Statistical Workflows

AI-enhanced methods for statistical analysis and data pipeline optimization in R

Overview

This page focuses on how AI can enhance traditional statistical workflows in R, improving efficiency and providing new insights.

Featured Workflows

Automated Exploratory Data Analysis

Use AI to generate comprehensive EDA reports with minimal code.

# Using DataExplorer package
library(DataExplorer)

# Generate comprehensive report
create_report(iris)

# Alternatively, using AI-based summaries
# Function to generate AI insights from basic EDA
ai_data_insights <- function(data, description = NULL) {
  # Generate basic summaries
  summary_stats <- summary(data)
  correlations <- cor(data[sapply(data, is.numeric)], use = "complete.obs")

  # Create a text prompt for the AI
  prompt <- paste0(
    "Based on this dataset",
    if (!is.null(description)) paste0(" about ", description) else "",
    ", here are the summary statistics:\n\n",
    capture.output(summary_stats),
    "\n\nAnd correlation matrix:\n\n",
    capture.output(correlations),
    "\n\nProvide 3-5 key insights about this data and suggest potential analyses."
  )

  # Call your preferred AI service here
  # (use one of the methods from the AI Tools page)
  return(prompt) # Replace with actual AI call
}

Automated Feature Engineering

Enhance model performance with AI-suggested feature transformations.

# Using recipes package with AI enhancement
library(recipes)
library(tidymodels)

# Standard feature engineering
rec <- recipe(target ~ ., data = training_data) %>%
  step_normalize(all_numeric_predictors()) %>%
  step_dummy(all_nominal_predictors())

# AI-enhanced features would extend this approach
# by suggesting optimal transformations based on data patterns

Hybrid Models: Statistical + Machine Learning

Combine traditional statistical methods with ML for interpretable yet powerful models.

# Example: Augmented regression approach
library(tidymodels)
library(mgcv)

# Fit a GAM model
gam_fit <- gam(y ~ s(x1) + s(x2) + x3, data = training_data)

# Use predictions as features in a second-level model
training_data$gam_pred <- predict(gam_fit)

# Add ML model that can capture additional patterns
final_model <- rand_forest(trees = 500) %>%
  set_engine("ranger") %>%
  set_mode("regression") %>%
  fit(y ~ . + gam_pred, data = training_data)

Coming Soon

• Automated statistical reporting
• AI-assisted hypothesis testing
• Intelligent data preprocessing pipelines