EDAForge is an R package for performing comprehensive Automatic Exploratory Data Analysis (EDA) with minimal code.
The package generates descriptive statistics, missing value summaries, visualizations, correlation analysis, outlier detection, principal component analysis (PCA), clustering, and automated reports.
- Dataset summary
- Missing value analysis
- Numeric summary statistics
- Categorical variable summary
- Correlation analysis
- Automatic visualizations
- Outlier detection
- Principal Component Analysis (PCA)
- Cluster analysis
- Excel report generation
- Publication-quality graphics
Install the development version from GitHub.
# install.packages("remotes")
remotes::install_github("vinodhpmd/EDAForge")library(EDAForge)
report <- auto_eda(iris)
report
#>
#> ========================================
#> EDAForge Report
#> ========================================
#>
#> Modules Completed
#>
#> * Summary
#> * Missing
#> * Numeric
#> * Categorical
#> * Correlation
#> * Outliers
#> * PCA
#> * Clustersummary_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
#>
#> attr(,"class")
#> [1] "SummaryData"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
numeric_summary(iris)
#>
#> =========================================
#> 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-08correlation_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 1.000pca <- pca_analysis(iris)
pca
#>
#> ==============================
#> Principal Component Analysis
#> ==============================
#>
#> Length Class Mode
#> sdev 4 -none- numeric
#> rotation 16 -none- numeric
#> center 4 -none- numeric
#> scale 4 -none- numeric
#> x 600 -none- numeric
cluster_analysis(iris)
#>
#> ===================================
#> Cluster Analysis
#> ===================================
#>
#> Clusters : 3
#>
#> Cluster Sizes
#> [1] 53 47 50report <- auto_eda(iris)
report
#>
#> ========================================
#> EDAForge Report
#> ========================================
#>
#> Modules Completed
#>
#> * Summary
#> * Missing
#> * Numeric
#> * Categorical
#> * Correlation
#> * Outliers
#> * PCA
#> * ClusterEDAForge
│
├── Data Summary
├── Missing Value Analysis
├── Numeric Summary
├── Categorical Summary
├── Correlation Analysis
├── Outlier Detection
├── PCA
├── Cluster Analysis
├── Automatic Plots
└── Report Generation
Vinodhkumar
MIT License