Package {EDAForge}

Type: Package
Title: Automatic Exploratory Data Analysis
Version: 0.1.1
Description: 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) <doi:10.1098/rspl.1895.0041>, and Wickham (2014) <doi:10.18637/jss.v059.i10>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 8.0.0
Depends: R (≥ 4.2)
Imports: e1071, rlang, dplyr, ggplot2, tidyr, psych, factoextra, openxlsx, GGally, visdat, igraph
Suggests: knitr,mice, rmarkdown, testthat (≥ 3.0.0), tibble
Config/testthat/edition: 3
URL: https://github.com/vinodhpmd/EDAForge
BugReports: https://github.com/vinodhpmd/EDAForge/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-08-01 00:38:33 UTC; m
Author: Vinodhkumar Obli Rajendran [aut, cre], Keerthi Aaradhana [aut]
Maintainer: Vinodhkumar Obli Rajendran <vinodhkumar.rajendran@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-08 11:30:20 UTC

Automatic Exploratory Data Analysis

Description

Performs a complete exploratory data analysis of a dataset.

Usage

auto_eda(data)

Arguments

data

A data.frame.

Value

An object of class AutoEDAReport.

Automatic Plot

Description

Automatically selects an appropriate plot based on the variable type.

Usage

auto_plot(data, variable)

Arguments

data

A data.frame.

variable

Name of the variable.

Value

A ggplot object.

Bar Plot

Description

Draws a bar plot for a categorical variable.

Usage

bar_plot(data, variable)

Arguments

data

A data.frame.

variable

Name of the categorical variable.

Value

A ggplot object.

Box Plot

Description

Draws a box plot for a numeric variable.

Usage

box_plot(data, variable)

Arguments

data

A data.frame.

variable

Name of the numeric variable.

Value

A ggplot object.

Categorical Plot

Description

Draws a bar plot for a categorical variable.

Usage

categorical_plot(data, variable)

Arguments

data

A data.frame.

variable

Name of a categorical variable.

Value

A ggplot object.

Categorical Summary

Description

Generates summary statistics for categorical variables.

Usage

categorical_summary(data)

Arguments

data

A data.frame.

Value

An object of class CategoricalSummary.

Cluster Analysis

Description

Performs K-means clustering on numeric variables.

Usage

cluster_analysis(data, centers = 3, scale = TRUE, nstart = 25)

Arguments

data

A data.frame.

centers

Number of clusters.

scale

Logical; should variables be scaled?

nstart

Number of random starts.

Value

An object of class ClusterResult.

Hierarchical Clustering Dendrogram

Description

Performs hierarchical clustering and displays a dendrogram.

Usage

cluster_dendrogram(data)

Arguments

data

A data.frame.

Value

Invisibly returns the hclust object.

Elbow Method

Description

Displays the Within-Cluster Sum of Squares (WSS) to help determine the optimal number of clusters.

Usage

cluster_elbow(data)

Arguments

data

A data.frame.

Value

A ggplot object.

Cluster Plot

Description

Plots clustering results.

Usage

cluster_plot(cluster)

Arguments

cluster

A ClusterResult object.

Value

A ggplot object.

Silhouette Plot

Description

Displays the average silhouette width for different numbers of clusters to help identify the optimal clustering solution.

Usage

cluster_silhouette(data)

Arguments

data

A data.frame.

Value

A ggplot object.

Correlation Analysis

Description

Computes the Pearson correlation matrix for numeric variables.

Usage

correlation_analysis(data, method = "pearson")

Arguments

data

A data.frame.

method

Correlation method ("pearson", "spearman", or "kendall").

Value

An object of class CorrelationMatrix.

Correlation Network

Description

Builds a correlation network from numeric variables.

Usage

correlation_network(data, cutoff = 0.7)

Arguments

data

A data.frame.

cutoff

Minimum absolute correlation.

Value

An igraph object.

Correlation Heatmap

Description

Displays a correlation heatmap.

Usage

correlation_plot(data, method = "pearson")

Arguments

data

A data.frame.

method

Correlation method.

Value

A ggplot object.

Pairwise Correlation Tests

Description

Computes pairwise Pearson correlation coefficients, p-values, and confidence intervals for all numeric variables in a dataset.

Usage

correlation_test(data)

Arguments

data

A data.frame.

Value

An object of class CorrelationTest.

Create EDAForge Report

Description

Creates a comprehensive EDAForge report.

Usage

create_report(data)

Arguments

data

A data.frame.

Value

An object of class AutoEDAReport.

