# Cumulative Mean in R (3 Examples)

In this article, Iâ€™ll show how to **compute the cumulative average** in the R programming language.

The tutorial consists of the following information:

Itâ€™s time to dive into the exemplifying R code:

## Creation of Example Data

The first step is to create some example data:

x <- c(5, 5, 1, 3, 9, 2) # Create example data x # Print example data # [1] 5 5 1 3 9 2 |

x <- c(5, 5, 1, 3, 9, 2) # Create example data x # Print example data # [1] 5 5 1 3 9 2

The previous output of the RStudio console shows that our example data is a numeric vector with a length of six.

## Example 1: Calculate Cumulative Mean Using cumsum() & seq_along() Functions

Example 1 shows how to get the cumulative mean of our example vector using the cumsum and seq_along functions provided by the basic installation of the R programming language.

Have a look at the following R code and its output:

x_cm1 <- cumsum(x) / seq_along(x) # Apply cumsum & seq_along x_cm1 # Print cumulative mean # [1] 5.000000 5.000000 3.666667 3.500000 4.600000 4.166667 |

x_cm1 <- cumsum(x) / seq_along(x) # Apply cumsum & seq_along x_cm1 # Print cumulative mean # [1] 5.000000 5.000000 3.666667 3.500000 4.600000 4.166667

As you can see, we have created a new vector object called x_cm1 that consists of six numeric values. Each of these values is the cumulative mean corresponding to the elements of our input vector.

## Example 2: Calculate Cumulative Mean Using cummean() Function of dplyr Package

In Example 2, Iâ€™ll explain how to use the cummean function of the dplyr package to compute the cumulative average of a numeric vector.

We first need to install and load the dplyr package to R, if we want to use the functions that are contained in the package:

install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr |

install.packages("dplyr") # Install dplyr package library("dplyr") # Load dplyr

Next, we can apply the cummean function to create a new vector of cumulative averages:

x_cm2 <- cummean(x) # Apply cummean x_cm2 # Print cumulative mean # [1] 5.000000 5.000000 3.666667 3.500000 4.600000 4.166667 |

x_cm2 <- cummean(x) # Apply cummean x_cm2 # Print cumulative mean # [1] 5.000000 5.000000 3.666667 3.500000 4.600000 4.166667

The previous R code has created another vector object called x_cm2 that contains exactly the same output as Example 1. However, this time we have used the cummean function of the dplyr package.

## Example 3: Add Cumulative Mean as Column to Data Frame

In this example, Iâ€™ll show how to create a data frame containing our original values and the cumulative mean of these values in a new variable.

For this task, we can apply the data.frame function and the cummean function of the dplyr package as shown below:

data <- data.frame(x, cm = cummean(x)) # Create data frame with cumulative mean data # Print data frame with cumulative mean |

data <- data.frame(x, cm = cummean(x)) # Create data frame with cumulative mean data # Print data frame with cumulative mean

Table 1 illustrates the data frame that we have just created. Our new data frame consists of two columns. The column x contains our original input values and the column cm contains the cumulative means that correspond to our input vector.

## Video, Further Resources & Summary

I have recently published a video on my YouTube channel, which shows the R syntax of this post. You can find the video below:

*The YouTube video will be added soon.*

In addition, you might read the related articles that I have published on this website:

- cumall, cumany & cummean R Functions of dplyr Package
- cumsum Function in R
- Cumulative Frequency & Probability Table
- Calculate Moving Average, Maximum, Median & Sum of Time Series
- R Programming Tutorials

This article has demonstrated how to **calculate the rolling average** in the R programming language. Please let me know in the comments section, in case you have additional questions.

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