Calculate mean value of subsets and store them in a vector for further analysis - r

Question

Hullo, I've been working on a dataset for a while now, but am also kind of stuck. One question/answer here was already helpful, but I need to calculate the mean not for a single value, but sixty.
My dataset is basically this:
> data[c(1:5, 111:116), c(1:6, 85:87)]
plotcode block plot subsample year month Alo.pra Ant.odo Arr.ela
91 B1A01 B1 A01 1 2003 May 0 9 0
92 B1A02 B1 A02 1 2003 May 38 0 0
93 B1A03 B1 A03 1 2003 May 0 0 0
94 B1A04 B1 A04 1 2003 May 0 0 0
95 B1A05 B1 A05 1 2003 May 0 0 0
214 B2A16 B2 A16 2 2003 May 0 0 0
215 B2A17 B2 A17 2 2003 May 0 0 0
216 B2A18 B2 A18 2 2003 May 486 0 0
217 B2A19 B2 A19 2 2003 May 0 0 0
218 B2A20 B2 A20 2 2003 May 0 0 0
219 B2A21 B2 A21 2 2003 May 0 0 0
The first few columns are general data about the data point. Each plot has had up to 4 subsamples. The columns 85:144 are the data I want to calculate the means of.
I used this command:
tapply(data2003[,85] , as.factor(data2003$plotcode), mean, na.rm=T)
But like I said, I need to calculate the mean sixty times, for columns 85:144.
My idea was using a for–loop.
for (i in 85:144)
{
temp <- tapply(data2003[,i], data2003$plotcode, mean, na.rm=T)
mean.mass.2003 <- rbind(mean.mass.2003, temp)
}
But that doesn't work. I get multiple error messages, "number of columns of result is not a multiple of vector length (arg 2)".
What I basically want is a table in which the columns represent the species, with the rows as the plotcode and the actual entries in the fields being the respective means.

Answer 1

I figured and fiddled and had some help that worked as I wanted it. I know that's a kind of convoluted approach, but I only just started R, so I do like to understand what I code:
data.plots<-matrix(NA, 88,60) ## A new, empty matrix we'll fill with the loop
for (i in 85:144) # The numbers because that's where our relevant data is
{
temp <- tapply(data2007[,i], data2007$plotcode, mean, na.rm=T) # What tapply does in this instance: It calculates the mean value of the i-th column form data2003 for every row in which the plotcode is the same, ignoring NAs. temp will be a single row of values, obviously.
data.plots[,i-84]<-as.numeric(temp) # shunts the single row from temp we just calculated consecutively into data.plots.
}
colnames(data.plots) <- colnames(data[85:144])
rownames(data.plots) <- as.data.frame(table(data$plotcode))[,1] # the second part is basically a count() function, returning in the first column the unique entries found and in the second the frequency of that entry.
This works. It shunts the mean biomass per species into a temporary vector(? data frame? matrix?) as its being calculated for every unique entry in data2003$plotcode, and then overwrites consecutively the rows of the target matrix data.plots.
After naming the rows and columns of data.plots I can work with it without always having to remember each name.