Delaying a Function with Error Handling: A Step-by-Step Guide

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In this article, we’ll explore how to delay a function that throws an error. We’ll examine different approaches to handling errors in R and provide a solution using the try and if statements.

Understanding the Problem

When writing functions that interact with external sources of data, such as reading CSV files, it’s essential to account for potential errors. If an error occurs during the execution of a function, it can disrupt the entire workflow and cause unexpected results.

One approach to handle errors is by using try-catch blocks, which allow you to catch and handle specific exceptions raised by the function. However, in some cases, simply retrying the function after a delay might be more suitable.

Approach 1: Using Try-Catch Blocks with Delay

In your original code, you used a try-catch block within another try-catch block, which is laborious and doesn’t provide a clear second attempt mechanism. Instead, we’ll focus on a simpler approach using the try statement and an if-else structure.

Approach 2: Using Try-Catch Blocks with withRestarts (Not Recommended)

Although you mentioned that withRestarts didn’t work for your specific use case, it’s essential to understand how it works. The withRestarts function is designed to retry a function until it succeeds or reaches the specified maximum attempts. However, its documentation and examples can be somewhat opaque.

For your purposes, we’ll focus on using the try statement and an if-else structure, which provides more control over the error handling process.

A Step-by-Step Guide

Step 1: Understanding the Code Structure

Our goal is to create a function that takes a single argument (x) and attempts to execute it using the read.csv() function. If an error occurs during execution, we’ll catch the exception, delay for 10 seconds, and then retry the function.

Here’s an example of how we can implement this:

muhur <- function(x, tries = 2) {
  if (tries <= 0)
    stop("Too many tries")
  
  result <- try(read.csv(x))
  
  # Check if an error occurred
  if (inherits(result, "try-error")) {
    cat("Failed, trying again in 10 seconds...\n")
    Sys.sleep(10)
    
    # Recursively call the function with one less attempt
    muhur(x, tries = tries - 1)
  
  } else
    result
}

In this code:

• We define a function muhur that takes two arguments: x and tries. The tries argument defaults to 2 if not provided.
• We check if the number of attempts is less than or equal to 0. If so, we stop the function with an error message.
• We attempt to execute the read.csv() function using the try statement. If an error occurs, result will be a try-error object.
• We check if an error occurred by checking if result is of type “try-error”. If so, we delay for 10 seconds and recursively call the function with one less attempt.

Step 2: Handling Inherited Errors

In the previous example, we used the inherits() function to check if result is a try-error object. However, this approach can be unreliable, as it depends on the specific error type being caught.

A better approach is to use the .e argument within the try statement, which allows us to specify a custom error handler. In our case, we want to catch any errors that occur during execution and return NA.

Here’s an updated example:

muhur <- function(x, tries = 2) {
  if (tries <= 0)
    stop("Too many tries")
  
  result <- try(read.csv(x), .e = function(e) NA_character_)
  
  # Check if an error occurred
  if (inherits(result, "try-error")) {
    cat("Failed, trying again in 10 seconds...\n")
    Sys.sleep(10)
    
    # Recursively call the function with one less attempt
    muhur(x, tries = tries - 1)
  
  } else
    result
}

In this updated code:

• We use the .e argument within the try statement to specify a custom error handler. In our case, we return NA_character_ if an error occurs.
• The rest of the logic remains the same.

Step 3: Creating a DataFrame with Repeated Attempts

To demonstrate the effectiveness of our function, let’s create a sample dataframe with repeated attempts and analyze the results:

test <- data.frame(a = c("test1", "test2"))
test %>% group_by(a) %>% mutate(b = muhur(a))

In this example:

• We create a sample dataframe test with two rows, each containing a string value.
• We use the %>% operator to pipe the dataframe into our muhur() function and apply it to each row in the dataset.

The resulting dataframe will contain two rows, each with the original value from the a column, but replaced by the result of calling muhur() on that value.

By following these steps and using the try statement and an if-else structure, we’ve created a robust function that can handle errors and retry failed attempts. This approach provides more control over the error handling process and is better suited for our specific use case.

Last modified on 2025-02-02