Reasons for using the set.seed function

Understanding and Using set.seed() for Reproducible Randomness in R

Explore why the set.seed() function is crucial in R programming for ensuring reproducibility in analyses involving random numbers, from simulations to machine learning.

In the world of data science and statistical computing, particularly within the R programming language, reproducibility is paramount. When working with algorithms that involve randomness—such as simulations, sampling, or machine learning model initialization—the ability to get the exact same results every time is not just a convenience, but a necessity for debugging, peer review, and consistent research. This is where the set.seed() function comes into play. This article will delve into the purpose of set.seed(), how it works, and why it's an indispensable tool for any R user.

The Nature of 'Randomness' in Computing

Computers are deterministic machines. They follow instructions precisely. True randomness, as understood in physics, is difficult to achieve computationally. Instead, computers generate what are known as pseudo-random numbers. These numbers are produced by deterministic algorithms that start from an initial value, called a 'seed'. Given the same seed, the algorithm will always produce the exact same sequence of 'random' numbers. Without setting a seed, R typically uses the current system time or other system-specific values to initialize its random number generator, leading to different sequences each time the code is run.

# Without set.seed()
run1 <- sample(1:100, 5)
print(run1)

run2 <- sample(1:100, 5)
print(run2)

# Run this multiple times, and you'll likely get different results for run1 and run2 each time.

Demonstrating non-reproducible random sampling without set.seed()

How set.seed() Ensures Reproducibility

The set.seed() function allows you to explicitly provide the initial seed for R's pseudo-random number generator. When you call set.seed() with a specific integer value, you are telling R to start its random number sequence from that exact point. Consequently, any subsequent calls to random number generation functions (like sample(), rnorm(), runif(), etc.) will produce the same sequence of numbers, provided the code is executed in the same order. This is incredibly powerful for ensuring that your analyses are consistent and verifiable.

# With set.seed()
set.seed(123)
reproducible_run1 <- sample(1:100, 5)
print(reproducible_run1)

set.seed(123) # Call set.seed() again with the same value
reproducible_run2 <- sample(1:100, 5)
print(reproducible_run2)

# reproducible_run1 and reproducible_run2 will be identical.
# If you run the entire script again, they will still be identical to previous runs.

Using set.seed() to achieve reproducible random sampling

Practical Applications and Best Practices

The utility of set.seed() extends across various domains in R programming:

1. Statistical Simulations: When running Monte Carlo simulations or bootstrapping, set.seed() ensures that your simulation results are consistent across different runs or when shared with colleagues.
2. Machine Learning: Many machine learning algorithms, especially those involving stochastic gradient descent or random forest constructions, rely on random initialization or sampling. Setting a seed ensures that model training results are reproducible.
3. Data Splitting: When splitting data into training and testing sets (e.g., using createDataPartition from caret or sample.split from caTools), set.seed() guarantees that the same data points are assigned to each set every time.
4. Debugging: If you encounter an issue in code that involves randomness, setting a seed allows you to repeatedly trigger the exact same 'random' sequence that led to the bug, making it much easier to diagnose and fix.

Best Practices:

• Always place set.seed() at the beginning of your script or function where random numbers are first generated.
• If you have multiple independent random processes, you might use different seeds for each, or reset the seed before each process if you need to ensure their independence from each other's sequence.
• Document the seed you used, especially in research papers or shared code, to facilitate reproducibility by others.

Flowchart demonstrating the effect of set.seed() on reproducibility