R packages discussed in these notes include duckdb (of course), dplyr, data.table, fst, xts, RSQLite, and vroom and a little Python Pandas by way of reticulate.
The notes exhibit a mild disdain for SQL. For a much more comprehensive discussion on difficulties with SQL, see these really interesting notes by Jamie Brandon: https://scattered-thoughts.net/writing/against-sql/. As an alternative to SQL I generally prefer dplyr.
These notes present several interesting, if somewhat eclectic, data-sciency examples. For more comprehensive and straight-up database-style performance comparisons, see the excellent work by H20 here: https://h2oai.github.io/db-benchmark/ (where both R’s data.table and DuckDB perform very well in general).
Also, you should check out https://github.com/pola-rs/polars for a remarkably high-performance new data frame implementation in Rust and geared to Python right now. This is the first data frame-like environment I have seen that really gives R’s data.table competition, aside from KDB+ of course.
Main overview:
The easy pieces:
A SQL rant born out of frustration while compiling these notes appears here: