I write a lot of multi-script analysis flows and I number the scripts like the lines in BASIC scripts I wrote for the C64.
010_phenotype_definition.R
020_regression_analysis.R
030_result_reformatting.R
That way I can stick additional scripts in-between if I realize I need them.
Wrote this whole #emacs config because of the increasing presence of pay-to-use workbenches in #research. I find it more complete and flexible than #jupyter or #rstudio. Give it a go!
GitHub - lf-araujo/workbenchless: Single-file Emacs configuration for a powerful scientific Notebook system that works flawlessly over ssh.
https://github.com/lf-araujo/workbenchless
I love seeing all these #positronIDE posts with tips on settings etc. Really helpful stuff!
What I feel in missing, is a recording of how to efficiently work with the debugger. I'm very comfy in the #rstudio debugger, but can't seem to get the hang of Positron's.
The latest Rstudio 2024.12.0 adds a ProjectId field to .Rproj file. I don't see any comment on the release notes. Could someone at @Posit clarify how is this used and how it is calculated?
We got different ids on the same project by different team members.
It would help to decide if I add this for all the projects or not. #rstudio
If you use #RStats {targets} a lot, within #RStudio or #PositronIDE , you might want to try out the new(ish) `tar_assign` for workflows instead of `tar_plan` or making your own lists of targets.
Because `tar_assign` takes a wrapped code block (using `{ ... }` for multiline statements), the interpreter actually **sees** the variables properly, and can do tab completion on the variable names!
It's so nice.
https://docs.ropensci.org/tarchetypes/reference/tar_assign.html
Dear scientists, especially the R-community:
The NZ government wants to scrap all soc social sciences and humanities.
I probably don't have to explain why this is a totally bonkers milei-ish idea. (maybe just add the detail that Māori researchers will be overproportionally affected by these budget cuts, but this is probably anyway a feature of the plan)
I address the #RStats community explicitely, since R was developed in Aotearoa. And here in the Fediverse we are *many* R nerds!
So, @kjhealy or other kiwis, is there something the international academic community could do to express our thoughts and support your struggle?
I'm starting a new research project and I want to use #python to force myself to learn new skills. What is the current best practice for creating reproducible reports in the python world? I dislike jupyter notbooks with a passion, so I'm leaning towards #quartopub. What would be the best IDE to manage a project running on a remote machine? Would #rstudio server work for that or should I use something else?
Another resource for regular expressions in R: A Shiny app by Adam Spannbauer
App: https://spannbaueradam.shinyapps.io/r_regex_tester/
Blog post: https://adamspannbauer.github.io/2018/01/16/r-regex-tester-shiny-app/
Need help with regular expressions in R? Check out the RegExplain RStudio add-in by @grrrck
https://www.garrickadenbuie.com/project/regexplain/
RStudio code snippets save time by offering easy access to code you want to re-use -- things that may not be worth creating their own package.
Here's how to write and save them: https://www.infoworld.com/article/2260760/how-to-use-rstudio-code-snippets.html
[although from 2019, all should still work!]