# Stratified Sampling in R (With Examples)

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

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 %>%
sample_n(size=10)

#find frequency of students from each 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 %>%