Plot Cumulative Distribution Function in R (original) (raw)

Last Updated : 17 Jul, 2025

A Cumulative Distribution Function (CDF) represents the probability that a random variable takes a value less than or equal to a given number. It helps understand how values in a dataset accumulate over a range.

**Syntax:

ecdf( data_vector )

**Parameter:

• **data_vector: determines the vector that contains data for CDF calculation.

1. Using Base R

To plot a CDF in R programming language, we first calculate it using the ecdf() function and then display it using the plot() function.

**Syntax:

plot(CDF)

**Parameter:

• **CDF: The result of the ecdf() function representing the CDF of the dataset.

**Example 1: We plot the CDF of 500 random numbers

• **sample_Data: A numeric vector generated using rnorm().
• **ecdf(sample_Data): Computes the cumulative probabilities.
• **plot(CDF): Plots the CDF curve. R `

sample_Data <- rnorm(500) CDF <- ecdf(sample_Data) plot(CDF)

`

**Output:

Cumulative Distribution Function in R

**Example 2: We plot the CDF of Petal.Length from the iris dataset

• **data(iris): Loads the built-in dataset.
• **iris$Petal.Length: Numeric column to be plotted.
• **ecdf(iris$Petal.Length): Computes cumulative distribution.
• **plot(...): Displays the CDF plot. R `

data(iris) plot(ecdf(iris$Petal.Length))

`

**Output:

Cumulative Distribution Function in R

2. Plotting CDF of a Known Distribution

To plot the CDF of a standard distribution, we use the curve() function along with a probability function like pnorm.

**Syntax:

curve( expression, from, to )

**Parameters:

• **expression: determines the expression function for CDF calculation.
• **from: determines the lower limit of data.
• **to: determines the upper limit of data.

**Example: We plot the CDF of a normal distribution from -10 to 10.

• **pnorm: The CDF of a normal distribution.
• **from = -10, to = 10: X-axis limits for the curve. R `

curve(pnorm, from = -10, to = 10)

`

**Output:

Cumulative Distribution Function in R

3. Plotting CDF Using ggplot2

We can also use the ggplot2 package to create styled CDF plots using stat_function().

**Syntax:

plot + stat_function( fun )

**Parameters:

• **fun: determines the function for the shape of the plot.

**Example: We create a CDF plot for the normal distribution using ggplot2.

• **library(ggplot2): Loads the ggplot2 package.
• **sample_limit: Defines the range of x values.
• **stat_function(fun = pnorm): Draws the normal CDF.
• **theme_minimal(), labs(): Customizes style and labels. R `

install.packages("ggplot2") library(ggplot2)

sample_limit <- data.frame(x = c(-10, 10))

ggplot(sample_limit, aes(x = x)) + stat_function(fun = pnorm, color = "skyblue", size = 2) + theme_minimal() + labs( title = "Cumulative Distribution Function (CDF) Plot", x = "X-axis", y = "Cumulative Probability", caption = "Created with ggplot2" )

`

**Output:

Cumulative Distribution Function in R

This plot displays the cumulative distribution function of a normal distribution using ggplot2, with a styled curve, custom labels and a minimal visual theme.