Random numbers are critical to many analyses, and consequently R has a wide variety of functions for sampling from different distributions.

A `sample`

function is important workhorse function for generating random values, and its behavior has a few quirks, so itâ€™s worth getting to know it more thoroughly. If you just pass it a number, *n*, it will return a permutation of the natural numbers from *1* to *n*

sample(7) [1] 1 2 5 7 4 6 3

If you give it a second value, it will return that many random numbers between *1* and *n*

sample(7, 5) [1] 7 2 3 1 5

Notice that all those random numbers are different. By default, `sample`

samples without replacement. That is, each value can only appear once. To allow sampling with replacement, pass `replace = TRUE`

sample(7, 10, replace = TRUE) [1] 4 6 1 7 5 3 6 7 4 2

Also, you can select *n* items from a vector

#sample(vec, n) sample(world.series$year, 10) [1] 1906 1963 1966 1928 1905 1924 1961 1959 1927 1934

The following example generates a random sequence of 10 simulated flips of a coin

sample(c("H","T"), 10, replace=TRUE) [1] "H" "H" "H" "T" "T" "H" "T" "H" "H" "T"

By default, sample will choose equally among the set elements and so the probability of selecting either *H* or *T* is 0.5. With a Bernoulli trial, the probability *p* of success is not necessarily 0.5. You can bias the sample by using the `prob`

argument of `sample`

. This argument is a vector of probabilities, one for each set element.

Suppose we want to generate 10 Bernoulli trials with a probability of success *p = 0.8*. We set the probability of *H* to be 0.2 and the probability of *T* to 0.8

sample(c("H","T"), 10, replace=TRUE, prob=c(0.2,0.8)) [1] "T" "T" "T" "T" "T" "H" "H" "T" "H" "T"

Data Science is a series of failures punctuated by the occasional success.
*N.D Lewis*

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