Aggregate Daily Data to Month/Year Intervals

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In this post, we will explore a common data aggregation problem: converting daily data into monthly or yearly intervals. We will discuss various approaches and techniques using R programming language, specifically leveraging the lubridate and plyr packages.

Introduction

When working with time-series data, it is often necessary to aggregate data from a daily frequency to a higher frequency, such as monthly or yearly intervals. This can be particularly useful for visualizing trends over longer periods of time. In this article, we will demonstrate how to achieve this using R programming language.

The Problem

Suppose we have a dataset with daily observations, and we want to create a new dataset that summarizes the data by month or year. We are not interested in the individual daily values but rather the aggregated values for each month or year.

For example, consider a dataset temp with two columns: date (a POSIXct object representing dates) and amount (numeric values). We want to create a new dataset that summarizes the amount column by month/year using the date.

Solution Overview

To solve this problem, we will use R programming language and leverage two packages:

1. lubridate: A package for working with dates and times in R.
2. plyr: A package for data manipulation and analysis.

We will use a combination of these packages to round the date column down to the nearest month, create a new column representing the year/month interval, and then summarize the amount column by this new column.

Step-by-Step Solution

Step 1: Load Required Libraries

First, we need to load the required libraries in R:

library(lubridate)
library(plyr)

Step 2: Create a Sample Dataset

Next, let’s create a sample dataset temp with daily observations and two columns: date (POSIXct object) and amount (numeric values):

# Generate dates from today to 300 days later
dates <- seq(today(), by = "days", length.out = 300)
# Create random amounts between 0 and 100
amounts <- runif(300, min = 0, max = 100)

# Create the dataset with date and amount columns
temp <- data.frame(date = dates, amount = amounts)

Step 3: Round Down Dates to Nearest Month

We use the floor_date function from the lubridate package to round down the date column to the nearest month:

# Round down dates to the nearest month
temp$my <- floor_date(temp$date, "month")

Step 4: Summarize Amounts by Year/Month Interval

Finally, we use the ddply function from the plyr package to summarize the amount column by the new my column representing the year/month interval:

# Summarize amounts by year/month interval
summary_df <- ddply(temp, "my", summarise, amount = mean(amount))

Code Summary

The complete code for this solution is as follows:

library(lubridate)
library(plyr)

# Generate dates from today to 300 days later
dates <- seq(today(), by = "days", length.out = 300)
# Create random amounts between 0 and 100
amounts <- runif(300, min = 0, max = 100)

# Create the dataset with date and amount columns
temp <- data.frame(date = dates, amount = amounts)

# Round down dates to the nearest month
temp$my <- floor_date(temp$date, "month")

# Summarize amounts by year/month interval
summary_df <- ddply(temp, "my", summarise, amount = mean(amount))

print(summary_df)

Code Explanation

Here’s an explanation of each line of code:

• We load the required libraries: lubridate for date and time manipulation and plyr for data manipulation.
• We generate a sequence of dates from today to 300 days later using seq.
• We create random amounts between 0 and 100 using runif.
• We create the dataset with two columns: date (POSIXct object) and amount (numeric values).
• We use floor_date from the lubridate package to round down the date column to the nearest month.
• Finally, we summarize the amount column by the new my column representing the year/month interval using ddply from the plyr package.

Example Output

The code will produce a summary dataset with one row for each unique month/year interval. The amount column represents the mean amount for each month/year interval.

For example, if the input data looks like this:

The output will be:

This shows that the mean amount for February 2022 is 15 and the mean amount for February 2023 is 30.

Last modified on 2023-11-30