Mastodawn

#AcademicChatter Hello, fellow academic chatters. I'm an experimental scientist. And I regularly have the problem of how to organize acquired data by different participants to a same experiment. I'd like to have some feedback of how other people do this. Not necessarily specific computer programmes (such as digital labbooks), but rather how-tos based on robust, basic things, such as text and csv files, folder structures etc. I'm often battling with the workflow which is including handwritten measurements, their digital counterparts, metadata for the measurements, codes for samples, all of these done by different people, but all will have to end in one final dataset, where I would like to be able to trace data back to the paper files, and their metadata. And all of this with not too much additional effort by everybody ;-) Have done this usually with some shared server space, but usually it ends up as something a bit difficult to get through. I'm now rather thinking about a system involving perhaps something based on #MarkDown or even #RMarkdown or #knitr etc. Any feedback much welcome 😁
#ReproducibleResearch

Annika Schiefner Feb 25

To the people analysing research data out there with R... I see #Quarto popping up everywhere and I am trying to figure out how it differs from #RMarkdown and why people are switching. So, have you used it? What are you using it for?

I don't like that it seems to add another layer of software and whenever a company tells me I should be using their format, that makes me suspicious... Please help!

Néhémie Feb 19

If you enjoyed #LoveDataWeek there is #LoveReplicationsWeek coming up on March 2-6.

There is a core program (online talks at 1pm CET) and you can submit your own events:

https://forrt.org/LoveReplicationsWeek/

#Replication #Reproducibility #rmarkdown #OpenScience

Love Replications Week

Johan Larsson Feb 12

I've made an autoformatter, linter, and LSP for Quarto, Pandoc, and RMarkdown documents! It's still very much a work-in-progress; only the formatter is reaching something resembling a mature state. Check it out at https://github.com/jolars/panache

#pandoc #markdown #quarto #rmarkdown

GitHub - jolars/panache: A formatter for Pandoc, Quarto, and RMarkdown

A formatter for Pandoc, Quarto, and RMarkdown. Contribute to jolars/panache development by creating an account on GitHub.

GitHub

Data Quine Jan 13

rOpenSci | All the Ways to Programmatically Edit or Parse R Markdown / Quarto Documents

Argh only just seeing this doc now, wish I'd found it a couple of months ago 🤣 I was generating static Rmd from one with dynamically generated chunks. I ended up using whisker pulling in external R code templates supplemented with knitr hooks to do some extra customisation during chunk processing.

https://ropensci.org/blog/2025/09/18/markdown-programmatic-parsing/

#RStats #RMarkdown #Quarto

All the Ways to Programmatically Edit or Parse R Markdown / Quarto Documents

Overview of programmatic ways to analyze and edit Markdown files: Markdown, R Markdown, Quarto, Hugo files, you name it.

Data Science Dec 31

Interactive resizing of picture and table content in Rmd and Quarto: https://r2resize.obi.obianom.com/index.html #rstats #quarto #rmarkdown

In-Text Resizer for Images, Tables and Fancy Resizable Containers in Shiny, Rmarkdown and Quarto Documents

Automatic resizing toolbar for containers, images and tables. Most suitable to include resize functionality in Markdown, Rmarkdown and Quarto documents.

kernpanik 🐾 🕊️ ☮ 🖖Dec 11

Performing some quick statistical analyses in classic #RStats and neatly “knitting” them into a PDF using #RMarkdown, #knitr, and #MacTeX #texLaTeX.

Call me old-fashioned, but I really enjoy this workflow.

#OpenSource #FOSS #statistics

Daniel E. Weeks Dec 1

R is wonderful: Creating a Supplement PDF full of summary & regression results tables, and estimated marginal mean plots, all completely auto-generated by the R Markdown code itself with no need for any manual post-modifications. 'gtsummary', 'ggplot2' rock! #Rstats #RMarkdown

Dee Toher Oct 24

Next week I get to present an #RStats talk on writing code to be run by people with no programming experience that requires some interactivity.

Have some examples of parameterised #RMarkdown documents; of #Shiny and using source to call more complex code without overwhelming them.

I also plan on concentrating on the importance of training these staff on the differences between errors, warnings and messages (which are unhelpfully similar in appearance in R).

Any other suggestions? (1/2?)

Felix Sep 13, 2025

Switching from #rmarkdown and #rdatatable to #quarto and #polars is a bit cumbersome. I just want to compile a document with tables to pdf.

If I print a polars table, I get the data type with it. If I convert it to pandas df, I get an index. If I set_tbl_hide_column_data_types, my strings get quotes. Is there no #knitr kable equivalent in #Python /Quarto?