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To estimate if a certain vector of numbers will fit into memory, you can quite easily predict the memory usage based on the size of the vector. An integer vector will use 4 bytes per number, and a numeric vector 8 bytes (double precision float). The following function prints the estimated memory usage of a vector based on the size of the vector and the type of vector:

predict_data_size = function(numeric_size, number_type = "numeric") {
if(number_type == "integer") {
byte_per_number = 4
} else if(number_type == "numeric") {
byte_per_number = 8
} else {
stop(sprintf("Unknown number_type: %s", number_type))
}
estimate_size_in_bytes = (numeric_size * byte_per_number)
class(estimate_size_in_bytes) = "object_size"
print(estimate_size_in_bytes, units = "auto")
}

For example:

> predict_data_size(1518*1518, "numeric")
17.6 Mb
> predict_data_size(1518*1518, "integer")
8.8 Mb
>

To print the size of the vector in a nice format, I change the class of estimate_size_in_bytes to "object_size". In this way if I call print on the object, R will call print.object_size (see utils:::print.object_size for the source), which performs the formatting.

You can also use this function to estimate the size of matrices and multi-dimensional arrays, it is the total size which matters. Note that the R object (vector, matrix, array) will take a little more space if it uses metadata (e.g. dimnames), but for any decently sized object this is probably small compared to the size of the numbers.