just a couple of hints as you are learning the language

I do very little with apply.  If necessary to process a matrix in row form, as this seems to be doing, I would define a function that did the row transformation, then apply it once.

sort takes the very useful additional parameter index=T, which will return the sorted order, obviating the need to do separate ranking.

For really sophisticated searching of sorted things, there is findInterval, which does binary search. It took me a while to find it in R. But it's incredibly nice. Here's an example of using sort (with i=T), and findInterval together.

# si.sample() :
# put a arbitrarily sampled sequence of events into a regularly sampled line,
# sampled at regular si intervals.
# IN: assumes you have just events-points in tm1 and event values (reals) in x1.
#
si.sample=function(x1,tm1, si=1, t0=floor(min(tm1)), tend=ceiling(max(tm1)), fill.na=T) {

  if (length(tm1) != length(x1)) stop("x1 and tm1 must be the same length!")

    # insure we are sorted by tm1
    s=sort(tm1,i=T)
    tm1=s$x
    x1=x1[s$ix]

    r=list()
    N = ceiling((tend - t0)/si) +1
    tm.new = t0 + (0:(N-1))*si
    a=rep(NA,N)

    # binary search using findInterval which defaults to half-open on the right [a,b) intervals... but since we want
    # half-open on the left (a,b] intervals, we need to:
    # negate time, apply findInterval, un-negate time to get correct behavior at the interval boundaries.

    fi=findInterval(sort(-tm1), sort(-tm.new),rightmost.closed=T) # negate time and apply findInterval
    fi2=1+length(tm.new)-rev(fi) # un-negating the time flip, to get correct boundary behavior.

    fi2[fi2>(length(tm.new))]=1 # assign any time points past tend values back to bucket 1 to get NA-ed as well during a[1]=NA
    a[fi2]=x1 # correctly overwritting with later values if multiple values per interval.
    a[1]=NA   # bucket that collects pre- t0 samples at t0 itself in tm.new; also gets samples post tend.

    if (fill.na) {
      r$v = fill.na.with.most.recently.seen(a)
    } else {
      r$v = a
    }
    r$tm = tm.new

    class(r)="regular.sample"
    r
}

# and it uses this
fill.na.with.most.recently.seen=function(x) {
  lastseen = x[1]
  for (i in 2:length(x)) {
    if (is.na(x[i])) {
      x[i]=lastseen
    } else {
      lastseen = x[i]
    }
  }
  x
}