Understanding data sampling in R

Sampling is a method to select a subset of data from a statistical population, which can use the characteristics of the population to estimate the whole population. The following recipe will demonstrate how to generate samples in R.

Getting ready

Make sure that you have an R working environment for the following recipe.

How to do it...

Perform the following steps to understand data sampling in R:

1. To generate random samples of a given population, the user can simply use the sample function:

> sample(1:10)

2. To specify the number of items returned, the user can set the assigned value to the size argument:

> sample(1:10, size = 5)

3. Moreover, the sample can also generate Bernoulli trials by specifying replace = TRUE (default is FALSE):

> sample(c(0,1), 10, replace = TRUE)

4. If we want to do a coin flipping trail, where the outcome is Head or Tail, we can use:

  > outcome <- c("Head","Tail")  > sample(outcome, size=1)

5. To generate result for 100 times, we can use...