If you are doing many operations with the same data, you might
want to convert your data into a list with missing values omitted.
Each element of the list would be a vector containing non-missing values.
Optionally, you can give the list dimensions.
x <- array(1:1000, c(10,10,10))
set.seed(0)
x[runif(1000) > .4] <- NA
temp <- x
dim(temp) <- c(10, prod(dim(x)[2:3]))
x2 <- lapply(1:ncol(temp), function(j, x) omit.na(x[,j]), x = temp)
x2 # list of length 100, containing non-missing values for each
# Now can turn x2 into a matrix/list hybrid if you like,
# to let you use apply on rows or columns
dim(x2) <- dim(x)[2:3]
# example using apply:
apply(x2, 2, function(x) length(unlist(x)))
Tim Hesterberg
>Dear all,
>
>I have numerous 3-dimenional arrays and each array contains no entire row or
>column with NA values. However, 50-60% of the values in the array are NA
>values. I perform various operations on the array using the apply statement.
>However, because the arrays are quite large the calculations take a while to
>finish. Within the apply statement I reduce the number of calculations on the
>vector by using the !is.na() statement. Is there a faster way to run the apply
>command without the need to read in an enitire vector from the array which
>contains a large amount of NA values, i.e. should I store the data in another
>format to avoid having an array with a large amount of NA values.
>
>For example, if I have a 3-dimensional 1000x1000x1000 and use the apply
>statement on dimensions 2 and 3, I'm reading in vectors of length 1000 in the
>apply statement and most of the 1000 values might be NAs. Can this be avoided?
>
>I would greatly appreciate any help on this.
>Dave
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