Density Plot

Description

Draws a density plot for a numeric variable.

Usage

density_plot(data, variable)

Arguments

data

A data.frame.

variable

Name of the numeric variable.

Value

A ggplot object.

Export Report to Excel

Description

Exports an EDAForge report to an Excel workbook.

Usage

export_excel(report, file = "EDAForge_Report.xlsx")

Arguments

report

An AutoEDAReport object returned by auto_eda() or create_report().

file

Output Excel filename.

Value

Invisibly returns the normalized output filename.

Export Numeric Summary

Description

Exports a numeric summary table to a CSV file.

Usage

export_numeric_summary(x, file)

Arguments

x

An object of class NumericSummary.

file

Path to the output CSV file.

Value

Invisibly returns the normalized output file path.

Histogram Plot

Description

Draws a histogram for a numeric variable.

Usage

histogram_plot(data, variable)

Arguments

data

A data.frame.

variable

Name of the numeric variable.

Value

A ggplot object.

Export HTML Report

Description

Creates an HTML report from an AutoEDAReport object.

Usage

html_report(report, file = "EDAForge_Report.html")

Arguments

report

An AutoEDAReport object.

file

Output HTML filename.

Value

Invisibly returns the output filename.

Missing Values Heatmap

Description

Displays a heatmap showing the pattern of missing values in a dataset.

Usage

missing_heatmap(data)

Arguments

data

A data.frame.

Value

A ggplot object.

Missing Value Pattern

Description

Displays the pattern of missing values in a dataset.

Usage

missing_pattern(data)

Arguments

data

A data.frame.

Details

This function is a wrapper around mice::md.pattern() and visualizes the missing-data pattern.

Value

Invisibly returns the missing-value pattern matrix produced by mice::md.pattern().

Examples

dat <- iris
dat$Sepal.Length[1:10] <- NA
missing_pattern(dat)

Missing Value Summary

Description

Summarizes missing values in a dataset.

Usage

missing_summary(data)

Arguments

data

A data.frame.

Value

An object of class MissingSummary.

Numeric Summary

Description

Generates descriptive statistics for all numeric variables.

Usage

numeric_summary(data)

Arguments

data

A data.frame.

Value

An object of class NumericSummary.

Outlier Detection

Description

Detects outliers in all numeric variables.

Usage

outlier_detection(data, method = "IQR", threshold = 3)

Arguments

data

A data.frame.

method

Outlier detection method. One of "IQR", "ZScore", or "MAD".

threshold

Numeric threshold used for ZScore and MAD methods.

Value

An object of class OutlierSummary.

Outlier Summary

Description

Detects and summarizes outliers.

Usage

outlier_summary(data, method = "IQR", threshold = 3)

Arguments

data

A data.frame.

method

Method used for outlier detection ("iqr" or "zscore").

threshold

Threshold for outlier detection.

Value

A data.frame summarizing detected outliers.

Outlier Values

Description

Returns all observations identified as outliers for a specified numeric variable using the 1.5 × IQR rule.

Usage

outlier_values(data, variable)

Arguments

data

A data.frame.

variable

Name of a numeric variable.

Value

A data.frame containing the outlier observations.

Pair Plot

Description

Creates a scatterplot matrix for all numeric variables.

Usage

pair_plot(data)

Arguments

data

A data.frame.

Value

A GGally ggmatrix object.

Principal Component Analysis

Description

Performs Principal Component Analysis (PCA) on numeric variables.

Usage

pca_analysis(data, scale = TRUE)

Arguments

data

A data.frame.

scale

Logical; should variables be scaled?

Value

An object of class PCAResult.

PCA Biplot

Description

Displays a PCA biplot showing both variables and observations.

Usage

pca_biplot(pca)

Arguments

pca

A PCAResult object.

Value

A ggplot object.

PCA Individual Plot

Description

Displays the observations in PCA space.

Usage

pca_individual_plot(pca)

Arguments

pca

A PCAResult object.

Value

A ggplot object.

Scree Plot

Description

Draws a scree plot showing the percentage of variance explained by each principal component.

Usage

pca_scree_plot(pca)

Arguments

pca

A PCAResult object.

Value

A ggplot object.

PCA Variable Plot

Description

Displays variable contributions to the principal components.

Usage

pca_variable_plot(pca)

Arguments

pca

A PCAResult object.

Value

A ggplot object.

Export PDF Report

Description

Creates a PDF report from an AutoEDAReport object.

Usage

pdf_report(report, file = "EDAForge_Report.pdf")

Arguments

report

An AutoEDAReport object.

file

Output PDF filename.

