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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 weâ€™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 theÂ **group_by()Â **andÂ **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 theÂ **group_by()Â **andÂ **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