% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/workflows.R
\name{tl_explore}
\alias{tl_explore}
\title{Exploratory Data Analysis Workflow}
\usage{
tl_explore(data, response = NULL, max_components = 5, k_range = 2:6)
}
\arguments{
\item{data}{A data frame}

\item{response}{Optional response variable for colored visualizations}

\item{max_components}{Maximum PCA components to compute (default: 5)}

\item{k_range}{Range of k values for clustering (default: 2:6)}
}
\value{
A list with class \code{"tidylearn_eda"} containing:
\describe{
\item{data}{The original data frame.}
\item{response}{The response variable name, or \code{NULL}.}
\item{pca}{The fitted PCA model.}
\item{optimal_k}{List with optimal cluster count results.}
\item{kmeans}{The fitted k-means model.}
\item{hclust}{The fitted hierarchical clustering model.}
\item{summary}{List with \code{n_obs}, \code{n_vars},
\code{n_components}, and \code{best_k}.}
}
}
\description{
Comprehensive EDA combining unsupervised learning techniques
to understand data structure before modeling
}
\examples{
\donttest{
eda <- tl_explore(iris, response = "Species")
plot(eda)
}
}
