Understanding the Issue with slice_sample in dplyr

The slice_sample function in the dplyr package is a powerful tool for sampling data from a dataset. It allows users to randomly select a specified number of rows from each group in a dataframe, without replacement. However, when using this function, it’s not uncommon to encounter errors due to the limitations of the underlying statistical sampling process.

In this article, we’ll delve into the world of slice_sample and explore why it can throw an error when attempting to sample data larger than the population size, particularly in scenarios where replace = FALSE.

Background: How slice_sample Works

The slice_sample function uses a combination of random sampling and stratification to select rows from each group in the dataframe. The basic process works as follows:

1. For each group in the dataframe, it calculates the proportion of the desired sample size (n) relative to the population size.
2. If the desired sample size is greater than the population size, the function silently truncates the result to the population size.
3. If replace = FALSE, the function does not allow sampling with replacement; instead, it uses a stratified sampling approach.

However, in some cases, this stratification process can lead to errors when attempting to sample data larger than the population size.

The Issue: Sampling Larger Than the Population Size

The error occurs because slice_sample is designed to ensure that the selected rows are representative of the population. When sampling larger than the population size, it attempts to draw a random sample from each group without replacement. However, this can lead to an imbalance in the representation of certain groups, particularly if there are only a limited number of rows available.

For example, consider a scenario where we have a dataframe with two groups: Group A and Group B. Let’s say we want to sample 1000 rows from each group. If replace = FALSE, the function will not allow sampling with replacement. Instead, it will use stratified sampling to ensure that the selected rows are representative of both groups.

However, if there are only 500 rows available in one of the groups (e.g., Group B), the function may attempt to draw a random sample of 1000 rows from this group alone, leading to an error.

The Solution: Updating to dplyr v1.1.0

Fortunately, the issue has been addressed in dplyr version 1.1.0. According to the GitHub issues page, the problem was resolved by changing the behavior of slice_sample when sampling larger than the population size.

In this newer version, slice_sample will silently truncate the result to the population size when replace = FALSE, rather than attempting to draw a random sample from each group without replacement.

Here’s an example that demonstrates the corrected behavior:

birds <- tibble(Areas = c(rep("City", 40), rep("Beach", 60), rep("Forest",100)), 
                Species = sample(c("Robin", "Seagul", "Owl"), size = 200, replace = T))

# Select 30 rows from each Area
birds %>% group_by(Areas) %>% slice_sample(n = 30) %>% .$Areas %>% table

# Select 50 rows from each Area. Because there are only 40 city birds, those are selected (this gave an error in earlier dplyr versions)
birds %>% group_by(Areas) %>% slice_sample(n = 50) %>% .$Areas %>% table

Conclusion

In conclusion, the issue with slice_sample when sampling larger than the population size is a common problem that can lead to errors. However, by updating to dplyr version 1.1.0, users can take advantage of the corrected behavior and avoid these issues.

When working with large datasets or complex sampling scenarios, it’s essential to be aware of the limitations of statistical sampling processes and to choose the correct function for your specific needs.

Additional Considerations

In addition to the correction provided by dplyr version 1.1.0, there are several other considerations when using slice_sample:

• Population size: When sampling from a population, it’s essential to ensure that the population size is sufficient to support the desired sample size.
• Group sizes: The distribution of group sizes can significantly impact the accuracy of the sample. In cases where there are large differences in group sizes, alternative sampling methods may be necessary.
• Sampling with replacement: When sampling with replacement, it’s essential to understand the implications for data representation and statistical inference.

By being aware of these considerations and choosing the correct function for your specific needs, you can effectively use slice_sample to extract representative samples from large datasets.

Last modified on 2024-03-11