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Sample from an unknown probability distribution

I have a vector with a length of ~ 100 thousand, with values ​​from 0 to 1, which corresponds to the suitability of habitats at geographical points. Although some of the values ​​are very small, many of them are 0.9, etc., therefore, the sum is much larger than one.

I would like to generate 1000 random samples of locations, with each sample having a length of 6 (without replacement), with the probability that the selected location is weighted by the value of the vector at that location.

The following are dummy data. Any ideas?

mylocs = letters[1:10]
myprobs = c(0.1,NA,0.01,0.2,0.6,NA,0.001,0.03,0.9,NA)
mydata = data.frame(mylocs,myprobs)
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I'm a little confused by your question, so here are two possible answers.

If you want you to select 1000 groups of six values, where groups can share values, then:

locs = letters[1:15]
probs = c(0.1,NA,0.01,0.2,0.6,NA,0.001,0.03,0.9,NA, 0.1, 0.1, 0.1, 0.1, 0.1)
mydata = data.frame(locs,probs)

d = na.omit(mydata)
replicate(1000, sample(d$locs, size=6, prob=d$probs, replace=F))

If groups should not share values, simply do:

## Change the "2" to 1000 in the real data set
s = sample(d$locs, size=6*2, prob=d$probs, replace=F)
matrix(s, ncol=6)
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