Generating the Same Random Sample Each Time in a Loop Using Sample

Generating the Same Random Sample Each Time in a Loop Using Sample_frac

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In this post, we will explore how to generate the same random sample each time in a loop when using sample_frac from the dplyr package. We will delve into the concept of lists and their usage with the dplyr package.

Introduction

The sample_frac function is used to randomly select rows from a data frame based on a specified proportion. However, by default, it creates a new data frame each time it’s called within a loop. This means that if we want to generate the same random sample multiple times in a loop, we will end up with different samples each time.

Problem and Solution

To solve this problem, we can create a list of data frames where each element is a randomly sampled version of our original data frame. We can do this by looping over a sequence of numbers, creating a new random sample for each iteration.

Method 1: Using a List

library(dplyr)

# Create the original data frame
tbl <- tibble(val = 1:50)

# Set the number of iterations
n <- 3

# Create an empty list to store our samples
lst1 <- vector('list', n)

# Loop over the sequence and create a new random sample for each iteration
for (i in seq_len(n)) {
    lst1[[i]] <- tbl %>%
      sample_frac(0.1)
}

print(lst1)  # This will print a list of three data frames, each with a different random sample

As you can see from the above code snippet, we are using vector('list', n) to create an empty list that we then populate with new random samples in the loop. This approach guarantees that we get the same random sample for each iteration.

However, this method is not only more verbose than necessary but also less efficient because it involves multiple operations: creating a vector of lists and then populating that vector with data frames.

Method 2: Using replicate

library(dplyr)

# Create the original data frame
tbl <- tibble(val = 1:50)

# Set the number of iterations
n <- 3

# Create a list to store our samples using replicate
lst1 <- replicate(n, tbl %>%
                   sample_frac(0.1), simplify = FALSE)

print(lst1)  # This will also print a list of three data frames, each with a different random sample

This method is more concise and efficient than the first one. We are utilizing replicate to create n copies of our original function call (which creates a new random sample), and then assign that result directly to our list.

Method 3: Using mutate and slice_sample

library(dplyr)

# Create the original data frame
tbl <- tibble(val = 1:50)

# Set the number of iterations
n <- 5

# Loop over a sequence and create a new random sample for each iteration using mutate and slice_sample
for (i in seq_len(n)) {
    lst1[[i]] <- tbl %>%
      mutate(rn = row_number()) %>%
      slice_sample(prop = 0.1)
}

print(lst1)  # This will print a list of five data frames, each with a different random sample

This method is somewhat different from the first two because we are using mutate and slice_sample instead of sample_frac. While it achieves the same result as our first approach (i.e., creating multiple versions of the same random sample), this one is a bit more complicated.

Conclusion

In conclusion, generating the same random sample each time in a loop using sample_frac requires some creative thinking. We’ve shown three different ways to achieve this goal, ranging from simple yet inefficient methods (like looping over the data frame) to more elegant and efficient approaches that utilize the power of lists and replicate.

Additional Considerations

While we have covered the basic problem of generating multiple random samples, there are some additional considerations worth mentioning:

Memory Efficiency: If you’re working with large datasets or need to generate a huge number of random samples, using replicate can be more memory-efficient than the other methods.
Performance: The choice of method also affects performance. For smaller datasets and fewer iterations, looping over the data frame might be faster due to less overhead from function calls and creation of intermediate results.

However, for larger datasets or numerous iterations, replicate often outperforms the simple loop approach due to its optimized C implementation under the hood.

Ultimately, choosing the best method depends on your specific needs, the size of your dataset, and the desired level of complexity in your code.

Last modified on 2024-03-23