In October last year we wrote about the CRAN Checks API (https://cranchecks.info). Since then there have been four new major items introduced: documentation, notifications, search, and a new version of the cchecks R package. First, an introduction to the API for those not familiar.
Webinars and community calls are a great way to gather many people to discuss a specific topic, without the logistic hurdles of in-person events. But whether online or in-person, to reach the broadest audience, all events should work towards greater accessibility.
In March we held a Community Call discussing the maintenance of R packages.This call included a starting presentation by Julia Silge followed by a discussion featuring panelists with a wide variety of backgrounds: Elin Waring, Erin Grand, Leonardo Collado-Torres and Scott Chamberlain.
We have been working hard behind the scenes on the upcoming release of our new git package named gert, a joint effort from rOpenSci and the Tidyverse team. One of the main features of gert is the out-of-the-box authentication mechanism, which is provided via the new credentials package.
🔗rOpenSci HQ The rOpenSci team, together with ten expert community members, put together a post: When Field or Lab Work is not an Option - Leveraging Open Data Resources for Remote Research. We highlighted examples of how specific collections of packages are being used right now in fields as varied as archaeology and climate science and compiled a table of > 100 rOpenSci packages for access to open data.
This week we released a major new version of the rsvg package on CRAN. This package provides R bindings to librsvg2 which is a powerful system library for rendering svg images into bitmaps that can be displayed, or use for further processing in for example the magick package. The biggest change in this release is the R package on Windows and MacOS now includes the latest librsvg 2.48.4. This is a major upgrade;
The COVID-19 pandemic has dramatically impacted all of our lives in a very short period of time.Spring and summer are usually very busy as students prepare to go the field to engage in various data collection efforts.The pandemic has also disrupted these carefully planned activities as travel is suspended and local and remote field stations have closed indefinitely.A lost field season can be a major setback for a dissertation timeline and
Our website is based on Markdown content rendered with Hugo.Markdown content is in some cases knit from R Markdown, but with less functionality than if one rendered R Markdown to html as in the blogdown default.In particular, we cannot use the usual BibTex + CSL + Pandoc-citeproc dance to handle a bibliography.Thankfully, using the rOpenSci package RefManageR, we can still make our own bibliography from a BibTeX file without formatting
Thanks to a quite overdue update of Hugo on our build system 1 , our website can now harness the full power of Hugo code highlighting for Markdown-based content.What’s code highlighting apart from the reason behind a tongue-twister in this post title?In this post we shall explain how Hugo’s code highlighter, Chroma, helps you prettify your code (i.e. syntax highlighting ), and accentuate parts of your code (i.e. line
Whilst working on the blog guide, Stefanie Butland and I consolidated knowledge we had already gained, but it was also the opportunity to up our Rmd/Hugo technical game.Our website uses Hugo but not blogdown 1 to render posts: every post is based on an .md file that is either written directly or knit from an .Rmd file.We wanted to provide clear guidance for both options, and to stick to the well-documented Hugo way of e.g. inserting
The R language has become very popular among scientists and analystsbecause it enables the rapid development of software and empowersscientific investigation. However, regardless of the language used,data analysis is usually complicated. Because of various projectcomplexities and time constraints, analytical software often reflectsthese challenges. “What did I measure? What analyses are relevant tothe study? Do I need to transform the data?
