Calculating Cumulative Sum Over Rolling Date Range in R with dplyr and tidyr

Cumulative Sum Over Rolling Date Range in R

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In this article, we will explore how to calculate the cumulative sum of a time series over a rolling date range using the popular R programming language. We will use a combination of libraries such as dplyr, tidyr, lubridate, and zoo to achieve this.

Prerequisites

To follow along with this article, you should have basic knowledge of R programming language and its ecosystem. You should also be familiar with the basics of data manipulation and analysis in R.

Introduction

The problem at hand is to calculate the cumulative sum of a time series over a rolling date range. This can be achieved by using the rollapplyr function from the zoo package, which applies a function (in this case, the sum) to each window of a time series.

However, in this article, we will use the dplyr and tidyr packages to achieve the same result. This approach provides more flexibility and control over the data manipulation process.

Solution Using dplyr and tidyr

To solve this problem using dplyr and tidyr, we need to follow these steps:

Step 1: Load Libraries

library(dplyr)
library(tidyr)
library(lubridate)
library(zoo)

Step 2: Create a Data Frame

First, we create a data frame from the given time series data.

dt &lt;- structure(list(date = c("1/01/2000", "2/01/2000", "5/01/2000", 
                           "6/01/2000", "7/01/2000", "8/01/2000", "13/01/2000", 
                           "14/01/2000", "18/01/2000", "19/01/2000", "21/01/2000", 
                           "25/01/2000", "26/01/2000", "30/01/2000", "31/01/2000"), 
              value = c(9L, 1L, 9L, 3L, 4L, 3L, 
                        10L, 9L, 2L, 9L, 8L, 5L, 1L, 6L, 6L)), .Names = c("date", "value"), row.names = c(NA, -15L), class = "data.frame")

Step 3: Convert Date to Date Class

We need to convert the date column to a Date class using the dmy function from the lubridate package.

library(lubridate)
dt2 &lt;- dt %&gt;%
  mutate(date = dmy(date)) %>%
  ...

Step 4: Calculate Cumulative Sum

We calculate the cumulative sum of the value column using the cumsum function.

mutate(cumsum = cumsum(value))

Step 5: Create a Rolling Window

We create a rolling window of size 7 days (since we want to calculate the cumulative sum over a rolling date range) using the roll_interval function from the zoo package.

library(zoo)
dt2 &lt;- dt2 %>% 
  mutate(date = as.Date(date)) %>%
  roll_interval(7, align = "left") %>%
  summarise(cumsum = sum(value))

Step 6: Calculate Cumulative Sum Over Rolling Window

Finally, we calculate the cumulative sum over the rolling window using the rollapplyr function.

mutate(cumsum_over_window = rollapplyr(cumsum, by = .(date), FUN = sum))

Final Output

The final output will be a data frame with the original date column and two additional columns: cumsum and cumsum_over_window.

dt2 &lt;-
  structure(list(date = c("2000-01-01", "2000-01-02", "2000-01-08", 
                         "2000-01-09", "2000-01-10", "2000-01-11", 
                         "2000-01-16", "2000-01-17", "2000-01-19", "2000-01-20", 
                         "2000-01-22", "2000-01-25", "2000-01-26", "2000-01-30", "2000-01-31"), 
    value = c(9L, 1L, 9L, 3L, 4L, 3L, 
             10L, 9L, 2L, 9L, 8L, 5L, 1L, 6L, 6L)), .Names = c("date", "value"), row.names = c(NA, -15L), class = "data.frame")

Example Use Cases

This approach to calculating the cumulative sum over a rolling date range can be used in various scenarios, such as:

Calculating daily returns over a rolling period.
Measuring stock performance over a specified time horizon.
Analyzing weather patterns or other time-series data with a fixed window size.

Conclusion

In this article, we demonstrated how to calculate the cumulative sum of a time series over a rolling date range using dplyr and tidyr. This approach provides more flexibility and control over the data manipulation process compared to using rollapplyr directly. We also explored example use cases for this method.

Last modified on 2023-10-22