This post explains how to do this with R.

> # ========================================================
> # Can we sample from things that are not numbers, for
> # example, pretend we are taking M&Ms out of a jar that
> # has blue, green, and red M&Ms?
> # ========================================================
> candy = c("blue","green","red")
> sample(candy, 20, replace=T)
 [1] "red"   "red"   "red"   "red"   "blue"  "green" "red"
 [8] "blue"  "blue"  "blue"  "blue"  "green" "green" "green"
[15] "blue"  "blue"  "blue"  "green" "blue"  "red"
> # ========================================================
> # "replace=T" is required since there are only three items
> # in our list. It means that we can sample the same item
> # more than one time.
> # ========================================================
> # ========================================================
> # What about probability?
> # ========================================================
> sample(candy, 20, replace=T, prob=c(0.7, 0.2, 0.1))
 [1] "blue"  "blue"  "green" "blue"  "blue"  "blue"  "blue"
 [8] "green" "green" "blue"  "blue"  "blue"  "blue"  "blue"
[15] "green" "blue"  "green" "green" "green" "blue"
> # ========================================================
> # A second time
> # ========================================================
> sample(candy, 20, replace=T, prob=c(0.7, 0.2, 0.1))
 [1] "green" "blue"  "green" "blue"  "blue"  "blue"  "blue"
 [8] "blue"  "blue"  "blue"  "blue"  "blue"  "blue"  "green"
[15] "blue"  "blue"  "red"   "green" "green" "blue"
> # ========================================================
> # Notice how, in general, we have more blues and greens
> # and almost no reds. You can sort these results too, but
> # then  you don't see the sequence in which the items
> # were selected, which might also be interesting. Here
> # are three more samples, sorted so you can see the effect
> # that setting the probability has on the outcome.
> # ========================================================
> sort(sample(candy, 20, replace=T, prob=c(0.7, 0.2, 0.1)))
 [1] "blue"  "blue"  "blue"  "blue"  "blue"  "blue"  "blue"
 [8] "blue"  "blue"  "blue"  "blue"  "blue"  "blue"  "blue"
[15] "blue"  "blue"  "green" "green" "red"   "red"
> sort(sample(candy, 20, replace=T, prob=c(0.7, 0.2, 0.1)))
 [1] "blue"  "blue"  "blue"  "blue"  "blue"  "blue"  "blue"
 [8] "blue"  "blue"  "blue"  "blue"  "blue"  "blue"  "blue"
[15] "blue"  "green" "red"   "red"   "red"   "red"
> sort(sample(candy, 20, replace=T, prob=c(0.7, 0.2, 0.1)))
 [1] "blue"  "blue"  "blue"  "blue"  "blue"  "blue"  "blue"
 [8] "blue"  "blue"  "blue"  "blue"  "blue"  "blue"  "green"
[15] "green" "green" "green" "green" "green" "red"
> # ========================================================
> # You can also assign a name to the probability set before
> # you start your sample instead of typing out the
> # probabilities each time you take a sample.
> # ========================================================
> ProbCandy = c(0.7, 0.2, 0.1)
> sort(sample(candy, 20, replace=T, prob=ProbCandy))
 [1] "blue"  "blue"  "blue"  "blue"  "blue"  "blue"  "blue"
 [8] "blue"  "blue"  "blue"  "blue"  "blue"  "blue"  "blue"
[15] "blue"  "blue"  "blue"  "blue"  "blue"  "green"

In this post:

1. Simple random sampling
2. Simple random sampling with a seed
3. Sorting your sample

> # ========================================================
> # Sample 30 from 1 to 300
> # ========================================================
> sample(300, 30)
 [1]  44 200 258 290 165 132 287  70 118 241  11 191 194 192
[15] 127 143  55 217  49 130 263 203  26  46 251 142  72 107
[29]  77  31
> # ========================================================
> # A second time
> # ========================================================
> sample(300, 30)
 [1] 101 108  25 289 257 102 110  45 173 197 202 168 134 225
[15] 184 155 226  68  86  51  90 204 159 263 245 201 213 169
[29] 127  11
> # ========================================================
> # What about a replicable sample?
> # ========================================================
> set.seed(123)
> sample(300, 30)
 [1]  87 236 122 263 279  14 156 262 162 133 278 132 196 165
[15]  30 257  70  12  93 269 250 194 179 276 181 195 150 163
[29]  79  40
> # ========================================================
> # A second time
> # ========================================================
> set.seed(123)
> sample(300, 30)
 [1]  87 236 122 263 279  14 156 262 162 133 278 132 196 165
[15]  30 257  70  12  93 269 250 194 179 276 181 195 150 163
[29]  79  40
> # ========================================================
> # Can we sort the output to make the result easy to see?
> # ========================================================
> set.seed(123)
> sort(sample(300, 30))
 [1]  12  14  30  40  70  79  87  93 122 132 133 150 156 162
[15] 163 165 179 181 194 195 196 236 250 257 262 263 269 276
[29] 278 279

What is “set.seed” for?

The “set.seed” function in R can help when you want to verify or replicate a sample. In this case, the seed I used was “123″ (but it can be any number you want it to be). So, if you run the same sample with the same seed in R, you should get the same results as me. If you ran the same sample without a seed, each time, you would get a different result.

Disclaimer: I’m not by any measures an expert in R. I have just been experimenting with the software. By the way, if you want to experiment with any of the code I mention here, you can first download R, copy the code, and in R, go to “Edit > Paste commands only”.