--- title: Characteristic NYC Neighborhood Complaints | daily.dev description: An R package wraps NYC's 311 complaints dataset, from 2010 to 2025, as a DuckDB-backed parquet file with about 43 million rows. The analysis narrows to... canonical: https://daily.dev/posts/characteristic-nyc-neighborhood-complaints-tjgqzgelj twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Characteristic NYC Neighborhood Complaints | daily.dev og:description: An R package wraps NYC's 311 complaints dataset, from 2010 to 2025, as a DuckDB-backed parquet file with about 43 million rows. The analysis narrows to... og:url: https://daily.dev/posts/characteristic-nyc-neighborhood-complaints-tjgqzgelj og:image: https://api.daily.dev/og/posts/tJgqzGElJ.png og:image:alt: Characteristic NYC Neighborhood Complaints og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Characteristic NYC Neighborhood Complaints **[R-bloggers](https://daily.dev/sources/rbloggers)** · 13 min read · 0 upvotes · 0 comments ## Summary An R package wraps NYC's 311 complaints dataset, from 2010 to 2025, as a DuckDB-backed parquet file with about 43 million rows. The analysis narrows to 2022-2025 records (about 13.5m) and counts distinct complaining addresses per category within each Neighborhood Tabulation Area, yielding roughly 4.6 million address-category pairs from about one million addresses. The most distinctive complaint category (the one most exceeding its citywide share) is mapped and labeled for each neighborhood, both broadly (like 'Sanitation and Dumping') and at a fine-grained level (like 'Traffic: Drag Racing' or 'Lost Property in Taxi or For-Hire Vehicle: Wallet'). ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How can I query a large parquet file efficiently from R? Use DuckDB as an in-memory database engine on top of the parquet file and write tidy dplyr-like queries that DuckDB executes directly, via a package such as duckplyr. This approach suits workloads that query a large dataset many different ways rather than repeatedly updating records, and it scales to datasets with tens of millions of rows, such as a 43-million-row NYC 311 complaints table. _Developers evaluating R and DuckDB for large dataset queries can find more workflow write-ups like this on daily.dev._ ### How many service request records does NYC's 311 dataset contain? The full NYC 311 complaints dataset spans 2010 to 2025 and contains about 43 million rows, each representing one service request. A narrower slice covering 2022 to 2025 alone totals about 13.5 million records, which were used to compute roughly 4.6 million address-category complaint pairs from about one million unique addresses. _Anyone building civic or open-data analyses can track dataset-scale projects like this via daily.dev._ ## Similar posts on daily.dev - [New York City Hexmaps](https://daily.dev/posts/new-york-city-hexmaps-foysajhwi) · R-bloggers · 0 upvotes · 0 comments - [I’m walking here\! 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