How do you explore larger and larger datasets? Lots of interesting responses to this tweet (inc. lots of love for @VisiData). Some of my own thoughts below, particularly as we go from static charts to interactive visualization...

https://twitter.com/simonw/status/1572285367382061057

For basic queries & charts, we can import into a database, then query away and visualize the results. For data on a single machine, @duckdb is nice given its CSV/Parquet/etc support, scalability, and integration with Python, R, JS (even WASM!). observablehq.com/@cmudig/duckdb

For a decade+, grammar-of-graphics approaches (ggplot, Tableau, #d3js, Vega/Altair) have been a leading way to make visualizations. Beyond chart templates & low-level programming, are there compelling alternatives? Or does the future lie in abstractions on top of these grammars? There's exciting research work on new/extended grammars, including:
- probability expressions 📊 (mjskay.com/papers/chi2020…)
- responsive charts 📲 (

https://twitter.com/hyeok__kim/status/1506395769615568904

)
- animation 🕺 (idl.cs.washington.edu/papers/gemini2, gganimate, and new @vega_vis work coming to VIS'22 from @mitvis)