Question

Here is probably an easy question.. but I am really struggling so help is very much appreciated.

I have 4d data that I wish to transform into 3d data. The data has the following attributes:

lon <- 1:96  
lat <- 1:73  
lev <- 1:60  
tme <- 1:12

data <- array(runif(96*73*60*12), 
              dim=c(96,73,60,12) ) # fill with random test values  

What I would like to do is calculate the mean of the first few levels (say 1:6). The new data would be of the form:

new.data <- array(96*73*12), dim=c(96,73,12) ) # again just test data  

But would contain the mean of the first 5 levels of data. At the moment the only way I have been able to make it work is to write a rather inefficient loop which extracts each of the first 5 levels and divides the sum of those by 5 to get the mean.

I have tried:

new.data <- apply(data, c(1,2,4), mean)  

Which nicely gives me the mean of ALL the vertical levels but can’t understand how to subset the 3rd dimension to get an average of only a few! e.g.

new.data <- apply(data, c(1,2,3[1:5],4), mean) # which returns   
  Error in ds[-MARGIN] : only 0's may be mixed with negative subscripts

I am desperate for some help!

Answer 1

apply with indexing (the proper use of “[“) should be enough for the mean of the first six levels of the third dimension if I understand your terminology:

> str(apply(data[,,1:6,] , c(1,2,4), FUN=mean) )
 num [1:96, 1:73, 1:12] 0.327 0.717 0.611 0.388 0.47 ...

This returns a 96 x 73 by 12 matrix.