Practice: Random Number Generation
Here you will use functions for randomizing and subsampling things. The exercises also touch on the reproducibility of these random manipulations. Run the code from the following example on your computer. Were you able to obtain the same "random" numbers after the set.seed was implemented?
When expecting someone to reproduce an R code that has random elements in it, the set.seed() function becomes very handy.
For example, these two lines will always produce different output (because that is the whole point of random number generators):
> sample(1:10,5)
[1] 6 9 2 7 10
> sample(1:10,5)
[1] 7 6 1 2 10
These two will also produce different outputs:
> rnorm(5)
[1] 0.4874291 0.7383247 0.5757814 -0.3053884 1.5117812
> rnorm(5)
[1] 0.38984324 -0.62124058 -2.21469989 1.12493092 -0.04493361
However, if we set the seed to something identical in both cases (most people use 1 for simplicity), we get two identical samples:
> set.seed(1)
> sample(letters,2)
[1] "g" "j"
> set.seed(1)
> sample(letters,2)
[1] "g" "j"
and same with, say, rexp() draws:
> set.seed(1)
> rexp(5)
[1] 0.7551818 1.1816428 0.1457067 0.1397953 0.4360686
> set.seed(1)
> rexp(5)
[1] 0.7551818 1.1816428 0.1457067 0.1397953 0.4360686
Source: RIP Tutorial, https://riptutorial.com/r/example/14029/random-number-generator-s-reproducibility
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