bhoung

  # This second example runs Trueskill on a tennis tournament, the Australian Open.
  # Note that actual computation is commented out as it takes about ~40 seconds to 
  # update skill ratings over 127 matches.
  
  library(trueskill) 

  # Data format of ausopen2012 is: Player, Opponent, Margin, Round, WRank, LRank
  data("ausopen2012")
  
  # create match_id in order to reshape

bhoung

---
title: Minutes played by Nets, playoffs 
layout: post
categories:
  - posts 
tags:
  - nba, sports
output: 
   #html_document
  md_document:

bhoung

---
title: "Basketball Reference"
layout: post
categories:
  - notebook
tags:
  - nba, sports
output: 
   html_document
#  md_document:

bhoung

---
title: "'21 Brooklyn Nets performance in games missed by stars"
layout: post
categories:
  - notebook
tags:
  - nba, sports
output: 
   html_document
#  md_document:

bhoung

-- https://www.slideshare.net/redongjun/simple-json-parser
-- https://www.youtube.com/watch?v=r_Enynu_TV0


import Text.ParserCombinators.Parsec
import Control.Monad

matchTrue :: Parser String
matchTrue = string "true"

bhoung

# original example from Digg Data website (Takashi J. OZAKI, Ph. D.) 
# http://diggdata.in/post/58333540883/k-fold-cross-validation-in-r


library(plyr)
library(randomForest)

data <- iris

# in this cross validation example, we use the iris data set to 

bhoung

setwd("C:/Documents and Settings/bhoung/My Documents/time/")
require("ggplot2")
require("maptools")
gpclibPermit()

# read in spatial data - from the ABS (Australian Burea of Statistics) website

shp <- readShapeSpatial( fn = "C:/Documents and Settings/bhoung/My Documents/asgc/SLA11aAust", verbose = TRUE, delete_null_obj=TRUE)
summary(shp)
list_names = names(shp)

bhoung

require(ggmap)
require(foreign)

melb <- read.dta("/Users/brendanhoung/example/melb.dta")

melbourne <- qmap(location='melbourne, Australia', zoom = 10) + theme_set(theme_bw())
melbourne_stamen <- qmap(location='melbourne, Australia', zoom = 10, source="stamen", maptype="watercolor")
melbourne_toner <- qmap(location='melbourne, Australia', zoom = 10, source="stamen", maptype="toner")
melbourne_osm <- qmap(location='melbourne, Australia', zoom = 10, source="osm")

bhoung

SGPstateData[["NAPLAN"]][["Growth"]][["System_Type"]] <- "Cohort Referenced"
SGPstateData[["NAPLAN"]][["Student_Report_Information"]][["Grades_Reported"]][["NUMERACY"]] <- c(3, 5, 7)
SGPstateData[["NAPLAN"]][["Assessment_Program_Information"]][["Grades_Tested"]] <- c(3, 5, 7)
SGPstateData$NAPLAN$Growth$Levels <- c("Very Low", "Low", "Typical", "High", "Very High")
SGPstateData[["NAPLAN"]][["Growth"]][["Cutscores"]] <- 
    list(Cuts=c(20, 40, 60, 80), 
       Labels=list("1st - 19th", "20th - 39th", "40th - 59th", "60th-79th", "80th-99th"))

#NUM YR3 2008 
s1 <- read.dta("C:/Protected/MI/Naplan/sgp/long/YR3_2008.dta")