Data analyst · Brisbane, Australia
Kyle Haynes
Data Integration team, Queensland Government Statistician’s Office (QGSO)
I work on data linkage — joining records across data sets that don’t share a clean key — which pulls in a fairly specific mix of theory: graph theory for the connections between records, and string matching for the fuzzy, almost-but-not-quite-identical values that connect them.
- data linkage
- graph theory
- string matching
- R & data.table
- spatial / addresses
What I work on
Most data linkage problems reduce to the same question: do these two records describe the same real-world thing? Answering that well means treating records and shared values as a graph — connected components, not pairwise rules — and being deliberate about how string similarity is measured, since names, addresses, and identifiers are rarely typed the same way twice.
- Data linkage — matching and deduplicating records across sources with no reliable shared key.
- Graph theory — connected components, clustering, and traversal as tools for resolving messy links.
- String matching — fuzzy and approximate matching for names, addresses, and free text.
Active projects
gnafr
R package for fuzzy matching Australian addresses against GNAF.
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fast.string
Fast string processing utilities used as building blocks for matching and cleaning text fields.
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graphfast
Limited, though, fast graphing methods
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jsdiffr
Diffs in R!
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