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# For each day, find the appropriate Poisson Parameter and use it to calculate the likelihood of the actual view count
# Likelihood = P(D|H)

Calc.Likelihood <- function(changes, poissons, stats, periods) {
  likelihood <- 0
  for (day in 1:nrow(stats)) {
    proceed <- FALSE
    period <- 1
    while (!proceed) {
      if (day < changes[period]) {

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library(httr)
library(rjson)

key <- '<edited>'
secret <- '<edited>'
tokenURL <- 'http://api.fitbit.com/oauth/request_token'
accessTokenURL <- 'http://api.fitbit.com/oauth/access_token'
authorizeURL <- 'http://www.fitbit.com/oauth/authorize'

fbr <- oauth_app('MyHome', key, secret)

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library(rjson)
library(reshape)

user <- 'TheLaughFactory'

videos <- data.frame(matrix(data = 0, nrow = 1000, ncol = 8))
colnames(videos) <- c('id', 'link', 'title', 'date', 'duration', 'view.count', 'avg.rating', 'num.raters')

found.all <- FALSE
index <- 1

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# Quantifies homecourt advantage in each regular season

library(RCurl)
library(XML)

# Get Basketball Reference regular season schedule with home margin of victory for each game
GetSchedule <- function(year) {
  
  url <- paste("http://www.basketball-reference.com/leagues/NBA_", year, "_games.html", sep = "")
  tables <- readHTMLTable(url)

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library(ggplot2)
library(moments)
library(sportsTools)
library(dplyr)

clean.lines <- function(data) {
  data <- data[-which(is.na(data$home.line)), ]
  data$expected <- -data$home.line
  data$actual <- data$home.score - data$away.score
  data$residual <- data$actual - data$expected

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library(sportsTools)
library(ggplot2)
library(grid)

# All years since NBA-ABA Merger
years <- 1977:2016

# Create empty data frame to hold results from each year
df <- data.frame(matrix(nrow = length(years), ncol = 4))
colnames(df) <- c('year', 'mean', 'sd', 'n')

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options(stringsAsFactors = FALSE)

ScrapeLSN <- function(school, cycle) {
  
  base.url <- 'http://SCHOOL.lawschoolnumbers.com/stats/CYCLE'
  
  url <- gsub('SCHOOL', school, base.url)
  url <- gsub('CYCLE', cycle, url)
  
  src <- readLines(url)

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library(sportsTools)
library(dplyr)
library(ggplot2)

start.year <- 2001
end.year <- 2015

all.games <- data.frame()

for (year in start.year:end.year) {

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