Calculating Rolling Averages in R: A Deeper Dive into Monthly and Daily Windows

When working with time series data, calculating rolling averages is a common task that can help identify trends and patterns. While packages like plyr and lubridate provide convenient functions for extracting months and days from date columns, creating a robust method to calculate rolling averages of past k months requires more attention to detail.

In this article, we will explore how to calculate the rolling average of past 1 month in R using both daily and monthly windows. We will delve into the code provided by the original poster, explain the underlying concepts, and provide additional insights on efficiency and alternative approaches.

Understanding Rolling Averages

A rolling average is a statistical function that calculates the average value of a dataset within a specified window of time or size. In this case, we are interested in calculating the rolling average of past 1 month, which means the window changes daily, incorporating new data points while excluding older ones.

The key challenge here is to handle varying lengths of months and ensure that the calculation accurately reflects the changing window.

The Original Poster’s Solution

Let’s analyze the code provided by the original poster:

install.packages(c("plyr", "lubridate"))
require(plyr); require(lubridate)

#read the daily data
daily = read.csv("daily_lumber_prices.csv")
price = daily$Open
date = daily$Date

#convert date to a usable format
date = strptime(date, "%d-%b-%y")
mon = month(date)
T = length(price)

#need to know when months change
change_month = rep(0,T)

for(t in 2:T){
  if(mon[t] != mon[t-1]){
    change_month[t-1] = 1
  }
}

month_avg = rep(0,T)
total = 0
days = 0

for(t in 1:T){
  if(change_month[t] == 0){
    #cumulative sums for each variable
    total = total + price[t] 
    days = days + 1
  }

  else{
    #need to include the current month in the calculation
    month_avg[t] = (total + price[t]) / (days + 1)
    #reset the variables
    total = 0
    days = 0
  }
}

The original poster uses a clever approach to account for varying lengths of months. They use a for loop to iterate through each data point and check if the month has changed (mon[t] != mon[t-1]). If the month changes, they set change_month[t-1] = 1, indicating that the previous day’s value should be included in the calculation.

For days with unchanged months, they accumulate the cumulative sum of prices and update the number of days. When a new month starts, they calculate the rolling average by dividing the accumulated total by the updated number of days plus one (to account for the current day).

Exploring Alternative Approaches

While the original poster’s solution works, there are alternative approaches that can provide more efficiency and flexibility.

One option is to use the roll function from the zoo package, which provides a simple way to calculate rolling averages:

library(zoo)

#...
month_avg <- rollMean(price, width = 1, fill = NA)

This code uses the rollMean function to calculate the rolling average of prices with a window size of 1. The fill argument is set to NA, which indicates that missing values should be filled with NA.

Another approach is to use the dplyr package, which provides a more concise way to perform data manipulation:

library(dplyr)

#...
month_avg <- daily %>%
  group_by(month = month(date)) %>%
  summarise(avg_price = mean(price))

This code groups the data by month and calculates the rolling average of prices within each group using the mean function.

Conclusion

Calculating rolling averages in R requires a combination of attention to detail, understanding of time series concepts, and efficient use of libraries like plyr, lubridate, zoo, and dplyr. The original poster’s solution provides a solid foundation for calculating monthly averages, but alternative approaches can offer more efficiency and flexibility.

By exploring different methods and libraries, data analysts and scientists can improve their skills in working with time series data and creating robust statistical functions.

Last modified on 2024-04-07