brooksandrew

#' @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.

brooksandrew

import java.io.File
import javax.imageio.ImageIO
import java.awt.Color

object makegray extends App {

   def pixels2gray(red: Int, green: Int, blue: Int): Int = (red + green + blue) / 3
  
   def makeGray(img: java.awt.image.BufferedImage): java.awt.image.BufferedImage = {
             val w = img.getWidth

brooksandrew

import org.imgscalr._
import java.io.File
import javax.imageio.ImageIO

object resize extends App {
   
  def resizeImg(img: java.awt.image.BufferedImage, width: Int, height: Int) = {
    Scalr.resize(img, Scalr.Method.BALANCED, width, height)
  }
  

brooksandrew

import org.imgscalr._
import java.io.File
import javax.imageio.ImageIO

object makeSqaure extends App {
  
  def makeSquare(img: java.awt.image.BufferedImage): java.awt.image.BufferedImage = {
      val w = img.getWidth
      val h = img.getHeight
      val dim = List(w, h).min

brooksandrew

555,666

brooksandrew

{
  "tcount": "123,456",
  "datime": "July 31st 2016"
}

brooksandrew

brooksandrew

brooksandrew

brooksandrew