Using the {arrow} and {duckdb} packages to wrangle medical datasets that are Larger than RAM 

For further learning, here are the links from the next to last slide: Arrow cheatsheet: raw.githubusercontent.com/rstudio/cheatsheets/master/arrow.pdf video intro: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-O42LUmJZPx0.html full workshop from useR!: arrow-user2022.netlify.app DuckDB website: duckdb.org R package: cran.r-project.org/web/packages/duckdb/index.html data.table website: rdatatable.gitlab.io/data.table dtplyr (a data.table translator): dtplyr.tidyverse.org

Really useful presentation, and timely for me. Personally I find data.table statements are greatly improved with just a little whitespace.

This talk blew my mind. Thank you very much!

Very good presentation. Thanks for sharing this

How about just using duckdb and SQL?

Wow, thank you for this illuminating presentation.

Wow. perfectly narrated.

Masterful deployment of the "Kobayashi Maru" reference. 🖖

Great presentation.

A neat question to answer. I'm using the duckplyr library and it's nice to not have to think about anything. It does make a strong argument for having a fast hard drive (an SSD is an order of magnitude faster than a traditional HDD, an M2 is an order of magnitude faster than that, and modern nvme drives are even faster).

Loved your presentation. Well done Sir!😊

You are a genius! Fantastic video! Thanks!

Perfectly summarizes my big data journey. Really good!

great video and interesting analysis use case!

Great talk. Concise and to the point.

Excellent presentation!