P <- read.table('house_polls.dat')
R <- read.table('house_results.dat')
P
R

T <- R[,2] + R[,3]
R[,2] <- 100 * R[,2] / T
R[,3] <- 100 * R[,3] / T

R <- cbind(R, R[,2]-R[,3])

D <- unique(as.vector(P[,2]))
D
length(D)

i2 <- which(R[,1] == "NC-08")
R[i2,]

as.vector(R[R[,4] <0,1])

SD <- NULL
## Loop through the races.
for (d in D)
{
    ## Get the polls for race d.
    i1 <- which(P[,2] == d)

    ## Get the recent polls (roughly three weeks back).
    i1 <- i1[P[i1,1] > 290]
    if (length(i1) == 0) { next }

    ## The standard deviation for the poll average difference.
    sdev <- 7/(1.96*sqrt(length(i1)))

    SD <- c(SD, sdev)
}

SD <- NULL
## Loop through the races.
for (d in D)
{
    ## Get the polls for race d.
    i1 <- which(P[,2] == d)

    ## Get the recent polls (roughly three weeks back).
    i1 <- i1[P[i1,1] > 290]
    if (length(i1) == 0) { next }

    ## Take the first poll only 
    i1 <- i1[1]
    ## The standard deviation for the poll average difference.
    sdev <- 7/(1.96*sqrt(length(i1)))

    SD <- c(SD, sdev)
}

k <- 1
n <- 10
theta <- 0.5
xytable <- array( 0, c(n,2) )

while(1)
{
  includeMe <- TRUE
  X <- rnorm(1)
  Y <- rnorm(1)
  
  if ( X > 1 && 1*(runif(1) < theta) )
  {
    includeMe <- FALSE
  }
  
  if ( includeMe )
  {
    xytable[k,1] <- X
    xytable[k,2] <- Y
    k <- k+1
  }
  
  if( k > n)
  {
    break
  }
}
xytable