Table of Contents
Systematic sampling in R can be performed by using the sample() function. This function allows you to specify the size of your sample, the population from which to draw the sample, and the probability of selecting each element. The function then randomly selects elements from the population to create a systematic sample of the desired size. You can also set a seed for reproducibility. To perform systematic sampling in R, you must first create a sequence of numbers that represent the population from which you will draw your sample, then use the sample() function to randomly select the desired number of elements from this sequence.
Researchers often take samples from a population and use the data from the sample to draw conclusions about the population as a whole.
One commonly used sampling method is systematic sampling, which is implemented with a simple two step process:
1. Place each member of a population in some order.
2. Choose a random starting point and select every nth member to be in the sample.
This tutorial explains how to perform systematic sampling in R.
Example: Systematic Sampling in R
Suppose a superintendent wants to obtain a sample of 100 students from a school that has 500 total students. She chooses to use systematic sampling in which she places each student in alphabetical order according to their last name, randomly chooses a starting point, and picks every 5th student to be in the sample.
The following code shows how to create a fake data frame to work with in R:
#make this example reproducible set.seed(1) #create simple function to generate random last names randomNames <- function(n = 5000) { do.call(paste0, replicate(5, sample(LETTERS, n, TRUE), FALSE)) } #create data frame df <- data.frame(last_name = randomNames(500), gpa = rnorm(500, mean=82, sd=3)) #view first six rows of data frame head(df) last_name gpa 1 GONBW 82.19580 2 JRRWZ 85.10598 3 ORJFW 88.78065 4 XRYNL 85.94409 5 FMDCE 79.38993 6 XZBJC 80.49061
And the following code shows how to obtain a sample of 100 students through systematic sampling:
#define function to obtain systematic sample obtain_sys = function(N,n){ k = ceiling(N/n) r = sample(1:k, 1) seq(r, r + k*(n-1), k) } #obtain systematic sample sys_sample_df = df[obtain_sys(nrow(df), 100), ] #view first six rows of data frame head(sys_sample_df) last_name gpa 3 ORJFW 88.78065 8 RWPSB 81.96988 13 RACZU 79.21433 18 ZOHKA 80.47246 23 QJETK 87.09991 28 JTHWB 83.87300 #view dimensions of data frame dim(sys_sample_df) [1] 100 2
Notice that the first member included in the sample was in row 3 of the original data frame. Each subsequent member in the sample is located 5 rows after the previous member.
And from using dim() we can see that the systematic sample we obtained is a data frame with 100 rows and 2 columns.
Types of Sampling Methods
Stratified Sampling in R
Cluster Sampling in R