In time series analysis, a **rolling average** represents the average value of a certain number of previous periods.

The easiest way to calculate a rolling average in R is to use the **rollmean()** function from the **zoo** package:

library(dplyr) library(zoo) #calculate 3-day rolling average df %>% mutate(rolling_avg = rollmean(values, k=3, fill=NA, align='right'))

This particular example calculates a **3**-day rolling average for the column titled **values**.

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

**Example: Calculate Rolling Average in R**

Suppose we have the following data frame in R that shows the sales of some product during 10 consecutive days:

#create data frame df <- data.frame(day=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), sales=c(25, 20, 14, 16, 27, 20, 12, 15, 14, 19)) #view data frame df day sales 1 1 25 2 2 20 3 3 14 4 4 16 5 5 27 6 6 20 7 7 12 8 8 15 9 9 14 10 10 19

We can use the following syntax to create a new column called **avg_sales3** that displays the rolling average value of sales for the previous 3 days in each row of the data frame:

**library(dplyr)
library(zoo)
#calculate 3-day rolling average of sales
df %>%
mutate(avg_sales3 = rollmean(sales, k=3, fill=NA, align='right'))
day sales avg_sales3
1 1 25 NA
2 2 20 NA
3 3 14 19.66667
4 4 16 16.66667
5 5 27 19.00000
6 6 20 21.00000
7 7 12 19.66667
8 8 15 15.66667
9 9 14 13.66667
10 10 19 16.00000
**

**Note**: The value for **k** in the **rollmean()** function controls the number of previous periods used to calculate the rolling average.

The **avg_sales3** column shows the rolling average value of sales for the previous 3 periods.

For example, the first value of **19.66667** is calculated as:

3-Day Moving Average = (25 + 20 + 14) / 3 = **19.66667**

You can also calculate several rolling averages at once by using multiple **rollmean()** functions within the **mutate()** function.

For example, the following code shows how to calculate the 3-day and 4-day moving average of sales:

**library(dplyr)
library(zoo)
#calculate 3-day and 4-day rolling average of sales
df %>%
mutate(avg_sales3 = rollmean(sales, k=3, fill=NA, align='right'),
avg_sales4 = rollmean(sales, k=4, fill=NA, align='right'))
day sales avg_sales3 avg_sales4
1 1 25 NA NA
2 2 20 NA NA
3 3 14 19.66667 NA
4 4 16 16.66667 18.75
5 5 27 19.00000 19.25
6 6 20 21.00000 19.25
7 7 12 19.66667 18.75
8 8 15 15.66667 18.50
9 9 14 13.66667 15.25
10 10 19 16.00000 15.00
**

The **avg_sales3** and **avg_sales4** columns show the 3-day and 4-day rolling average of sales, respectively.

**Additional Resources**

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

How to Plot Multiple Columns in R

How to Average Across Columns in R

How to Calculate the Mean by Group in R