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Written by John MacKintosh on 2024-12-20 at 07:47

[#]rstats

Recommend me your go-to resources for working with (deeply) nested lists / list columns and purrr, that aren't R4DS or Advanced R.

Thank you

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Descendants

Written by Paul (Tex) Hewson on 2024-12-20 at 08:12

@johnmackintosh python. Seriously, if it's a deeply nested JSON structure I find that's easier. But in R itself this is my current goto resource

https://www.spsanderson.com/steveondata/posts/2024-10-29/

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Written by Josep Pueyo-Ros :rstats: on 2024-12-20 at 08:29

@texhewson @johnmackintosh If you choose to change language, I'd would recommend using node.js. JavaScript and JSON structures are designed to work together. You can flatten the data to one or several one-level jsons and then read them easily from R.

You can even use nodejs-polars and convert to tabular data exportable to csv.

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Written by Michael McCarthy on 2024-12-20 at 08:21

@johnmackintosh As someone also doing this work, I feel like there aren’t many resources on the topic aside from what you already mentioned. That said:

• {tidyr} has a rectangling article, although it’s similar to the R4DS chapter: https://tidyr.tidyverse.org/articles/rectangle.html

• tidyr::unpack() is like tidyr::unnest() but for df-columns

• I think some newer data formats like arrow can save files containing list columns, which seems handy

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Written by Michael McCarthy on 2024-12-20 at 08:26

@johnmackintosh For me the challenge working with this type of data is mostly conceptual and specific to a given data set. The tools are generally straightforward—it’s deciding how to apply them that can be less obvious…

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Written by Eric Book on 2024-12-20 at 14:34

@johnmackintosh I don't know of any other books or articles that stand out and haven't been mentioned. I'll just add some packages (and a tool).

listviewer, https://github.com/timelyportfolio/listviewer

nplyr, https://markjrieke.github.io/nplyr/

rrapply, https://jorischau.github.io/rrapply/

tidyjson, https://github.com/colearendt/tidyjson

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