Pseudo-random number generation
Sampling in R
Richie Cotton
Data Evangelist at DataCamp
What does random mean?
{adjective} made, done, happening, or chosen without method or conscious decision.
- Oxford Languages
True random numbers
- Generated from physical processes, like flipping coins.
- Hotbits uses radioactive decay.
- RANDOM.ORG uses atmospheric noise.
- Available in R via the
randompackage.
- Available in R via the
- True randomness is expensive.
1 https://www.fourmilab.ch/hotbits 2 https://www.random.org
Pseudo-random number generation
- Next "random" number calculated from previous "random" number.
- The first "random" number calculated from a seed.
- If you start from the same seed value, all future random numbers will be the same.
seed <- 1
calc_next_random(seed)
3
calc_next_random(3)
2
calc_next_random(2)
6
Random number generating functions
| function | distribution | function | distribution | function | distribution |
|---|---|---|---|---|---|
| rbeta | Beta | rgeom | Geometric | rsignrank | Wilcoxon signed rank |
| rbinom | Binomial | rhyper | Hypergeometric | rt | t |
| rcauchy | Cauchy | rlnorm | Lognormal | runif | Uniform |
| rchisq | Chi-squared | rlogis | Logistic | rweibull | Weibull |
| rexp | Exponential | rnbinom | Negative binomial | rwilcox | Wilcoxon rank sum |
| rf | F | rnorm | Normal | ||
| rgamma | Gamma | rpois | Poisson |
Visualizing random numbers
rbeta(5000, shape1 = 2, shape2 = 2)
[1] 0.2788 0.7495 0.6485 0.6665 0.6546 0.1575
...
[4996] 0.84719 0.35177 0.92796 0.67603 0.53960
randoms <- data.frame(
beta = rbeta(5000, shape1 = 2, shape2 = 2)
)
ggplot(randoms, aes(beta)) +
geom_histogram(binwidth = 0.1)
Random numbers seeds
set.seed(20000229)
rnorm(5)
-1.6538 -0.4028 -0.1654 -0.0734 0.5171
rnorm(5)
1.908 0.379 -1.499 1.625 0.693
set.seed(20000229)
rnorm(5)
-1.6538 -0.4028 -0.1654 -0.0734 0.5171
rnorm(5)
1.908 0.379 -1.499 1.625 0.693
Using a different seed
set.seed(20000229)
rnorm(5)
-1.6538 -0.4028 -0.1654 -0.0734 0.5171
rnorm(5)
1.908 0.379 -1.499 1.625 0.693
set.seed(20041004)
rnorm(5)
-0.6547 -0.7854 -0.0152 0.1514 0.5285
rnorm(5)
0.748 0.974 0.174 -0.781 -0.930
Let's practice!
Sampling in R
