Скачать или смотреть Mastering Random Sampling with Running Length Encoding (RLE) in R

Discover how to achieve effective random sampling without replacement using Running Length Encoding (RLE) in R, along with a practical solution that fits various scenarios.
---
This video is based on the question https://stackoverflow.com/q/63625057/ asked by the user 'dugar' ( https://stackoverflow.com/u/2554748/ ) and on the answer https://stackoverflow.com/a/63625378/ provided by the user 'pseudospin' ( https://stackoverflow.com/u/13203971/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Random sampling using running length encoding (rle)

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Mastering Random Sampling with Running Length Encoding (RLE) in R

Random sampling is a fundamental technique in data analysis that allows you to extract a subset of observations from a larger set. However, when it comes to running length encoding (RLE) in R, achieving random sampling without replacement can be a bit tricky. In this guide, we’ll explore the challenge of sampling from an RLE encoded vector and provide a solution to accomplish this effectively.

The Problem: Random Sampling with RLE

Suppose you have a vector represented using RLE, like so:

[[See Video to Reveal this Text or Code Snippet]]

When you try to sample values from this RLE object using:

[[See Video to Reveal this Text or Code Snippet]]

You might expect to get a sample like [1] 1 1, but what you actually retrieve might not work as intended. The result can be misleading as it may fail to represent true samples from the original data due to the nature of RLE.

Expected Output Example

Using straightforward sampling on a regular vector would look like this:

[[See Video to Reveal this Text or Code Snippet]]

The challenge is making this functionality work in the context of RLE.

The Solution: Custom Sampling Function for RLE

To overcome this issue, we can create a custom function called sample_rle that handles random sampling on RLE objects properly. Here's how you can achieve that:

Implementing the sample_rle Function

Create the Function: This function will take an RLE object as an argument.

Use findInterval: To determine the indices of the sampled values based on the lengths and cumulative sums of the RLE components.

Here's the complete implementation:

[[See Video to Reveal this Text or Code Snippet]]

Sampling with the Created Function

Now that we have our sampling function, let's see it in action:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Functionality

x$values: This component provides the unique values of the RLE object.

sample(sum(x$lengths), ...): This generates random indices based on the total count of the original vector.

cumsum(x$lengths): This calculates the cumulative sum of the lengths of the RLE sequences, setting up the intervals for sampling.

Conclusion

Using the custom sample_rle function, you can effectively perform random sampling without replacement on RLE encoded variables in R. This ensures that the sampled outputs reflect the original data accurately, giving you flexibility and power in your data analysis work.

By following the steps outlined in this guide, you can enhance your skills in R and efficiently handle RLE structures when it comes to sampling. Happy coding!