How to Efficiently Create Consistent Row Hashes for data.table in R

Описание: Learn how to generate consistent hashes for each row of a data.table in R, ensuring identical hashes when hashing rows individually or in bulk.
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This video is based on the question https://stackoverflow.com/q/79350168/ asked by the user 'FSU79' ( https://stackoverflow.com/u/8378731/ ) and on the answer https://stackoverflow.com/a/79350202/ provided by the user 'M. Galanakis' ( https://stackoverflow.com/u/9095398/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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The Challenge: Consistent Row Hashing in data.table

When working with large datasets in R using data.table, avoiding duplicate rows by hashing can be very effective. However, hashing rows individually might produce different results compared to hashing rows via functions like apply. This inconsistency stems from how apply converts rows to character vectors before hashing.

What Happens with apply?

For example, using apply(x, 1, digest) on a data.table:

Converts each row to a character vector

Drops the original data structure

Changes the data format, leading to different hash results than hashing a data.table slice

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

The Modern, Reliable Solution

To get consistent hashes for each row, hash the actual row as a one-row data.table. Here's how:

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

x[i] retrieves the i-th row as a data.table preserving the structure

digest() creates a hash based on the full row structure

sapply iterates efficiently over all rows

Additional Tips

Avoid using apply on a data.table for row-wise operations that depend on structure.

If hashing performance is a concern, and you only want to check for duplicates, compare rows using identical():

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

This directly compares rows without hashing, which can sometimes be faster.

Summary

Hash rows of a data.table by iterating over row indices and hashing each row slice.

Avoid apply on data.table rows since it converts rows to character vectors, affecting hash outcomes.

Use digest(x[i]) for consistent, reproducible, and efficient row hashing.

This approach ensures that you can reliably detect duplicates and manage large datasets without redundant storage.