical frequency.
2 scatter Sepal.Length Sepal.Width Numeric relationship.
2 scatter Sepal.Length Petal.Length Numeric relationship.
2 scatter Sepal.Length Petal.Width Numeric relationship.
2 scatter Sepal.Width Petal.Length Numeric relationship.
2 scatter Sepal.Width Petal.Width Numeric relationship.
2 scatter Petal.Length Petal.Width Numeric relationship.

Dataset profile

variable class type n_unique missing missing_pct
Sepal.Length numeric numeric 35 0 0
Sepal.Width numeric numeric 23 0 0
Petal.Length numeric numeric 43 0 0
Petal.Width numeric numeric 22 0 0
Species factor categorical 3 0 0

Missing values

No missing values detected.

The dashboard layer is independent of Shiny. Shiny can be used when a full application with user-defined inputs and reactive server logic is required.

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.