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Are you thinking about submitting a package to rOpenSci’s open peer software review? Considering volunteering to review for the first time? Maybe you’re an experienced package author or reviewer and have ideas about how we can improve. Join our Community Call on Wednesday, September 13th. We want to get your feedback and we’d love to answer your questions! Agenda Welcome (Stefanie Butland, rOpenSci Community Manager, 5 min) guest: Noam Ross, editor (15 min) Noam will give an overview of the rOpenSci software review and onboarding, highlighting the role editors play and how decisions are made about policies and changes to the process....
As you might remember from my blog post about ropenaq, I work as a data manager and statistician for an epidemiology project called CHAI for Cardio-vascular health effects of air pollution in Telangana, India. One of our interests in CHAI is determining exposure, and sources of exposure, to PM2.5 which are very small particles in the air that have diverse adverse health effects. You can find more details about CHAI in our recently published protocol paper....
Take a look at the data This is a phrase that comes up when you first get a dataset. It is also ambiguous. Does it mean to do some exploratory modelling? Or make some histograms, scatterplots, and boxplots? Is it both? Starting down either path, you often encounter the non-trivial growing pains of working with a new dataset. The mix ups of data types - height in cm coded as a factor, categories are numerics with decimals, strings are datetimes, and somehow datetime is one long number....
Contributing to an open-source community without contributing code is an oft-vaunted idea that can seem nebulous. Luckily, putting vague ideas into action is one of the strengths of the rOpenSci Community, and their package onboarding system offers a chance to do just that. This was my first time reviewing a package, and, as with so many things in life, I went into it worried that I’d somehow ruin the package-reviewing process— not just the package itself, but the actual onboarding infrastructure…maybe even rOpenSci on the whole....
Last week, version 1.0 of the magick package appeared on CRAN: an ambitious effort to modernize and simplify high quality image processing in R. This R package builds upon the Magick++ STL which exposes a powerful C++ API to the famous ImageMagick library. The best place to start learning about magick is the vignette which gives a brief overview of the overwhelming amount of functionality in this package. Towards Release 1....
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