How to Calculate a Rolling Average in R (With Example)

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.