You can use the **ecdf** function in R to calculate and plot an empirical cumulative distribution function.

Here is the most common way to use this function:

#calculate empirical cumulative distribution function of data p = ecdf(data) #plot empirical cumulative distribution function plot(p)

The following example shows how to use this function in practice.

**Example: How to Use ecdf() Function in R**

For this example, let’s create a vector of 1,000 random values that follow a standard normal distribution:

#make this example reproducible set.seed(1) #create vector of 1,000 random values that follow standard normal distribution data = rnorm(1000) #view first six values in vector head(data) [1] -0.6264538 0.1836433 -0.8356286 1.5952808 0.3295078 -0.8204684

We can use the **ecdf** function to calculate the empirical cumulative distribution function of this dataset and then use the **plot** function to visualize it:

#calculate empirical cumulative distribution function of data p = ecdf(data) #plot empirical cumulative distribution function plot(p)

Note that you can also use the **xlab**, **ylab** and **main** arguments within the plot function to add an x-axis label, y-axis label and title to the plot, respectively:

#calculate empirical cumulative distribution function of data p = ecdf(data) #plot empirical cumulative distribution function with axis labels and title plot(p, xlab='x', ylab='CDF', main='CDF of Data')

The x-axis displays the values from the dataset.

The y-axis displays the cumulative distribution function.

**Related**: The Difference Between a CDF vs. PDF in Statistics

**Additional Resources**

The following tutorials explain how to perform other common tasks in R:

How to Plot a Normal Distribution in R

A Guide to dnorm, pnorm, qnorm, and rnorm in R

How to Perform a Shapiro-Wilk Test for Normality in R