# Getting Started with EDAForge

# Introduction

EDAForge is an R package for automated exploratory data analysis (EDA). It provides functions for descriptive statistics, missing value analysis, correlation analysis, visualization, outlier detection and automated reporting.

    library(EDAForge)

# Example dataset

This vignette uses the built-in `iris` dataset.

    data <- iris
    head(iris)
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    #> 1          5.1         3.5          1.4         0.2  setosa
    #> 2          4.9         3.0          1.4         0.2  setosa
    #> 3          4.7         3.2          1.3         0.2  setosa
    #> 4          4.6         3.1          1.5         0.2  setosa
    #> 5          5.0         3.6          1.4         0.2  setosa
    #> 6          5.4         3.9          1.7         0.4  setosa

# Numeric summary

Generate descriptive statistics for all numeric variables.

    num_sum <- numeric_summary(iris)
    head(num_sum)
    #>
    #> =========================================
    #>       EDAForge Numeric Summary
    #> =========================================
    #>
    #>      Variable   N Missing Mean Median    SD Variance     SE   CV Minimum  Q1
    #>  Sepal.Length 150       0 5.84   5.80 0.828    0.686 0.0676 14.2     4.3 5.1
    #>   Sepal.Width 150       0 3.06   3.00 0.436    0.190 0.0356 14.3     2.0 2.8
    #>  Petal.Length 150       0 3.76   4.35 1.765    3.116 0.1441 47.0     1.0 1.6
    #>   Petal.Width 150       0 1.20   1.30 0.762    0.581 0.0622 63.6     0.1 0.3
    #>   Q3 Maximum IQR Range Skewness Kurtosis Shapiro_P
    #>  6.4     7.9 1.3   3.6    0.309   -0.606  1.02e-02
    #>  3.3     4.4 0.5   2.4    0.313    0.139  1.01e-01
    #>  5.1     6.9 3.5   5.9   -0.269   -1.417  7.41e-10
    #>  1.8     2.5 1.5   2.4   -0.101   -1.358  1.68e-08

# Missing value summary

Summarize missing values.

    missing_summary(iris)
    #>
    #> =========================================
    #>       EDAForge Missing Value Report
    #> =========================================
    #>
    #> Rows                         150
    #> Columns                      5
    #> Variables with Missing       0
    #> Complete Cases               150
    #> Total Missing Values         0
    #> Overall Missing              0.00%
    #>
    #> Variable Summary
    #> -----------------------------------------
    #>      Variable    Type Missing Percent Complete
    #>  Sepal.Length numeric       0       0      150
    #>   Sepal.Width numeric       0       0      150
    #>  Petal.Length numeric       0       0      150
    #>   Petal.Width numeric       0       0      150
    #>       Species  factor       0       0      150

# Correlation analysis

Compute the correlation matrix for numeric variables.

    correlation_analysis(iris)
    #>
    #> =====================================
    #>       Correlation Matrix
    #> =====================================
    #>
    #>              Sepal.Length Sepal.Width Petal.Length Petal.Width
    #> Sepal.Length        1.000      -0.118        0.872       0.818
    #> Sepal.Width        -0.118       1.000       -0.428      -0.366
    #> Petal.Length        0.872      -0.428        1.000       0.963
    #> Petal.Width         0.818      -0.366        0.963       1.000

# Export numeric summary

Output files in examples and vignettes should always be written to a temporary directory.

    outfile <- file.path(tempdir(), "NumericSummary.csv")

    export_numeric_summary(
      num_sum,
      outfile
    )

    file.exists(outfile)
    #> [1] TRUE

# Automated EDA

Generate an overall exploratory data analysis.

    auto_eda(iris)
    #>
    #> ========================================
    #>           EDAForge Report
    #> ========================================
    #>
    #> Modules Completed
    #>
    #> * Summary
    #> * Missing
    #> * Numeric
    #> * Categorical
    #> * Correlation
    #> * Outliers
    #> * PCA
    #> * Cluster
    #> * Statistics

# Session information

    sessionInfo()
    #> R version 4.5.1 (2025-06-13 ucrt)
    #> Platform: x86_64-w64-mingw32/x64
    #> Running under: Windows 10 x64 (build 19045)
    #>
    #> Matrix products: default
    #>   LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] LC_COLLATE=C                   LC_CTYPE=English_India.utf8
    #> [3] LC_MONETARY=English_India.utf8 LC_NUMERIC=C
    #> [5] LC_TIME=English_India.utf8
    #>
    #> time zone: Asia/Calcutta
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] EDAForge_0.1.1
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] vctrs_0.6.5        cli_3.6.6          knitr_1.51         rlang_1.3.0
    #>  [5] xfun_0.54          otel_0.2.0         generics_0.1.4     S7_0.2.0
    #>  [9] jsonlite_2.0.0     glue_1.8.0         e1071_1.7-16       htmltools_0.5.9
    #> [13] sass_0.4.10        scales_1.4.0       rmarkdown_2.31     grid_4.5.1
    #> [17] tibble_3.3.0       evaluate_1.0.5     jquerylib_0.1.4    fastmap_1.2.0
    #> [21] yaml_2.3.10        lifecycle_1.0.5    compiler_4.5.1     dplyr_1.1.4
    #> [25] RColorBrewer_1.1-3 pkgconfig_2.0.3    rstudioapi_0.19.0  farver_2.1.2
    #> [29] digest_0.6.37      R6_2.6.1           class_7.3-23       tidyselect_1.2.1
    #> [33] pillar_1.11.1      magrittr_2.0.3     bslib_0.9.0        proxy_0.4-28
    #> [37] tools_4.5.1        gtable_0.3.6       ggplot2_4.0.3      cachem_1.1.0