semenoffalex

## shiny_arules.R
#' @title Assocation Rules Visualization Shiny App
#' @description Launches a Shiny App that provides an interactive interface to the visualizations of the \code{arulesViz} package.
#' The app allows users to mine rules based on all or just subsets of features, sort by criteria (lift, support, confidence) and visualize
#' using network graph, grouped bubble and scatter plots. \cr
#' Users filter rules to target only those with a certain variable on the RHS or LHS of the rule.
#' Rule mining is computed using the \link{apriori} algorithm from \code{arules}.
#'
#' @param dataset data.frame, this is the dataset that association rules will be mined from.  Each row is treated as a transaction.  Seems to work
#' OK when a the S4 transactions class from \code{arules} is used, however this is not thoroughly tested.
#' @param bin logical, \code{TRUE} will automatically discretize/bin numerical data into categorical features that can be used for association analysis.
	#' @title Assocation Rules Visualization Shiny App
	#' @description Launches a Shiny App that provides an interactive interface to the visualizations of the \code{arulesViz} package.
	#' The app allows users to mine rules based on all or just subsets of features, sort by criteria (lift, support, confidence) and visualize
	#' using network graph, grouped bubble and scatter plots. \cr
	#' Users filter rules to target only those with a certain variable on the RHS or LHS of the rule.
	#' Rule mining is computed using the \link{apriori} algorithm from \code{arules}.
	#'
	#' @param dataset data.frame, this is the dataset that association rules will be mined from. Each row is treated as a transaction. Seems to work
	#' OK when a the S4 transactions class from \code{arules} is used, however this is not thoroughly tested.
	#' @param bin logical, \code{TRUE} will automatically discretize/bin numerical data into categorical features that can be used for association analysis.