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 stratified random sampling, in which a population is split into groups and a certain number of members from each group are randomly selected to be included in the sample.
This tutorial explains how to perform stratified random sampling in R.
Example: Stratified Sampling in R
A high school is composed of 400 students who are either Freshman, Sophomores, Juniors, or Seniors. Suppose we’d like to take a stratified sample of 40 students such that 10 students from each grade are included in the sample.
The following code shows how to generate a sample data frame of 400 students:
#make this example reproducible set.seed(1) #create data frame df each=100), gpa = rnorm(400, mean=85, sd=3)) #view first six rows of data frame head(df) grade gpa 1 Freshman 83.12064 2 Freshman 85.55093 3 Freshman 82.49311 4 Freshman 89.78584 5 Freshman 85.98852 6 Freshman 82.53859
Stratified Sampling Using Number of Rows
The following code shows how to use the group_by() and sample_n() functions from the dplyr package to obtain a stratified random sample of 40 total students with 10 students from each grade:
library(dplyr) #obtain stratified sample strat_sample % group_by(grade) %>% sample_n(size=10) #find frequency of students from each grade table(strat_sample$grade) Freshman Junior Senior Sophomore 10 10 10 10
Stratified Sampling Using Fraction of Rows
The following code shows how to use the group_by() and sample_frac() functions from the dplyr package to obtain a stratified random sample in which we randomly select 15% of students from each grade:
library(dplyr) #obtain stratified sample strat_sample % group_by(grade) %>% sample_frac(size=.15) #find frequency of students from each grade table(strat_sample$grade) Freshman Junior Senior Sophomore 15 15 15 15
Additional Resources
Types of Sampling Methods
Cluster Sampling in R
Systematic Sampling in R