Combine rows into one, replace NA - r

Question

I have the following dataframe: (this is just a small sample)
VALUE COUNT AREA n_dd-2000 n_dd-2001 n_dd-2002 n_dd-2003 n_dd-2004 n_dd-2005 n_dd-2006 n_dd-2007 n_dd-2008 n_dd-2009 n_dd-2010
2 16 2431 243100 NA NA NA NA NA NA 3.402293 3.606941 4.000461 3.666381 3.499614
3 16 2610 261000 3.805082 4.013435 3.98 3.490139 3.433857 3.27813 NA NA NA NA NA
4 16 35419 3541900 NA NA NA NA NA NA NA NA NA NA NA
and I would like to combine all three rows into one row replacing NA with the number that appears in each column (there's only one number per column). Just ignore the first three columns. I used this code:
bdep[4,4:9] <- bdep[3,4:9]
to replace NA's with numbers from another row, but can't figure out how to repeat it for all the columns. The columns 4 and beyond have a sequence in each row of six numbers followed by 20 NA's, so I've tried going down the road of using lapply() and seq() or for loops, but my efforts are failing.

Answer 1

I did a simple solution by replacing the NA:s with zeroes and adding all rows per column. Did this work?
#data
bdep <- rbind(c(rep(NA,6),3.402293,3.606941,4.000461,3.666381,3.499614),
c(3.805082,4.013435,3.98,3.490139,3.433857,3.27813, rep(NA,5)),
c(rep(NA,11)))
#solution
bdep2 <- ifelse(is.na(bdep), 0, bdep)
bdep3 <- apply(bdep2, 2, sum)
bdep3 #the row you want?

Answer 2

I finally came to a solution by patching together some code I found in other posts (esp. sequencing and for loops). I think this would be considered messy coding, so I'd welcome other solutions. This should better describe what I was trying to do in the OP, where I was trying to generalize too much. Specifically, I have 17 variables, measured over 14 years (that's 238 columns), and something happened while generating these data where the first 6 years of a variable are in one row and the following 8 years are in the other row, so instead of re-run the model, I just wanted to combine the two rows into one.
Below are some sample data, simplified from my real scenario.
Create the data frame:
df <- data.frame(
VALUE = c(16, 16, 16),
COUNT = c(2431, 2610, 35419),
AREA = c(243100, 261000, 3541900),
n_dd_2000 = c(NA, 3.805, NA),
n_dd_2001 = c(3.402, NA, NA)
)
The next two lines establish a sequence starting a pattern at column 4, repeating every 1 column, repeated 2 times out in the first line, 1 time out in the second line, and how many times to repeat the sequence:
info <- data.frame(start=seq(4, by=1, length.out=2), len=rep(1,2))
info2 <- data.frame(start=seq(5, by=1, length.out=1), len=rep(1,2))
This is the code from my real dataset, where I started at column 4, repeated the pattern every 14 columns, out 17 times, and looked at the first 6, then 8 columns: info <- data.frame(start=seq(4, by=14, length.out=17), len=rep(c(6,8),17))
The two for loops below write the specified values in the sequence from row 2 and row 1 to row 3, respectively:
foo = sequence(info$len) + rep(info$start-1, info$len)
foo2 = sequence(info2$len) + rep(info2$start-1, info2$len)
for(n in 1:length(foo)){
df[3,foo[n]] <- df[2,foo[n]]
}
for(n in 1:length(foo2)){
df[3,foo2[n]] <- df[1,foo2[n]]
}
Then I removed the first two rows I got those values from and I'm left with one complete row, no NA's:
df <- df[-(1:2),]