Package 'AutoEDA'

Title: Automatic Exploratory Data Analysis
Description: Automatically performs exploratory data analysis, including data summaries, missing value analysis, descriptive statistics, visualizations, correlation analysis, outlier detection, and report generation.
Authors: Vinodhkumar [aut, cre]
Maintainer: Vinodhkumar <[email protected]>
License: MIT + file LICENSE
Version: 0.0.1
Built: 2026-10-13 11:27:56 UTC
Source: https://github.com/vinodhpmd/autoeda

Help Index


Automatic Exploratory Data Analysis

Description

Performs automatic exploratory data analysis.

Usage

auto_eda(data)

Arguments

data

A data.frame.

Value

An object of class AutoEDA.


Automatic Plot

Description

Automatic Plot

Usage

auto_plot(data, variable)

Bar Plot

Description

Bar Plot

Usage

bar_plot(data, variable)

Box Plot

Description

Box Plot

Usage

box_plot(data, variable)

Plot Categorical Variable

Description

Plot Categorical Variable

Usage

categorical_plot(data, variable)

Arguments

data

data.frame

variable

variable name


Categorical Variable Summary

Description

Summarizes categorical variables (factor and character).

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)

Arguments

data

A data.frame.

centers

Number of clusters.

scale

Logical.

Value

ClusterResult object.


Hierarchical Clustering

Description

Hierarchical Clustering

Usage

cluster_dendrogram(data)

Arguments

data

data.frame


Elbow Method

Description

Elbow Method

Usage

cluster_elbow(data)

Arguments

data

data.frame


Cluster Plot

Description

Cluster Plot

Usage

cluster_plot(cluster)

Arguments

cluster

ClusterResult


Silhouette Plot

Description

Silhouette Plot

Usage

cluster_silhouette(data)

Arguments

data

data.frame


Correlation Analysis

Description

Computes correlation matrix for numeric variables.

Usage

correlation_analysis(data, method = "pearson")

Arguments

data

A data.frame.

method

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

Value

Correlation matrix.


Correlation Network

Description

Correlation Network

Usage

correlation_network(data, cutoff = 0.7)

Arguments

data

data.frame

cutoff

correlation cutoff


Correlation Heatmap

Description

Correlation Heatmap

Usage

correlation_plot(data)

Arguments

data

data.frame


Pairwise Correlation Tests

Description

Pairwise Correlation Tests

Usage

correlation_test(data)

Arguments

data

data.frame


Create AutoEDA Report

Description

Create AutoEDA Report

Usage

create_report(data)

Arguments

data

data.frame

Value

AutoEDAReport


Density Plot

Description

Density Plot

Usage

density_plot(data, variable)

Export Report to Excel

Description

Export Report to Excel

Usage

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

Arguments

report

AutoEDAReport

file

Excel file


Export Numeric Summary

Description

Export Numeric Summary

Usage

export_numeric_summary(x, file = "NumericSummary.csv")

Arguments

x

NumericSummary object

file

Output csv filename


Histogram

Description

Histogram

Usage

histogram_plot(data, variable)

HTML Report

Description

HTML Report

Usage

html_report(report)

Missing Value Heatmap

Description

Missing Value Heatmap

Usage

missing_heatmap(data)

Arguments

data

data.frame


Missing Data Pattern

Description

Missing Data Pattern

Usage

missing_pattern(data)

Arguments

data

data.frame


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 Variable Summary

Description

Comprehensive descriptive statistics for all numeric variables.

Usage

numeric_summary(data)

Arguments

data

A data.frame.

Value

An object of class NumericSummary.


Detect Outliers

Description

Detects outliers in numeric variables using the IQR method.

Usage

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

Arguments

data

A data.frame.

method

"IQR", "ZScore", or "MAD"

threshold

Threshold for Z-score or MAD

Value

An object of class OutlierSummary.


Outlier Summary

Description

Summarize outliers using the IQR method.

Usage

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

Arguments

data

A data.frame.

method

"IQR","ZScore","MAD"

Value

A data.frame.


Pair Plot

Description

Pair Plot

Usage

pair_plot(data)

Principal Component Analysis

Description

Performs PCA on numeric variables.

Usage

pca_analysis(data, scale = TRUE)

Arguments

data

A data.frame.

scale

Logical. Scale variables?

Value

PCAResult object.


PCA Biplot

Description

PCA Biplot

Usage

pca_biplot(pca)

Arguments

pca

PCAResult


PCA Individual Plot

Description

PCA Individual Plot

Usage

pca_individual_plot(pca)

Scree Plot

Description

Scree Plot

Usage

pca_scree_plot(pca)

Arguments

pca

PCAResult


PCA Variable Plot

Description

PCA Variable Plot

Usage

pca_variable_plot(pca)

Plot Missing Values

Description

Plot Missing Values

Usage

plot_missing(data)

Arguments

data

data.frame


Plot Outliers

Description

Plot Outliers

Usage

plot_outliers(data)

Arguments

data

data.frame


Scatter Plot

Description

Scatter Plot

Usage

scatter_plot(data, x, y)

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.


AutoEDA Theme

Description

AutoEDA Theme

Usage

theme_autoeda()