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Binary vectorisation of large data sets
I have a large (~2GB) data set consisting of 1's and 0's. The data set is of this form:
class(df) = 'data.frame'
df = data.frame(x = c(rep(1, 5e5), rep(0, 5e5))
I am trying to generate a binary vector of size 2GB. I am trying this:
vector = rep(0, 2GB)
for(i in 1:nrow(df)){
vector[i] = ((df$x[i] == 1) == (df$x[i] == 0))
}
However it is taking a really long time (5 hours or so). It has been running for 9 hours now, and I still have not reached 2GB. I am wondering if there is a faster way to go about it. Note that I need a binary vector, so simply converting the data to the necessary type is not an option.
A:
You could use rep() and data.table for this:
# set seed for reproducibility
set.seed(1)
library(data.table)
DT 0b46394aab
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