couthcommander

# one connect/rcon *only*
exportForSas <- function(rcon, directory = NULL, ...) {
  if(is.null(directory)) {
    directory <- getwd()
  }
  curtime <- format(Sys.time(), '%Y%m%d%H%M%S')
  cb <- assembleCodebook(rcon, expand_check = TRUE)
  # used for looking up field_label
  tmpcb <- unique(cb[,c('field_name','field_label','form_name')])

couthcommander

library(Hmisc)
options(digits=3)
set.seed(173)
sex <- factor(sample(c("m","f"), 500, rep=TRUE))
age <- rnorm(500, 50, 5)
treatment <- factor(sample(c("Drug","Placebo"), 500, rep=TRUE))
symp <- c('Headache','Stomach Ache','Hangnail','Muscle Ache','Depressed')
symptom1 <- sample(symp, 500,TRUE)
symptom2 <- sample(symp, 500,TRUE)
symptom3 <- sample(symp, 500,TRUE)

couthcommander

x <- matrix(NA, 200, 20)
pmean <- c(20, 10, 8, 5, 8, 6)
psd <- c(5, 2, 2, 2, 2, 3)
for(i in seq(nrow(x))) {
    pos <- (i - 1) %% 6 + 1
    x[i,1] <- i
    x[i,2] <- pos
    x[i,3] <- ifelse(i <= 100, (i - 1) %/% 10 + 1, 0)
    x[i,4:20] <- sample(c(0, rnorm(16, pmean[pos], psd[pos])))
}

couthcommander

library(XML)

fetchData <- function(id) {
    url <- sprintf("http://www.espn.com/ncf/teams/roster?teamId=%s", id)
    x <- htmlTreeParse(url, useInternalNodes=TRUE)
    y <- xpathSApply(x, "//td", xmlValue)
    z <- data.frame(matrix(y[-c(1:8)], ncol=7, byrow=TRUE), stringsAsFactors=FALSE)
    names(z) <- y[2:8]
    z[,'TM'] <- y[1]
    z

couthcommander

     N     meanH    medH  meanP  medP pastN pastmeanH pastmedH pastmeanP pastmedP         dHit         dPit
NYN 84  92.83333  97.0 4.008214 4.030    78  89.39744       90  4.096154    4.170 -3.435897436 -0.087939560
WAS 82  91.23171  92.0 4.141098 4.180    80  91.22500       92  4.068250    4.160 -0.006707317  0.072847561
MIA 83  89.72289  92.0 4.030602 4.030    79  92.39241       97  4.003038    4.030  2.669513497  0.027564435
CHN 82  95.64634  93.0 4.274390 4.305    80  92.87500       97  4.207500    4.275 -2.771341463  0.066890244
MIL 83  98.83133 104.0 4.252651 4.290    79  93.12658       92  4.059367    4.030 -5.704743023  0.193283514
SFN 81  94.76543  97.0 4.034198 4.180    81  93.41975       92  4.130370    4.180 -1.345679012 -0.096172840
PHI 82  92.04878  95.0 4.050244 4.160    80  94.96250       98  4.078375    4.160  2.913719512 -0.028131098
LAN 81  96.72840  98.0 4.135432 4.180    81  95.18519       97  4.166914    4.180 -1.543209877 -0.031481481
SLN 84  93.10714  91.5 4.242024 4.260    78  95.4359

couthcommander

nprimes <- function(n) {
  res <- numeric(n)
  cnt <- 1
  x <- 2
  while(cnt <= n) {
    good <- TRUE
    for(i in seq(2, sqrt(x))) {
      if(x != i && x %% i == 0) {
        good <- FALSE
        break

couthcommander

library(shiny)

shinyServer(function(input, output) {
  output$vorpPlot <- reactivePlot(function() {
    id <- input$obs
    nsim <- 1000
    gender <- if(id == 51) 1
    else if(id == 52) 0
    else stats[id, 'gender']
    spots <- seq(4+gender*2)