R/Protein_Dataset.R

In TFM: Sparse Online Principal Component for Truncated Factor Model

#' Protein Structural and Feature Dataset  
#'  
#' A dataset containing **RMSD** (structural deviation) and nine numerical features (F1-F9)  
#' for analyzing protein conformations, interactions, or functional properties.  
#'  
#' @format A data frame with `n` observations (rows) and 10 variables (columns):  
#' \describe{  
#'   \item{\code{RMSD}}{Root Mean Square Deviation (RMSD) - measures structural deviation from a reference protein conformation.  
#'   Units are typically in \strong{angstroms (\\eqn{\\AA})} for protein structure analysis.}  
#'   \item{\code{F1}}{Feature 1 - e.g., intermolecular interaction energy, hydrophobicity score, or solvent-accessible surface area (SASA).  
#'   (Replace with actual biological/chemical interpretation.)}  
#'   \item{\code{F2}}{Feature 2 - e.g., hydrogen bond count, secondary structure stability, or residue-level charge.  
#'   (Replace with actual biological/chemical interpretation.)}  
#'   \item{\code{F3}}{Feature 3 - [Customize based on data context]}  
#'   \item{\code{F4}}{Feature 4 - [Customize based on data context]}  
#'   \item{\code{F5}}{Feature 5 - [Customize based on data context]}  
#'   \item{\code{F6}}{Feature 6 - [Customize based on data context]}  
#'   \item{\code{F7}}{Feature 7 - [Customize based on data context]}  
#'   \item{\code{F8}}{Feature 8 - [Customize based on data context]}  
#'   \item{\code{F9}}{Feature 9 - [Customize based on data context]}  
#' }  
#'  
#' @examples  
#' # Load the dataset
#' data(protein_features)
#' 
#' # Basic summary
#' summary(protein_features)
#'   
#' # Plot RMSD vs Feature 1
#' plot(
#'   x = protein_features$RMSD,
#'   y = protein_features$F1,
#'   xlab = "RMSD (\\eqn{\\AA})",
#'   ylab = "Feature 1 Value",
#'   main = "RMSD vs Feature 1"
#' )
#' 
#' # Advanced visualization (if tidyverse is available)
#' if (requireNamespace("tidyverse", quietly = TRUE)) {
#'   protein_features %>%
#'     tidyr::pivot_longer(
#'       cols = -RMSD,
#'       names_to = "Feature",
#'       values_to = "Value"
#'     ) %>%
#'     ggplot2::ggplot(ggplot2::aes(x = Feature, y = Value)) +
#'     ggplot2::geom_boxplot(fill = "skyblue") +
#'     ggplot2::labs(
#'       title = "Distribution of F1-F9 Features",
#'       x = "Feature",
#'       y = "Value"
#'     )
#' }  
#' ''protein''