Value

Invisibly returns the output filename.

Missing Values Plot

Description

Visualizes the number of missing values in each variable.

Usage

plot_missing(data)

Arguments

data

A data.frame.

Value

A ggplot object.

Plot Outliers

Description

Creates boxplots for all numeric variables to visualize potential outliers.

Usage

plot_outliers(data)

Arguments

data

A data.frame.

Value

A ggplot object.

Print EDAForge Report

Description

Print EDAForge Report

Usage

## S3 method for class 'AutoEDAReport'
print(x, ...)

Arguments

x

AutoEDAReport object.

...

Additional arguments.

Value

Invisibly returns the AutoEDAReport object.

Print Categorical Summary

Description

Prints the categorical summary generated by EDAForge.

Usage

## S3 method for class 'CategoricalSummary'
print(x, ...)

Arguments

x

A CategoricalSummary object.

...

Additional arguments passed to print.data.frame().

Value

Invisibly returns the CategoricalSummary object.

Print Cluster Analysis Result

Description

Prints a summary of K-means clustering results.

Usage

## S3 method for class 'ClusterResult'
print(x, ...)

Arguments

x

A ClusterResult object.

...

Additional arguments (unused).

Value

Invisibly returns the ClusterResult object.

Print Correlation Matrix

Description

Prints a correlation matrix.

Usage

## S3 method for class 'CorrelationMatrix'
print(x, ...)

Arguments

x

A CorrelationMatrix object.

...

Additional arguments (unused).

Value

Invisibly returns the CorrelationMatrix object.

Print Missing Summary

Description

Prints the missing value summary generated by EDAForge.

Usage

## S3 method for class 'MissingSummary'
print(x, ...)

Arguments

x

A MissingSummary object.

...

Additional arguments (unused).

Value

Invisibly returns the MissingSummary object.

Print Numeric Summary

Description

Prints the numeric summary generated by EDAForge.

Usage

## S3 method for class 'NumericSummary'
print(x, ...)

Arguments

x

A NumericSummary object.

...

Additional arguments (unused).

Value

Invisibly returns the NumericSummary object.

Print Outlier Summary

Description

Prints the outlier summary generated by EDAForge.

Usage

## S3 method for class 'OutlierSummary'
print(x, ...)

Arguments

x

An OutlierSummary object.

...

Additional arguments (unused).

Value

Invisibly returns the OutlierSummary object.

Print PCA Result

Description

Prints a summary of a principal component analysis.

Usage

## S3 method for class 'PCAResult'
print(x, ...)

Arguments

x

A PCAResult object.

...

Additional arguments (unused).

Value

Invisibly returns the PCAResult object.

Scatter Plot

Description

Draws a scatter plot for two numeric variables.

Usage

scatter_plot(data, x, y)

Arguments

data

A data.frame.

x

Name of the X variable.

y

Name of the Y variable.

Value

A ggplot object.

Dataset Summary

Description

Provides a quick overview of a dataset.

Usage

summary_data(data)

Arguments

data

A data.frame.

Value

A list containing dataset information.

EDAForge Theme

Description

A consistent ggplot2 theme used throughout EDAForge.

Usage

theme_autoeda()

Value

A ggplot2 theme object.

Validate categorical variable

Description

Validate categorical variable

Usage

validate_categorical(data, variable)

Arguments

data

Data frame.

variable

Variable name.

Validate input data

Description

Checks whether the input is a valid data.frame.

Usage

validate_data(data)

Arguments

data

Input dataset.

Validate PCA / Clustering input

Description

Validate PCA / Clustering input

Usage

validate_multivariate(data)

Arguments

data

Data frame.

Validate numeric variable

Description

Validate numeric variable

Usage

validate_numeric(data, variable)

Arguments

data

Data frame.

variable

Variable name.

Validate correlation data

Description

Validate correlation data

Usage

validate_numeric_dataframe(data)

Arguments

data

Data frame.

Validate one variable

Description

Validate one variable

Usage

validate_variable(data, variable)

Arguments

data

Data frame.

variable

Variable name.

Validate two variables

Description

Validate two variables

Usage

validate_xy(data, x, y)

Arguments

data

Data frame.

x

First variable.

y

Second variable.

Export Word Report

Description

Creates a Word report from an AutoEDAReport object.

Usage

word_report(report, file = "EDAForge_Report.docx")

Arguments

report

An AutoEDAReport object.

file

Output Word filename.

Value

Invisibly returns the output filename.