Andrew Brooks brooksandrew

Also known as @andrewbrooks-o at Outreach.io

brooksandrew / jsonex.json

Last active August 1, 2016 00:37

brooksandrew / samplenumber.html

Created July 31, 2016 21:21

555,666

brooksandrew / makeSquare.scala

Created April 12, 2016 19:26

Square rectangular images and center as much as possible using ImageIO and imgscalr in Scala. Save output as new jpg.

	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 / resize.scala

Last active April 12, 2016 19:31

Re-size images using ImageIO and imgscalr in Scala and save output as a new jpgs

	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 / makeGray.scala

Created April 12, 2016 16:52

Example of color to grayscale conversion with ImageIO in Scala.

	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 / shiny_arules.R

Last active May 19, 2022 08:14

Launches a Shiny App that provides an interactive interface to the arules and arulesViz package which train and visualize association rules

	#' @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.