# Geometric Distribution in R (4 Examples) | dgeom, pgeom, qgeom & rgeom Functions

This tutorial shows how to **apply the geometric functions** in the R programming language.

The tutorial contains four examples for the geom R commands. More precisely, the tutorial will consist of the following content:

- Example 1: Geometric Density in R (dgeom Function)
- Example 2: Geometric Cumulative Distribution Function (pgeom Function)
- Example 3: Geometric Quantile Function (qgeom Function)
- Example 4: Simulation of Random Numbers (rgeom Function)
- Video & Further Resources

You’re here for the answer, so let’s get straight to the examples…

## Example 1: Geometric Density in R (dgeom Function)

In the first example, we will illustrate the density of the geometric distribution in a plot. As a first step, we need to create a vector of quantiles:

x_dgeom <- seq(0, 20, by = 1) # Specify x-values for dgeom function

Now, we can apply the dgeom function to this vector as shown in the R code below. Note that I’m using a probability of 0.5 (i.e. 50%) in the examples of this tutorial. You may modify this probability to your specific preferences.

y_dgeom <- dgeom(x_dgeom, prob = 0.5) # Apply dgeom function

The previous R syntax stored the density values of the geometric distribution in the data object y_dgeom. We can now plot these values with the plot R function as follows:

plot(y_dgeom) # Plot dgeom values

**Figure 1: Application of dgeom Function.**

## Example 2: Geometric Cumulative Distribution Function (pgeom Function)

Example 2 shows how to draw a plot of the geometric cumulative distribution function (CDF). As in Example 1, we first need to create a sequence of quantiles:

x_pgeom <- seq(0, 20, by = 1) # Specify x-values for pgeom function

We can use this sequence of quantiles as input for the pgeom function:

y_pgeom <- pgeom(x_pgeom, prob = 0.5) # Apply pgeom function

And then, we can apply the plot function to draw a graphic containing the values of the geometric cumulative distribution function:

plot(y_pgeom) # Plot pgeom values

**Figure 2: Application of pgeom Function.**

## Example 3: Geometric Quantile Function (qgeom Function)

In the third example, we will discuss the geometric quantile function. In case of the quantile function, we need to create a vector of probabilities (instead of quantiles as in Examples 1 and 2):

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

Now, we can use the qgeom R function to return the quantile function values that correspond to our input probabilities:

y_qgeom <- qgeom(x_qgeom, prob = 0.5) # Apply qgeom function

Finally, we can produce a graph that is showing our quantile function values:

plot(y_qgeom) # Plot qgeom values

**Figure 3: Application of qgeom Function.**

## Example 4: Simulation of Random Numbers (rgeom Function)

We can also simulate a set of random numbers, which follows the geometric distribution. For this task, we first need to specify a seed and a sample size of random numbers that we want to generate:

set.seed(53535) # Set seed for reproducibility N <- 10000 # Specify sample size

Then, we can apply the rgeom function to generate our random numbers:

y_rgeom <- rgeom(N, prob = 0.5) # Draw N geometrically distributed values y_rgeom # Print values to RStudio console

The following histogram shows how our random numbers are distributed:

hist(y_rgeom, # Plot of randomly drawn geom density breaks = 70, main = "")

**Figure 4: Application of rgeom Function.**

Compare the distribution of the random numbers shown in Figure 4 and the geometric density shown in Figure 1. Both figures show the geometric distribution.

## Video & Further Resources

Have a look at the following video of my YouTube channel. I’m explaining the R programming syntax of this article in the video.

*The YouTube video will be added soon.*

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

- 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

In addition, you may read the other RStudio posts of my homepage. I have released several tutorials about different types of distributions already.

Summary: In this article you learned how to **deal with the geom functions** in R programming. If you have any further questions, don’t hesitate to tell me about it in the comments.

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