Creating Trailing Rolling Averages without NaNs at the Beginning of Output in R using Dplyr and Zoo Packages

Trailing Rolling Average without NaNs at the Beginning of the Output

Introduction

When working with time series data or data that has a natural ordering, it’s often necessary to calculate rolling averages. However, when dealing with nested dataframes, it can be challenging to ensure that the first few rows of the output are not filled with NaN (Not a Number) values. In this article, we’ll explore how to create a trailing rolling average without NaNs at the beginning of the output using the dplyr and zoo packages in R.

Background

The problem you’re facing arises because rollmean from the zoo package doesn’t have a partial=TRUE argument. This means that if you want to calculate the rolling mean for only a subset of rows, you’ll need to use another function like rollapplyr. Additionally, nested functions specified by formulas are not supported in this context.

Solution

To solve this problem, we can modify the code to use rollapplyr instead of rollmean, and define a custom function that calculates the rolling mean for only a subset of rows. Here’s how you can do it:

library(tidyverse)
library(zoo)

example <- 
  tibble("index" = c(rep(1, 5), rep(2, 5)), "data_a" = c(1:3, 1:2, 1:3, 1:2), "data_b" = c(2:4, 2:3, 2:4, 2:3)) %>%
  group_by(index) %>% 
  nest()

example_ra <- example %>% 
  mutate(roll_mean = map(data, ~ mutate(.x, across(
    .cols = where(is.numeric),
    .fns = function(x) rollapplyr(x, width = 5, FUN = mean, partial = TRUE)
  ))))

# Desired output
print(example_ra)

In the above code:

We use rollapplyr instead of rollmean.
We define a custom function using .fns = function(x) rollapplyr(x, width = 5, FUN = mean, partial = TRUE) that calculates the rolling mean for only a subset of rows.
We apply this function to each numeric column in the nested dataframe using across.

Why This Works

The key insight here is that we’re not trying to calculate the rolling average over the entire dataframe at once. Instead, we’re calculating it row by row, skipping the first few rows as needed.

By using partial = TRUE, we ensure that the function ignores any missing values when calculating the rolling mean. This prevents NaNs from appearing in the output.

The use of .cols and .fns allows us to define a custom function for each numeric column separately. This is necessary because nested functions specified by formulas are not supported in this context.

Conclusion

In this article, we explored how to create a trailing rolling average without NaNs at the beginning of the output using the dplyr and zoo packages in R. By using rollapplyr instead of rollmean, defining a custom function that calculates the rolling mean for only a subset of rows, and applying this function to each numeric column separately, we can achieve the desired output.

Additional Resources

Last modified on 2024-02-25