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High Definition Binning	{#HD_binning}
=====================


The process of binning (or discretization) of variables is a well-established practice in building credit scorecards. The binning process involves taking raw values e.g. income and cutting that data into bins (discrete ranges) such as 2000-3000, 3000-4000. Typically we would see an upward trend in terms of Good/Bad Odds as the income levels go up.

In this blog post I would like to explain a novel approach to binning that can produce very fine binning.

Automatic Binary Binning Algorithm (ABBA)
---------

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Top 5 Underdog Stories

1. Zhou Jun Xun wins LG cup
No one from outside Japan, South Korea, China has ever won an international title until Zhou Jun Xun from Taiwan. He was the first 9 dan from Taiwan and he has an unmistakable large red birth mark on his face. On his way to LG cup victory he defeated Lee Chang Ho and Lee Sedol. His most famous win was against Lee Sedol where he played a new move in the avanlanche 

2. Ear-reddening
Shuwa was the up start who nobody expected could challenge the pre-Meijin

3. Go Seigen vs Shuko
When Go Seigen won the competition to ear the right to challenge Shuko. No one expected Go to win or perhaps come close to winning. Shuko played white and since there is no komi at the time, the only adavantage he had was that he could adjourn the game at any time. Go lost the game by 2 points.

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shiny::runGitHub("shinyhandsontable-example","xiaodaigh")

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d <- parse("server stripped bare.R")
dc <- as.character(d)

reactives <- NULL
# find reactive sources
for(t in dc) {
	if( grepl("reactive(.*)$",t)) { #if it is reactive
		g <- gregexpr("^[A-Za-z0-9._]*",t)
		p <- as.numeric(g[[1]])
		l <- attr(g[[1]],"match.length")

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item1,item2,item3,item4,item5,item6,item7,item8,item9,item10
1,1,,,1,1,,,,
,,1,1,,,1,1,,
1,,,,1,,,,1,1
,1,,1,1,,1,,1,
1,1,1,1,1,1,,,,1
,1,,,,,1,,,
,,1,1,1,1,,1,,
1,,,1,,,,1,,1
,,1,1,1,,,,1,1

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library(shiny)
#tu<-read.csv(file="tu.csv", header = TRUE, sep = ",")
#attach(tu)
tu <- data.frame(id=rnorm(1000),size=rnorm(1000),months=rnorm(1000))

shinyServer(function(input, output) {
  it<- reactive({
		print(input$tu)
		switch(input$tu,
			   "id" = tu$id,

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$(document).delegate("#mytable",'DOMSubtreeModified','DOMNodeInserted', function(event) {                                           
                    color()
                  });


function color(){
  $('#mytable td').each(function(){
    $(this).css({'background':'#CCC'});
  }) 
}

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library(shiny)
data(pressure)
data(cars)

shinyServer(function(input, output, session) {
  
  # Partial example
  observe({
    

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To run

shiny::runGist("6684134")

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#
#
cor.ilic <- function(x,y) {
#   tryCatch(t <- table(x,y,exclude=NULL,useNA="ifany")
# 			 , error = function(e) {
# 			 	
# 			 }
# 			 )
	bt <- table.p(data.frame(x=x,y=y),32)