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Calculating a Rolling Average in R is a way to observe trends over a defined period of time without being influenced by outlier data points. This can be done by using the rollmean() function in R. This function takes a numeric vector of data as its input and produces a vector of the same length that contains the rolling averages. An example of using this function can be seen by creating a data set of numbers, and then passing it as an argument to the rollmean() function. The resulting vector will contain the rolling averages for the data set.
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 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