apply a function over groups of columns

Question

How can I use apply or a related function to create a new data frame that contains the results of the row averages of each pair of columns in a very large data

Answer 1

A similar question was asked here by @david: averaging every 16 columns in r (now closed), which I answered by adapting @TylerRinker's answer above, following a suggestion by @joran and @Ben. Because the resulting function might be of help to OP or future readers, I am copying that function here, along with an example for OP's data.

# Function to apply 'fun' to object 'x' over every 'by' columns
# Alternatively, 'by' may be a vector of groups
byapply <- function(x, by, fun, ...)
{
    # Create index list
    if (length(by) == 1)
    {
        nc <- ncol(x)
        split.index <- rep(1:ceiling(nc / by), each = by, length.out = nc)
    } else # 'by' is a vector of groups
    {
        nc <- length(by)
        split.index <- by
    }
    index.list <- split(seq(from = 1, to = nc), split.index)

    # Pass index list to fun using sapply() and return object
    sapply(index.list, function(i)
            {
                do.call(fun, list(x[, i], ...))
            })
}

Then, to find the mean of the replicates:

byapply(dat, 3, rowMeans)

Or, perhaps the standard deviation of the replicates:

byapply(dat, 3, apply, 1, sd)

Update

by can also be specified as a vector of groups:

byapply(dat, c(1,1,1,2,2,2), rowMeans)