DrData
Interactive Statistical Analysis and Machine Learning Platform
A 'Shiny'-based interactive platform for end-to-end data science workflows. Provides modules for data import (CSV, 'Excel', RDS, TXT), data preprocessing (missing value imputation, encoding, scaling, outlier removal), exploratory data analysis with interactive plots and normality tests, supervised learning (regression and classification each with eight algorithms), and unsupervised learning (k-means, hierarchical clustering, density-based spatial clustering of applications with noise). Designed for students and practitioners in data science and artificial intelligence.
DrData
DrData is a Shiny-based interactive platform for end-to-end data science workflows — designed for students and practitioners in data science and artificial intelligence.
Features
| Module | What it does |
|---|---|
| Data Import | CSV, Excel (xlsx/xls), RDS, TXT; built-in example datasets (iris, mtcars, Titanic) |
| Preprocessing | Missing value imputation, duplicate removal, column/row dropping, scaling (z-score, min-max, log), one-hot encoding, outlier detection |
| EDA | 8 interactive plot types; normality tests (Shapiro-Wilk, KS, Anderson-Darling, Jarque-Bera); Auto-EDA report |
| Regression | 8 algorithms: Linear, Ridge, Lasso, Decision Tree, Random Forest, SVM, GBM, Neural Network; interaction terms; full diagnostic plots |
| Classification | 8 algorithms: Logistic, Decision Tree, Random Forest, SVM, KNN, Naive Bayes, GBM, Neural Network; ROC curve; confusion matrix |
| Clustering | K-Means, Hierarchical, DBSCAN; elbow plot; silhouette score; cluster profiles |
Installation
From CRAN (once accepted)
install.packages("DrData")
Development version
# install.packages("remotes")
remotes::install_github("mohsenmehdinia/DrData")
Quick Start
library(DrData)
run_app()
Load the built-in iris or mtcars dataset from the Data Import tab to
explore immediately — no data preparation needed.
Workflow
Data Import → Preprocessing → EDA → Regression / Classification / Clustering
License
MIT © 2026 Mohsen Mehdinia