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#rprogramming — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #rprogramming, aggregated by home.social.

  1. Having gotten my head fully around R pipes, I feel I need to write an article about it before I forget the fiddly details:

    |>, %>%, with(), lambdas, %$%, ...

    there are a lot of clever tricks for edge cases! (The with() hack I should have figured out for myself though!)

    Also, %$% -- the exposition pipe -- why didn't any of you tell me about this one!?!? You're slacking!

    #rlang #datascience #stats #statistics #rprogramming #tidyverse #tidydata #Rpipes #pipes!

  2. My 2025 'Year in Review' blog post is now live! ✨ It was a year focused on deliberate, sustainable work while managing post-COVID recovery. I'm sharing insights on deep dives into APIs with `httr2`, the R Package Development Advent Calendar, and ongoing `ggseg` improvements. It’s all about creating durable outputs. Come read about my journey and what's next for 2026! What were your highlights? drmo.site/8NNsxa

  3. 🚨 Breaking News: R enthusiasts celebrate the groundbreaking achievement of finally getting R to run in a browser... by enabling #JavaScript. 🎉 Because, of course, nothing screams #innovation like making a heavyweight statistical tool rely on a glorified calculator script. 🤦‍♂️
    webr.sh/ #RProgramming #BrowserTech #DataScience #HackerNews #ngated

  4. 🚨 Breaking News: R enthusiasts celebrate the groundbreaking achievement of finally getting R to run in a browser... by enabling #JavaScript. 🎉 Because, of course, nothing screams #innovation like making a heavyweight statistical tool rely on a glorified calculator script. 🤦‍♂️
    webr.sh/ #RProgramming #BrowserTech #DataScience #HackerNews #ngated

  5. 🚨 Breaking News: R enthusiasts celebrate the groundbreaking achievement of finally getting R to run in a browser... by enabling #JavaScript. 🎉 Because, of course, nothing screams #innovation like making a heavyweight statistical tool rely on a glorified calculator script. 🤦‍♂️
    webr.sh/ #RProgramming #BrowserTech #DataScience #HackerNews #ngated

  6. 🚨 Breaking News: R enthusiasts celebrate the groundbreaking achievement of finally getting R to run in a browser... by enabling #JavaScript. 🎉 Because, of course, nothing screams #innovation like making a heavyweight statistical tool rely on a glorified calculator script. 🤦‍♂️
    webr.sh/ #RProgramming #BrowserTech #DataScience #HackerNews #ngated

  7. 🚨 Breaking News: R enthusiasts celebrate the groundbreaking achievement of finally getting R to run in a browser... by enabling #JavaScript. 🎉 Because, of course, nothing screams #innovation like making a heavyweight statistical tool rely on a glorified calculator script. 🤦‍♂️
    webr.sh/ #RProgramming #BrowserTech #DataScience #HackerNews #ngated

  8. Join us on November 30 from 4:00 pm to 5:00 pm CET (GMT+1) for a talk on using #Generative #AI in #R with Sharon Machlis @smach — Tech Journalist and Data Professional.

    In this session, Sharon will offer an accessible, high-level overview of what’s possible today — including how #large #language #models can help you write and improve your R code, as well as add #AI-driven features to your applications.

    RSVP 🔗 meetup.com/rladies-paris/event.

    #RStats #RStatsFr #Rprogramming #RLadies #Paris

  9. Using dplyr and ggplot2 in R can significantly streamline your data analysis process, making it easier to work with complex data sets.

    I have created a video tutorial in collaboration with Albert Rapp, where I demonstrate how to do this in practice: youtube.com/watch?v=EKISB0gnue4

    #coding #datavisualization #rprogramming #dataviz #statisticalanalysis #package #datastructure #ggplot2 #bigdata #tidyverse

  10. Tidy topological machine learning with TDAvec and tdarec by Jason Cory Brunson, Alexsei Luchinsky, Umar Islambekov

    Topological data analysis (TDA) is increasingly integrated into machine learning. Introducing two R packages—TDAvec and tdarec—to bridge TDA with the Tidymodels ecosystem, offering efficient persistent homology vectorization and tidy ML pipelines.

