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 <- data.frame(grade = rep(c('Freshman', 'Sophomore', 'Junior', 'Senior'), 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 <- df %>% 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 <- df %>% 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

Great article there, Zach.

One of the few resources online I was able to find that defines and applies stratified probability sampling, with very little rambling. I was able to easily apply it to the problem I was working on. Thanks for the article.

Hi

I have three IDBs and this is the number of people registered from each

Site female Male Total

IDB_A 46 14 60

IDB_B 17 23 40

IDB_C 79 21 100

Total 142 58 200

And this is the sample I want to select from each site

Site female Male Total

IDB_A 20 6 26

IDB_B 7 10 17

IDB_C 34 9 43

Total 60 25 85

And I used the following code by creating three different strata (one for each site) and then selected a random sample from each stratum (NOTE: FBF_PDM is the name of the dataset)

str1 <- FBF_PDM[FBF_PDM$Sites=="IDB_A",]

str2 <- FBF_PDM[FBF_PDM$Sites=="IDB_B", ]

str3 <- FBF_PDM[FBF_PDM$Sites=="IDB_C", ]

sample1 <- str1[sample(1:nrow(str1), 26, replace = FALSE), ]

sample2 <- str2[sample(1:nrow(str2), 17, replace = FALSE), ]

sample3 <- str3[sample(1:nrow(str3), 43, replace = FALSE), ]

overall <- rbind(sample1, sample2, sample3)

write.table(overall, "overall2.csv", row.names = FALSE, sep = " , ")

but this code does not give me exact sample I wanted to have, so is there a way I can have the sample I wanted to select from each site with gender (male, female).

Thanks