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#R

3 posts3 participants0 posts today
Public
Sharon Machlis

Rainy day here, so I'm using #GenerativeAI to help teach me more about Kyle Walker's cool {mapgl} #RStats 📦 . And it's been pretty fun so far.

1) I uploaded mapgl help files and vignettes to both ChatGPT & Claude.
2) I used the CustomGPT AI Tutor Blueprint chatgpt.com/g/g-1rlItjngn-ai-t to create a good prompt for a mapGL tutor.
3) I added that prompt to Claude, and my tutor was ready!
Prompt: gist.github.com/smach/4c08c4d9

The {pkgprompt} and {rdocdump} #R 📦s help turn R docs into text.

Instead of starting with data and aesthetics, mapgl thinks like this: Map canvas first - you create a base map (like a Google Maps background) Add data as "sources" - your sf objects become data sources Add "layers" that reference those sources - these are like geoms but work differently Style with expressions - not aesthetics, but JavaScript-style expressions This is more like how a web developer builds an interactive map, which is why it's so powerful for complex visualizations. Before I show you the syntax, let me test your intuition: If you had to guess, how do you think you'd start building a mapgl map differently than a ggplot? What do you think comes first - the data or something else? And here's a follow-up: Given that mapgl creates interactive web maps, what do you think might be the equivalent of choosing your x and y variables in ggplot2?
#RSpatial
Public
K Belisario (she/her)

Looking for any examples of using a ShinyApp to allow researchers to select the data they would need for their specific research question. This will be for a large, multi-site project and I was hoping there would be a way to have the the dataset processed based on selection and then sent all securely in the format they need (e.g. long vs wide format). For this to be done securely, I’m thinking the app sends a request which is downloaded regularly to a local machine which can then automatically send out the data as needed using secured methods… But there’s probably a more eloquent way to do this! #R #AskFedi #Data #Database

Public
Sharon Machlis

The {ragnar} #RStats 📦 for doing RAG (retrieval augmented generation) with R has been submitted to CRAN 🎉 ragnar.tidyverse.org/news/inde

In other #GenAI R news: the new {kuzco} #RStats 📦 for computer vision works with local #LLMs in R for classification, recognition, sentiment, text extraction and alt-text generation. posit.co/blog/kuzco-computer-v

Happy to see #R #GenAI activity heating up

ragnar.tidyverse.orgChangelog
Public
Center for Health Data Science

💠 R FOR DATA SCIENCE 💠

Looking to take your #Rskills to the next level as a #UniversityofCopenhagen #researcher, employee, or #PhD student? We've got the #course for you!

💡 R for Data Science
🗓 16-18 June 2025
📍 Panum, University of Copenhagen

✏ Register here as an employee: lnkd.in/dyQx6K63
🖋️ Register here for ECTS credits as a PhD student: lnkd.in/da47HJ3f

#excel #rstudio #r #datascience #data #phd #research #datascience #UCPH

GIF
Public
Anna Pryslopska

Quick question for the #R community: I’m using #learnr for a tutorial, but every now and then chunks hang or refuse to execute and the "Run code" button is initially disabled. Has anyone diagnosed what causes this? Browser quirks, package versions, server limits? In the meantime, I came across @allison_horst 's fantastic tutorial which works perfectly online:
allisonhorst.shinyapps.io/dply
When I run it locally, I have the same problems.
Would love to hear your experiences or workarounds! #rstats

Public
Dr Mircea Zloteanu 🌼🐝

#statstab #332 {rix} Reproducible Environments with Nix

Thoughts: Docker, renv, groundhog? Now a new option!

#reproducibility #r #replication #openscience #openresearch

docs.ropensci.org/rix/

docs.ropensci.orgReproducible Data Science Environments with NixSimplifies the creation of reproducible data science environments using the Nix package manager, as described in Dolstra (2006) <ISBN 90-393-4130-3>. The included `rix()` function generates a complete description of the environment as a `default.nix` file, which can then be built using Nix. This results in project specific software environments with pinned versions of R, packages, linked system dependencies, and other tools. Additional helpers make it easy to run R code in Nix software environments for testing and production.
Public *
Lluís Revilla

There are 1153 aliases shared between CRAN and base #R. The most repeated are: plot, summary, predict, print, show, filter, plot-methods, coef, .onAttach, data.

Are #rstats package maintainers aware that when the user click on a link they might be redirected to a disambiguation page instead of the page they have in mind?

Did you know there are 16156 packages that share 36976 duplicated aliases with other packages on CRAN?

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