Package {DrData}

Title: Interactive Statistical Analysis and Machine Learning Platform
Version: 0.2.0
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
URL: https://github.com/mohsenmehdinia/DrData
BugReports: https://github.com/mohsenmehdinia/DrData/issues
RoxygenNote: 7.3.3
Imports: shiny (≥ 1.7.0), stats, utils
Suggests: shinydashboard, plotly, DT, ggplot2, dplyr, tidyr, readr, readxl, caret, randomForest, rpart, rpart.plot, e1071, class, nnet, colourpicker, glmnet, cluster, dbscan, GGally, gbm, pROC, reshape2, scales, nortest, tseries, testthat (≥ 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-05-13 14:11:26 UTC; ASUS
Author: Mohsen Mehdinia [aut, cre]
Maintainer: Mohsen Mehdinia <mehdinia.55@gmail.com>
Repository: CRAN
Date/Publication: 2026-05-19 07:10:16 UTC

Build a model formula with optional interaction terms

Description

Build a model formula with optional interaction terms

Usage

build_model_formula(
  target,
  features,
  use_interactions = FALSE,
  interaction_vars = NULL
)

Arguments

target

Single character: response variable name.

features

Character vector of predictor names.

use_interactions

Logical; add two-way interactions? Default FALSE.

interaction_vars

Character vector of variables to interact.

Value

A formula object.

Examples

build_model_formula("mpg", c("cyl", "hp", "wt"))
build_model_formula("mpg", c("cyl","hp","wt"), TRUE, c("cyl","hp"))

Compute regression performance metrics

Description

Compute regression performance metrics

Usage

ml_metrics_regression(y_true, y_pred)

Arguments

y_true

Numeric vector of observed values.

y_pred

Numeric vector of predicted values.

Value

One-row data.frame with columns RMSE, MAE, R2.

Examples

ml_metrics_regression(c(1,2,3,4,5), c(1.1,1.9,3.2,3.8,5.1))

Prepare a data frame for machine learning

Description

Prepare a data frame for machine learning

Usage

ml_prepare_data(data, target, features = NULL)

Arguments

data

A data.frame.

target

Single character string: the response column name.

features

Character vector of predictor names. Default: all except target.

Value

Named list: data, target, features.

Examples

prep <- ml_prepare_data(mtcars, target = "mpg")
names(prep)

Split a data frame into training and test sets

Description

Split a data frame into training and test sets

Usage

ml_split(data, train_ratio = 0.8, seed = 42)

Arguments

data

A data.frame to split.

train_ratio

Numeric in (0,1); proportion for training. Default 0.8.

seed

Integer random seed. Default 42.

Value

Named list with train and test data frames.

Examples

splits <- ml_split(mtcars, train_ratio = 0.75, seed = 1)
nrow(splits$train)

Run the DrData Application

Description

Launches the 'DrData' interactive 'Shiny' application for statistical analysis and machine learning workflows.

Usage

run_app()

Value

No return value, called for side effects.

Examples

if(interactive()){
  run_app()
}