**Quartiles** are values that split up a dataset into four equal parts.

- The
**first quartile**represents the 25th percentile of a dataset. - The
**second quartile**represents the 50th percentile of a dataset. This value is equivalent to the median value of the dataset. - The
**third quartile**represents the 75th percentile of a dataset.

We can easily calculate the quartiles of a given dataset in R by using the **quantile()** function. This tutorial provides examples of how to use this function in practice.

**Calculating Quartiles in R**

The following code shows how to calculate the quartiles of a given dataset in R:

#define dataset data = c(4, 7, 12, 13, 14, 15, 15, 16, 19, 23, 24, 25, 27, 28, 33) #calculate quartiles of dataset quantile(data) 0% 25% 50% 75% 100% 4.0 13.5 16.0 24.5 33.0

Here’s how to interpret the output:

- The first value displays the minimum value in the dataset:
**4.0** - The second value displays the first quartile of the dataset:
**13.5** - The third value displays the second quartile of the dataset:
**16.0** - The fourth value displays the third quartile of the dataset:
**24.5** - The fifth value displays the maximum value in the dataset:
**33.0**

**Related: **How to Easily Calculate Percentiles in R

**Visualizing Quartiles in R**

We can use the **boxplot()** function to create a boxplot to visualize the quartiles of this dataset in R:

#create boxplot boxplot(data)

Here’s how to interpret the boxplot:

- The bottom “whisker” displays the minimum value of
**4**. - The bottom line of the box displays the first quartile value of
**13.5**. - The black bar in the middle of the box displays the second quartile value of
**16.0**. - The top line of the box displays the third quartile value of
**24.5**. - The top “whisker” displays the maximum value of
**33.0**.

This single plot helps us quickly visualize the distribution of values in the dataset.