In this article, you’ll learn how to draw elements randomly from an object in R. We will also be creating objects with random values all this using just one function
sample takes a sample of the specified size from the elements of
x using either with or without replacement.
> sample(x, size, replace = FALSE, prob = NULL) > sample.int(n, size = n, replace = FALSE, prob = NULL, useHash = (!replace && is.null(prob) && size <= n/2 && n > 1e7))
|either a vector of one or more elements from which to choose, or a positive integer. See ‘Details.’|
|a positive number, the number of items to choose from. See ‘Details.’|
|a non-negative- integer giving the number of items to choose.|
|should sampling be with replacement?|
|a vector of probability weights for obtaining the elements of the vector being sampled.|
x has length 1, is numeric (in the sense of
x >= 1, sampling via
sample takes place from
1:x. Note that this convenience feature may lead to undesired behaviour when
x is of varying length in calls such as
sample(x). See the examples.
x can be any R object for which
length and subsetting by integers make sense: S3 or S4 methods for these operations will be dispatched as appropriate.
sample the default for
size is the number of items inferred from the first argument, so that
sample(x) generates a random permutation of the elements of
It is allowed to ask for
size = 0 samples with
n = 0 or a length-zero
x, but otherwise
n > 0 or positive
length(x) is required.
Non-integer positive numerical values of
x will be truncated to the next smallest integer, which has to be no larger than
prob argument can be used to give a vector of weights for obtaining the elements of the vector being sampled. They need not sum to one, but they should be non-negative and not all zero. If
replace is true, Walker’s alias method (Ripley, 1987) is used when there are more than 200 reasonably probable values: this gives results incompatible with those from R < 2.2.0.
replace is false, these probabilities are applied sequentially, that is the probability of choosing the next item is proportional to the weights amongst the remaining items. The number of nonzero weights must be at least
size in this case.
sample.int is a bare interface in which both
size must be supplied as integers.
n can be larger than the largest integer of type
integer, up to the largest representable integer in type
To draw elements randomly
We first need an object with random values so that we can draw elements from it. And we will be doing this with the help of
# Creating a vector of 10 elements. # Set seed to get the same random values every time. > set.seed(100) > x <- sample(1:100, size = 10) > x  74 89 78 23 86 70 4 55 95 7
Now to draw elements randomly from the vector.
# to get one random value from x > sample(x, 1)  4 # to get three random values from x > sample(x, 3)  89 95 7
Hence, we saw how to draw elements randomly from an object and also about the
sample() function with its details and how to use it along with the examples.
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