try this:
> x <- read.table(text = " tree live1 live2 live3 live4 live5
+ 1 tree1 0 0 0 1 1
+ 2 tree2 0 0 1 1 0
+ 3 tree3 0 1 1 0 0
+ 4 tree4 1 1 0 0 0
+ 6 tree4 1 1 1 1 0 # another test condition
+ 5 tree5 1 0 0 0 0", header = TRUE)
>
> # get matrix of data columns
> z <- as.matrix(x[, -1])
> # process each row
> a <- apply(z, 1, function(.row){
+ # determine where found (will be a 2)
+ found <- pmin(cumsum(.row) + 1, 3) # cannot be greater than 3
+ # determined where it died
+ die <- cumsum(diff(c(0, .row)) != 0)
+ # replace value at die == 2 with 4
+ found[die == 2] <- 4
+ c(NA, "found", "alive", "mort")[found]
+ })
> t(a) # result
[,1] [,2] [,3] [,4] [,5]
1 NA NA NA "found" "alive"
2 NA NA "found" "alive" "mort"
3 NA "found" "alive" "mort" "mort"
4 "found" "alive" "mort" "mort" "mort"
6 "found" "alive" "alive" "alive" "mort"
5 "found" "mort" "mort" "mort" "mort"
>
On Fri, Apr 13, 2012 at 4:53 PM, Alexander Shenkin <ashen...@ufl.edu> wrote:
> Hello Folks,
>
> I have 5 columns for thousands of tree records that record whether that
> tree was alive or dead. I want to recode the columns such that the cell
> reads "found" when a live tree is first observed, "alive" for when a
> tree is found alive and is not just found, and "mort" when it was
> previously alive but is now dead.
>
> Given the following:
>
> > tree_live = data.frame(tree =
> c("tree1","tree2","tree3","tree4","tree5"), live1 = c(0,0,0,1,1), live2
> = c(0,0,1,1,0), live3 = c(0,1,1,0,0), live4 = c(1,1,0,0,0), live5 = c(1,
> 0, 0, 0, 0))
>
> tree live1 live2 live3 live4 live5
> 1 tree1 0 0 0 1 1
> 2 tree2 0 0 1 1 0
> 3 tree3 0 1 1 0 0
> 4 tree4 1 1 0 0 0
> 5 tree5 1 0 0 0 0
>
> I would like to end up with the following:
>
> > tree_live_recode
>
> live1 live2 live3 live4 live5
> 1 NA NA NA found alive
> 2 NA NA found alive mort
> 3 NA found alive mort 0
> 4 found alive mort 0 0
> 5 found mort 0 0 0
>
> I've accomplished the recode in the past, but only by going over the
> dataset multiple times in messy and inefficient fashion. I'm wondering
> if there are concise and efficient ways of going about it?
>
> (I haven't been using the Survival package for my analyses, but I'm
> starting to look into it.)
>
> Thanks,
> Allie
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
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