Automatic Exploratory Data Analysis

Automatically performs exploratory data analysis (EDA) for tabular datasets, including data summaries, missing value analysis, descriptive statistics, visualizations, correlation analysis, outlier detection, and automated report generation. The package provides a streamlined workflow for rapid data exploration and produces publication-ready tables and graphics. For methodological details see Tukey (1977, ISBN:9780201076165), Pearson (1895) , and Wickham (2014) .

EDAForge

Overview

EDAForge is an R package for comprehensive automated Exploratory Data Analysis (EDA) of tabular datasets. It provides descriptive statistics, data quality assessment, missing value analysis, visualization, correlation analysis, outlier detection, principal component analysis (PCA), clustering, automated report generation, and publication-quality graphics with minimal code.

Features

• Automated exploratory data analysis
• Dataset summary and data quality assessment
• Missing value analysis
• Numeric and categorical summaries
• Correlation analysis and visualization
• Automatic statistical graphics
• Outlier detection
• Principal Component Analysis (PCA)
• Cluster analysis
• HTML, PDF, and Word report generation
• Excel export
• Publication-quality graphics

Installation

Development version (GitHub)

# install.packages("remotes")
remotes::install_github("vinodhpmd/EDAForge")

After EDAForge is available on CRAN, install the released version using:

install.packages("EDAForge")

Quick Start

library(EDAForge)

report <- auto_eda(iris)

report
#>
#> ========================================
#>         EDAForge Report
#> ========================================
#>
#> Modules Completed
#>
#> * Summary
#> * Missing
#> * Numeric
#> * Categorical
#> * Correlation
#> * Outliers
#> * PCA
#> * Cluster

Dataset Summary

summary_data(iris)
#> $Rows
#> [1] 150
#>
#> $Columns
#> [1] 5
#>
#> $NumericVariables
#> [1] 4
#>
#> $CharacterVariables
#> [1] 0
#>
#> $FactorVariables
#> [1] 1
#>
#> $LogicalVariables
#> [1] 0
#>
#> $MissingValues
#> [1] 0
#>
#> $DuplicateRows
#> [1] 1
#>
#> $MemoryMB
#> [1] 0.01

Missing Value Analysis

missing_summary(iris)

Numeric Summary

numeric_summary(iris)

Correlation Analysis

correlation_analysis(iris)

Principal Component Analysis

pca <- pca_analysis(iris)

pca

Cluster Analysis

cluster <- cluster_analysis(iris)

cluster

Generate Complete Report

report <- auto_eda(iris)

report

Package Structure

EDAForge
│
├── Data Summary
├── Missing Value Analysis
├── Numeric Summary
├── Categorical Summary
├── Correlation Analysis
├── Outlier Detection
├── PCA
├── Cluster Analysis
├── Automatic Plots
├── HTML/PDF/Word Reports
└── Excel Export

Authors

Vinodhkumar Obli Rajendran
Keerthi Aardhana

Documentation

Documentation, examples, issue tracking, and development updates are available at:

https://github.com/vinodhpmd/EDAForge

Citation

If you use EDAForge in your research, please cite the package using:

citation("EDAForge")

Issues

Please report bugs, feature requests, or suggestions at:

https://github.com/vinodhpmd/EDAForge/issues

License

This package is distributed under the MIT License. See the LICENSE file for details.