    Bug reports, feature requests, and code contributions welcome!

    r-consortium.org/posts/tidy-to

  11. If you're still using raw R outputs for presentations, it's time for an upgrade! Tools like gtsummary bring your statistical results to life, making them much more digestible for non-technical audiences.

    The visualization included here was originally shared in a post by Dr. Alexander Krannich. Thanks to Alexander for inspiring me to create this post.

    More details are available at this link: eepurl.com/gH6myT

    #statisticalanalysis #rprogramming #bigdata #coding

  12. Local regression is a non-parametric method for fitting smooth curves to data by applying multiple localized regressions. It is useful for uncovering non-linear relationships when the data’s exact form is unknown. Proper use of local regression can reveal trends in noisy data, but poor implementation might lead to misleading results.

    Image: en.wikipedia.org/wiki/Local_re

    More details: eepurl.com/gH6myT

    #database #package #bigdata #businessanalyst #tidyverse #datavisualization #rprogramming

  13. Local regression is a non-parametric method for fitting smooth curves to data by applying multiple localized regressions. It is useful for uncovering non-linear relationships when the data’s exact form is unknown. Proper use of local regression can reveal trends in noisy data, but poor implementation might lead to misleading results.

    Image: en.wikipedia.org/wiki/Local_re

    More details: eepurl.com/gH6myT

    #database #package #bigdata #businessanalyst #tidyverse #datavisualization #rprogramming

  14. Local regression is a non-parametric method for fitting smooth curves to data by applying multiple localized regressions. It is useful for uncovering non-linear relationships when the data’s exact form is unknown. Proper use of local regression can reveal trends in noisy data, but poor implementation might lead to misleading results.

    Image: en.wikipedia.org/wiki/Local_re

    More details: eepurl.com/gH6myT

    #database #package #bigdata #businessanalyst #tidyverse #datavisualization #rprogramming

  15. Local regression is a non-parametric method for fitting smooth curves to data by applying multiple localized regressions. It is useful for uncovering non-linear relationships when the data’s exact form is unknown. Proper use of local regression can reveal trends in noisy data, but poor implementation might lead to misleading results.

    Image: en.wikipedia.org/wiki/Local_re

    More details: eepurl.com/gH6myT

    #database #package #bigdata #businessanalyst #tidyverse #datavisualization #rprogramming

  16. Local regression is a non-parametric method for fitting smooth curves to data by applying multiple localized regressions. It is useful for uncovering non-linear relationships when the data’s exact form is unknown. Proper use of local regression can reveal trends in noisy data, but poor implementation might lead to misleading results.

    Image: en.wikipedia.org/wiki/Local_re

    More details: eepurl.com/gH6myT

    #database #package #bigdata #businessanalyst #tidyverse #datavisualization #rprogramming

  17. Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic data into clear, informative visualizations.

    Visualization: github.com/thackl/gggenomes

    Further details: statisticsglobe.com/online-cou

    #programming #package #dataanalytics #rprogramming #pythonprojects #bigdata #ggplot2

  18. Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic data into clear, informative visualizations.

    Visualization: github.com/thackl/gggenomes

    Further details: statisticsglobe.com/online-cou

    #programming #package #dataanalytics #rprogramming #pythonprojects #bigdata #ggplot2

  19. Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic data into clear, informative visualizations.

    Visualization: github.com/thackl/gggenomes

    Further details: statisticsglobe.com/online-cou

    #programming #package #dataanalytics #rprogramming #pythonprojects #bigdata #ggplot2

  20. Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic data into clear, informative visualizations.

    Visualization: github.com/thackl/gggenomes

    Further details: statisticsglobe.com/online-cou

    #programming #package #dataanalytics #rprogramming #pythonprojects #bigdata #ggplot2

  21. Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic data into clear, informative visualizations.

    Visualization: github.com/thackl/gggenomes

    Further details: statisticsglobe.com/online-cou

    #programming #package #dataanalytics #rprogramming #pythonprojects #bigdata #ggplot2

  22. In Bayesian inference, a credible interval is a range of values within which a parameter lies with a certain probability, given the observed data and prior beliefs. The image of this post (based on this Wikipedia image: en.wikipedia.org/wiki/Credible) represents a 90% highest-density credible interval of a posterior probability distribution.

    More details: eepurl.com/gH6myT

    #statistical #datasciencecourse #datascience #rprogramming #datastructure