# Student t distribution in R (4 Examples) | dt, pt, qt & rt Functions

This article shows how to **apply the Student t functions** in R.

The tutorial is structured as follows:

- Example 1: Student t Probability Density Function (dt Function)
- Example 2: Student t Cumulative Distribution Function (pt Function)
- Example 3: Student t Quantile Function (qt Function)
- Example 4: Generating Random Numbers (rt Function)
- Video, Further Resources & Summary

Let’s dive right into the examples.

## Example 1: Student t Probability Density Function (dt Function)

In the first example, we’ll create a graphic showing the density of the Student t distribution.

First, we need to create a vector of quantiles in R:

x_dt <- seq(- 10, 10, by = 0.01) # Specify x-values for dt function

After running the previous R code, we can apply the dt command in R as follows. In the example, we use 3 degrees of freedom (as specified by the argument df = 3):

y_dt <- dt(x_dt, df = 3) # Apply dt function

The Student t density values are now stored in the data object y_dt. We can draw a graph representing these values with the plot R function:

plot(y_dt) # Plot dt values

**Figure 1: Density of Student t Distribution in R.**

## Example 2: Student t Cumulative Distribution Function (pt Function)

This example shows how to draw the cumulative distribution function (CDF) of a Student t distribution.

As in the previous example, we first need to create an input vector:

x_pt <- seq(- 10, 10, by = 0.01) # Specify x-values for pt function

Then, we can apply the pt function to this input vector in order to create the corresponding CDF values:

y_pt <- pt(x_pt, df = 3) # Apply pt function

Finally, we can apply the plot function to draw a graphic representing the CDF of the Student t distribution in R:

plot(y_pt) # Plot pt values

**Figure 2: Cumulative Distribution Function of Student t Distribution in R.**

## Example 3: Student t Quantile Function (qt Function)

If we want to draw a plot of the quantile function of the Student t distribution, we need to create a sequence of probabilities as input:

x_qt <- seq(0, 1, by = 0.01) # Specify x-values for qt function

We then can apply the qt R command to these probabilities:

y_qt <- qt(x_qt, df = 3) # Apply qt function

The corresponding plot can be created with the plot function as follows:

plot(y_qt) # Plot qt values

**Figure 3: Quantile Function of Student t Distribution in R.**

## Example 4: Generating Random Numbers (rt Function)

We can also apply the Student t functions in order to generate random numbers. First, we have to set a seed for reproducibility and we also need to specify a sample size N that we want to simulate:

set.seed(91929) # Set seed for reproducibility N <- 10000 # Specify sample size

We can now use the rt function to generate our set of random numbers:

y_rt <- rt(N, df = 3) # Draw N log normally distributed values y_rt # Print values to RStudio console

The following histogram illustrates the distribution of our random numbers (i.e. a Student t distribution):

hist(y_rt, # Plot of randomly drawn student t density breaks = 100, main = "")

**Figure 4: Random Numbers According to Student t Distribution in R.**

## Video, Further Resources & Summary

Have a look at the following video of my YouTube channel. In the video tutorial, I’m explaining the contents of this tutorial.

*The YouTube video will be added soon.*

You may also have a look at the other articles on distributions and the simulation of random numbers in R:

- Bernoulli Distribution in R
- Beta Distribution in R
- Binomial Distribution in R
- Bivariate & Multivariate Distributions in R
- Cauchy Distribution in R
- Chi-Squred Distribution in R
- Exponential Distribution in R
- F Distribution in R
- Gamma Distribution in R
- Geometric Distribution in R
- Hypergeometric Distribution in R
- Log Normal Distribution in R
- Logistic Distribution in R
- Negative Binomial Distribution in R
- Normal Distribution in R
- Poisson Distribution in R
- Student t Distribution in R
- Studentized Range Distribution in R
- Uniform Distribution in R
- Weibull Distribution in R
- Wilcoxon Signedank Statistic Distribution in R
- Wilcoxonank Sum Statistic Distribution in R

Furthermore, I can recommend to read the related tutorials on this website. Please find a selection of interesting articles here.

In this article you learned how to **draw and simulate a Student t distribution** in the R programming language. Please let me know in the comments below, in case you have any further questions.