To select a random sample in R we can use the sample() function, which uses the following syntax:
sample(x, size, replace = FALSE, prob = NULL)
where:
- x: A vector of elements from which to choose.
- size: Sample size.
- replace: Whether to sample with replacement or not. Default is FALSE.
- prob: Vector of probability weights for obtaining elements from vector. Default is NULL.
This tutorial explains how to use this function to select a random sample in R from both a vector and a data frame.
Example 1: Random Sample from a Vector
The following code shows how to select a random sample from a vector without replacement:
#create vector of data data <- c(1, 3, 5, 6, 7, 8, 10, 11, 12, 14) #select random sample of 5 elements without replacement sample(x=data, size=5) [1] 10 12 5 14 7
The following code shows how to select a random sample from a vector with replacement:
#create vector of data data <- c(1, 3, 5, 6, 7, 8, 10, 11, 12, 14) #select random sample of 5 elements with replacement sample(x=data, size=5, replace=TRUE) [1] 12 1 1 6 14
Example 2: Random Sample from a Data Frame
The following code shows how to select a random sample from a data frame:
#create data frame df <- data.frame(x=c(3, 5, 6, 6, 8, 12, 14), y=c(12, 6, 4, 23, 25, 8, 9), z=c(2, 7, 8, 8, 15, 17, 29)) #view data frame df x y z 1 3 12 2 2 5 6 7 3 6 4 8 4 6 23 8 5 8 25 15 6 12 8 17 7 14 9 29 #select random sample of three rows from data frame rand_df <- df[sample(nrow(df), size=3), ] #display randomly selected rows rand_df x y z 4 6 23 8 7 14 9 29 1 3 12 2
Here’s what’s happening in this bit of code:
1. To select a subset of a data frame in R, we use the following syntax: df[rows, columns]
2. In the code above, we randomly select a sample of 3 rows from the data frame and all columns.
3. The end result is a subset of the data frame with 3 randomly selected rows.
It’s important to note that each time we use the sample() function, R will select a different sample since the function chooses values randomly.
In order to replicate the results of some analysis, be sure to use set.seed(some number) so that the sample() function chooses the same random sample each time. For example:
#make this example reproducible set.seed(23) #create data frame df <- data.frame(x=c(3, 5, 6, 6, 8, 12, 14), y=c(12, 6, 4, 23, 25, 8, 9), z=c(2, 7, 8, 8, 15, 17, 29)) #select random sample of three rows from data frame rand_df <- df[sample(nrow(df), size=3), ] #display randomly selected rows rand_df x y z 5 8 25 15 2 5 6 7 6 12 8 17
Each time you run the above code, the same 3 rows of the data frame will be selected each time.
Additional Resources
Stratified Sampling in R (With Examples)
Systematic Sampling in R (With Examples)
Cluster Sampling in R (With Examples)
Hi, Many thanks for the great explanation. I have a question: Is there any functions or method by which I can I control the mean or SD of the sample that will be chosen, I would appreciate that.
Thank you for the demo. I got to the last step with matching number of items in each column. However, I am looking to match 10 subjects to a randomly generated numbers which run up to 100. I need only 10 randomly pulled numbers against each subject from these 100. How do you do that?
Hi Jaimini..You are very welcome!
Let’s say you have 10 subjects, and you want to assign each one 10 random numbers drawn from the numbers 1 to 100. These numbers should be unique for each subject. Here’s how you can do that in R.
“`r
# Set a seed to make sure the results are the same every time you run it (optional)
set.seed(123)
# Create a list of subject names
subjects <- paste0("Subject_", 1:10) # Generate 10 random numbers for each subject df <- data.frame( Subject = rep(subjects, each = 10), RandomNumber = as.vector(replicate(length(subjects), sample(1:100, 10))) ) # Print the result print(df) ``` What's happening here is that we’re repeating each subject 10 times so that each will get 10 numbers. Then we're using the `replicate` function to pull 10 unique numbers for each subject from the numbers 1 to 100. The `as.vector` part just reshapes the results into a single column so that it lines up properly in the data frame.