 KoalaTea

# Moving Average in R

## Intro

When working with time series, we often want to view the average over a certain number of days. For example, we can view a 7-day rolling average to give us an idea of change from week to week. In this article, we will learn how to conduct a moving average in R.

## Data

Let’s load a data set of monthly milk production. We will load it from the url below. The data consists of monthly intervals and kilograms of milk produced.

df <- read.csv('https://raw.githubusercontent.com/ourcodingclub/CC-time-series/master/monthly_milk.csv')
df$month = as.Date(df$month)
head(df)
##        month milk_prod_per_cow_kg
## 1 1962-01-01               265.05
## 2 1962-02-01               252.45
## 3 1962-03-01               288.00
## 4 1962-04-01               295.20
## 5 1962-05-01               327.15
## 6 1962-06-01               313.65

Now, we convert our data to a time series object then to an zoo object to have access to many indexing methods explored below.

library(zoo)
##
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
##
##     as.Date, as.Date.numeric
df.ts = ts(df[, -1], frequency = 12, start=c(1962, 1, 1))
df.ts = as.zoo(df.ts)
head(df.ts)
## Jan 1962 Feb 1962 Mar 1962 Apr 1962 May 1962 Jun 1962
##   265.05   252.45   288.00   295.20   327.15   313.65

## Conducting a moving average

To conduct a moving average, we can use the rollapply function from the zoo package. This function takes three variables: the time series, the number of days to apply, and the function to apply. In the example below, we run a 2-day mean (or 2 day avg).

library(zoo)
ts.2day.mean = rollapply(df.ts, 2, mean)
head(ts.2day.mean)
## Jan 1962 Feb 1962 Mar 1962 Apr 1962 May 1962 Jun 1962
##  258.750  270.225  291.600  311.175  320.400  300.825

We can also plot the data over our orignal time series to see how the avg smoothed out the data.

plot(df.ts)
lines(ts.2day.mean, col = 'red')

Let’s do another example with a 7-day avg which is a common task in disease outbreaks and stocks.

ts.7day.mean = rollapply(df.ts, 7, mean)
head(ts.7day.mean)
## Apr 1962 May 1962 Jun 1962 Jul 1962 Aug 1962 Sep 1962
## 289.9286 290.5714 291.0214 286.9714 280.3500 271.0286

Again, let’s plot the data.

plot(df.ts)
lines(ts.7day.mean, col = 'red')

## Other Rolling Functions

You may have noticed from the above that we can do more than a rolling average with the rollapply function. We can actually apply any math function. Let’s run a couple of more examples, sum and median.

ts.7day.median = rollapply(df.ts, 7, median)
head(ts.7day.median)
## Apr 1962 May 1962 Jun 1962 Jul 1962 Aug 1962 Sep 1962
##   288.00   288.00   288.00   288.00   269.55   261.90
ts.7day.sum = rollapply(df.ts, 7, sum)
head(ts.7day.sum)
## Apr 1962 May 1962 Jun 1962 Jul 1962 Aug 1962 Sep 1962
##  2029.50  2034.00  2037.15  2008.80  1962.45  1897.20