Loop Function on Timeseries Works on Small DF, but Not in Large DF - Error: C Stack Usage Too Close to the Limit

In this article, we will explore a common issue encountered when working with time series data and large data frames. The problem is that a recursive loop function may work fine on smaller data sets, but fails when dealing with larger ones due to stack overflow errors.

Introduction

When working with time series data, it’s often necessary to identify patterns or trends in the data. One common approach is to use a recursive loop function to find the next smallest value in a sequence. This technique works well on smaller data sets, but can fail when dealing with larger ones due to stack overflow errors.

The Problem

The problem arises because of how R handles recursion in functions. When a function calls itself recursively, it uses up memory on the call stack. If the recursive function is called too many times, the call stack becomes full, leading to a stack overflow error.

In this case, the find.next.smaller function is called recursively for each element in the vector of values. This can lead to a large number of recursive calls if the vector is very large.

The Solution

To solve this problem, we need to rethink our approach and avoid using recursion whenever possible. In this article, we will explore an alternative solution that uses a different technique to find the next smallest value in a sequence.

An Alternative Approach

One way to avoid using recursion is to use the which function to find the indices of values less than or equal to the current value, and then add 1 to the result. This approach avoids the need for recursive calls and can be more efficient for large data sets.

Here’s an example of how we can modify the find.next.smaller function to use this alternative approach:

find_nearest_value <- function(surge, time1, val1, times, vals) {
  if (!grepl("Surge", surge)) return(NA)
  idx <- which(vals <= val1)[1] + 1
  if (idx > length(times)) return(NA)
  return(idx)
}

This function uses the which function to find the indices of values less than or equal to val1, and then adds 1 to the result. If the resulting index is greater than the length of the times vector, the function returns NA.

Implementing the Solution

To implement this solution, we need to modify our original code to use the new find_nearest_value function instead of the recursive find.next.smaller function.

Here’s an example of how we can modify our original code:

df$Surge_start <- NA
df[which(df$Lead_Value - df$Value >= 2), "Surge_start"] <- paste("Surge", seq(1:length(which(df$Lead_Value - df$Value >= 2)), 1), sep="")
df$Date_time <- as.POSIXct(df$Date_time, format = "%Y-%m-%d %H:%M:%S")
df$Surge_value <- sapply(lapply(seq(as.numeric(strsplit(df$Surge_start, " ")[[1]][1], split = ".")[1]), function(x) x), function(x) {
  val1 <- df[df$Date_time == df$Date_time[seq(as.numeric(strsplit(df$Surge_value, " ")[1][1], split = ".")[1])], ]$Value[x]
  time1 <- df[df$Date_time == df$Date_time[seq(as.numeric(strsplit(df$Surge_value, " ")[1][1], split = ".")[1])], ]$Date_time[x]
  find_nearest_value(df$Date_time[seq(as.numeric(strsplit(df$Surge_value, " ")[1][1], split = ".")[1])], time1, val1, seq(as.numeric(strsplit(df$Surge_value, " ")[1][1], split = ".")[1]), as.numeric(strsplit(df$Surge_value, " ")[1][1]))
})

This code uses the find_nearest_value function to find the next smallest value in each sequence of values.

Conclusion

In this article, we explored a common issue encountered when working with time series data and large data frames. We saw how a recursive loop function may work fine on smaller data sets, but fails when dealing with larger ones due to stack overflow errors. We also saw an alternative approach that uses a different technique to find the next smallest value in a sequence, which avoids the need for recursion and can be more efficient for large data sets. By using this alternative approach, we can avoid common issues encountered when working with time series data and large data frames.

Last modified on 2024-02-19