Calculating tree growth using for loop - r

Question

I am trying to calculate diameter growth for a set of trees over a number of years in a dataframe in which each row is a given tree during a given year. Typically, this sort of data has each individual stem as a single row with that stem's diameter for each year given in a separate column, but for various reasons, this dataframe needs to remain such that each row is an individual stem in an individual year. A simplistic model version of the data would be as follows
df<-data.frame("Stem"=c(1:5,1:5,1,2,3,5,1,2,3,5,6),
"Year"=c(rep(1997,5), rep(1998,5), rep(1999,4), rep(2000,5)),
"Diameter"=c(1:5,seq(1.5,5.5,1),2,3,4,6,3,5,7,9,15))
df
Stem Year DAP
1 1 1997 1.0
2 2 1997 2.0
3 3 1997 3.0
4 4 1997 4.0
5 5 1997 5.0
6 1 1998 1.5
7 2 1998 2.5
8 3 1998 3.5
9 4 1998 4.5
10 5 1998 5.5
11 1 1999 2.0
12 2 1999 3.0
13 3 1999 4.0
14 5 1999 6.0
15 1 2000 3.0
16 2 2000 5.0
17 3 2000 7.0
18 5 2000 9.0
19 6 2000 15.0
What I am trying to accomplish is to make a new column that takes the diameter for a given stem in a given year and subtracts the diameter for that same stem in the previous year. I assume that this will require some set of nested for loops. Something like
for (i in 1:length(unique(df$Stem_ID){
for (t in 2:length(unique(df$Year){
.....
}
}
What I'm struggling with is how to write the function that calculates:
Diameter[t]-Diameter[t-1] for each stem. Any suggestions would be greatly appreciated.

Answer 1

Try:
> do.call(rbind, lapply(split(df, df$Stem), function(x) transform(x, diff = c(0,diff(x$Diameter)))))
Stem Year Diameter diff
1.1 1 1997 1.0 0.0
1.6 1 1998 1.5 0.5
1.11 1 1999 2.0 0.5
1.15 1 2000 3.0 1.0
2.2 2 1997 2.0 0.0
2.7 2 1998 2.5 0.5
2.12 2 1999 3.0 0.5
2.16 2 2000 5.0 2.0
3.3 3 1997 3.0 0.0
3.8 3 1998 3.5 0.5
3.13 3 1999 4.0 0.5
3.17 3 2000 7.0 3.0
4.4 4 1997 4.0 0.0
4.9 4 1998 4.5 0.5
5.5 5 1997 5.0 0.0
5.10 5 1998 5.5 0.5
5.14 5 1999 6.0 0.5
5.18 5 2000 9.0 3.0
6 6 2000 15.0 0.0

Answer 2

Rnso's answer works. You could also do the slightly shorter:
>df[order(df$Stem),]
>df$diff <- unlist(tapply(df$Diameter,df$Stem, function(x) c(NA,diff(x))))
Stem Year Diameter diff
1 1 1997 1.0 NA
6 1 1998 1.5 0.5
11 1 1999 2.0 0.5
15 1 2000 3.0 1.0
2 2 1997 2.0 NA
7 2 1998 2.5 0.5
12 2 1999 3.0 0.5
16 2 2000 5.0 2.0
3 3 1997 3.0 NA
8 3 1998 3.5 0.5
13 3 1999 4.0 0.5
17 3 2000 7.0 3.0
4 4 1997 4.0 NA
9 4 1998 4.5 0.5
5 5 1997 5.0 NA
10 5 1998 5.5 0.5
14 5 1999 6.0 0.5
18 5 2000 9.0 3.0
19 6 2000 15.0 NA
Or if you're willing to use the data.table package you can be very succinct:
>require(data.table)
>DT <- data.table(df)
>setkey(DT,Stem)
>DT <- DT[,diff:= c(NA, diff(Diameter)), by = Stem]
>df <- as.data.frame(DT)
Stem Year Diameter diff
1 1 1997 1.0 NA
2 1 1998 1.5 0.5
3 1 1999 2.0 0.5
4 1 2000 3.0 1.0
5 2 1997 2.0 NA
6 2 1998 2.5 0.5
7 2 1999 3.0 0.5
8 2 2000 5.0 2.0
9 3 1997 3.0 NA
10 3 1998 3.5 0.5
11 3 1999 4.0 0.5
12 3 2000 7.0 3.0
13 4 1997 4.0 NA
14 4 1998 4.5 0.5
15 5 1997 5.0 NA
16 5 1998 5.5 0.5
17 5 1999 6.0 0.5
18 5 2000 9.0 3.0
19 6 2000 15.0 NA
If you have a large dataset, data.table has the advantage of being extremely fast.