[R] inefficient for loop, is there a better way?

[Morway, Eric]

The code below is a small reproducible example of a much larger problem.
While the script below works, it is really slow on the true dataset with
many more rows and columns.  I'm hoping to get the same result to examp,
but with significant time savings.

The example below is setting up a data.frame for an ensuing regression
analysis.  The purpose of the script below is to appends columns to 'examp'
that contain values corresponding to the total number of days in the
previous 7 ('per') above some stage ('elev1' or 'elev2').  Is there a
faster method that leverages existing R functionality?  I feel like the
hack below is pretty clunky and can be sped up on the true dataset.  I
would like to run a more efficient script many times adjusting the value of
'per'.

ts <- 1:1000
examp <- data.frame(ts=ts, stage=sin(ts))

hi1 <- list()
hi2 <- list()
per <- 7
elev1 <- 0.6
elev2 <- 0.85
for(i in per:nrow(examp)){
    examp_per <- examp[seq(i - (per - 1), i, by=1),]
    stg_hi_cond1 <- subset(examp_per, examp_per$stage > elev1)
    stg_hi_cond2 <- subset(examp_per, examp_per$stage > elev2)

    hi1 <- c(hi1, nrow(stg_hi_cond1))
    hi2 <- c(hi2, nrow(stg_hi_cond2))
}
examp$days_abv_0.6_in_last_7   <- c(rep(NA, times=per-1), unlist(hi1))
examp$days_abv_0.85_in_last_7  <- c(rep(NA, times=per-1), unlist(hi2))

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