Licenses are an important topic within open source. Without licenses, information or code can be publicly available but not legally available for reuse or redistribution. The open source software community’s most common licenses are the MIT license or the GNU GPLv3. When you read the MIT or GNU license, you can see they are rather specific: and They aim to cover primarily software, not other forms of information such as, for example, data.

GitHub recently previewed ‘Copilot Workspace’, which aims to use generative AI to assist software developers. Rather than just giving in-line suggestions, as GitHub copilot does, workspace allows users to map out and execute entire projects. We got early preview access to the tool, so decided to see how well it performed for our needs in Epiverse-TRACE.

Nos complace anunciar el nuevo Grupo de Trabajo de Documentación Multilingüe del R Consortium.Este Grupo de Trabajo surgió luego de varias conversaciones durante el R Project Sprint 2023 y supervisará la implementación del soporte de documentación multilingüe en R y organizará los esfuerzos de traducción de la comunidad.Nuestro primer proyecto es el paquete (experimental) rhelpi18n que añade soporte en R para el uso de

We are happy to announce the brand-new R Consortium Multilingual Working Group.This Working Group came about after discussions during the R Project Sprint 2023 and will oversee the implementation of multilingual documentation support in R and organise community translation efforts.Our first project is the (experimental) rhelpi18n package, which adds multilingual documentation support!

At the end of June 2024, Posit released a beta version of its next-generation IDE for data science: Positron. This follows Posit’s general vision for language-agnostic data analysis software: RStudio PBC renamed itself to Posit PBC in 2022 to help move away from a pure R focus, and Quarto is pan-lingual successor to R Markdown.

We are continuing our post series on S3 object orientation and interoperability in R. We have previously discussed what makes a good S3 class and how to choose a good parent for it, as well as when to write or not write a custom method. We have highlighted in particular how classes inheriting from data.frames can simplify user experience because of familiarity, and reduce developer workload due to the pre-existing S3 methods.

I have recently published a series of blog posts on the reasons why one may want to start using object-oriented programming (and more specifically R S3 classes) to improve interoperability with other tools from the ecosystem. But there are still questions I have not addressed directly, even if they may have been implicitly included sometimes: what makes a good object class? What good practices in class &

List of packages I’ve found useful in my workflow during 2024 (so far) Plot spiralize: Visualize Data on Spirals tags : #plot [cran package link] https://CRAN.R-project.org/package=spiralize description from the author/vignette panelView: Visualizing Panel Data tags : #plot [cran package link] https://CRAN.R-project.org/package=panelView description from the

Developing dendroNetwork as a package was not a goal from the beginning, but looking back, I think that it should have been. I wish someone had suggested making a package to me much earlier. Why? Because of many things, but mostly: reproducibility and transparency. This enables others to also use the method and software.

A few days ago, my wife, a bunch of my kids, and I were huddled around a big wall map of the United States, joking about the relative unimportance of Rhode Island, the smallest state in the US. It’s one of the states I never ever think about: …and it’s just so small . Amid the joking, my wife came to Rhode Island’s defense by declaring that even though it’s so small, it has one of the highest proportions of coastline to land borders.