Dear list,
I have a data.frame with segments between river junctions and
dimensionless predictions of runoff (runoff/area) at some of these
junctions. As I want to plot my values on a continuous river network
(this data.frame is part of a SpatialLinesDataFrame), I would like to
change NA values to the closest non-NA value downstream.
Here is a simple example:
> examp = data.frame(FROMJCT = c(1,2,3,4,5,7,8,9,10,11,12,13,14),TOJCT
= c(2,3,4,5,6,4,7,8,8,10,8,12,9))
> examp$pred = NA
> examp$pred[c(2,4,5,7,13)] = c(1,2,3,4,5)
> examp
FROMJCT TOJCT pred
1 1 2 NA
2 2 3 1
3 3 4 NA
4 4 5 2
5 5 6 3
6 7 4 NA
7 8 7 4
8 9 8 NA
9 10 8 NA
10 11 10 NA
11 12 8 NA
12 13 12 NA
13 14 9 5
"FROMJCT" describes the upstream and "TOJCT" the downstream junction.
examp$pred[7] above should hence get the value 3, as its "TOJCT"
junction is the same as the "FROMJCT" junction of examp$pred[6].
examp$pred[8] should get the same value, as it is linked to
examp$pred[6] through examp$pred[7]. I can do this iteratively by
propagating values upwards in the river network by combining a while and
a for-loop:
ichange = 1
while (ichange > 0) {
ichange = 0
for (i in 1:dim(examp)[1]) {
if (!is.na(examp$pred[i])) {
toid = which(examp$TOJCT == examp$FROMJCT[i])
if (length(toid) > 0 && is.na(examp$pred[toid])) {
examp$pred[toid] = examp$pred[i]
ichange = ichange + 1
}
}
}
print(ichange)
}
But this looks messy and is rather slow when the river network is
described through a large number of segments. I am quite sure that I
have missed a better way of propagating the values. This is a
preprocessing step before plotting a result in a documentation example,
so I am looking for a short, intuitive and nice solution... Any hints?
Thanks,
Jon
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