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At rOpenSci we are developing on a suite of packages that expose powerful graphics and imaging libraries in R. Our latest addition is av – a new package for working with audio/video based on the FFmpeg AV libraries. This ambitious new project will become the video counterpart of the magick package which we use for working with images. install.packages("av") av::av_demo() The package can be installed directly from CRAN and includes a test function av_demo() which generates a demo video from random histograms....
Do you have code that accompanies a research project or manuscript? How do you review and archive that code before you submit a paper? Our next Community Call will present different perspectives on this hot topic, with plenty of time for Q&A. What’s the culture of the group around feedback and code collaboration? What are the use cases? What are some practices that can adopted? 🕘 Tuesday, October 16th, 9-10 AM PDT (find your timezone)...
🔗 Background Surveys are ubiquitous in the social sciences, and the best of them are meticulously planned out. Statisticians often decide on a sample size based on a theoretical design, and then proceed to inflate this number to account for “sample losses”. This ensures that the desired sample size is achieved, even in the presence of non-response. Factors that reduce the pool of interviews include participant refusals, inability to contact respondents, deaths, and frame inaccuracies....
Remember our recent post showing that one can wrangle Markdown files programmatically without regex? That tech note showed how to convert Markdown bodies to XML in order to extract information from them. Now, this post goes one step further and presents tinkr, a package for converting .md and .Rmd files to XML, editing them, and… writing them back as Markdown! 🔗 General tinkr workflow The goal of tinkr is to convert Markdown files to XML and back to allow their editing with xml2 (XPath!...
Hundreds of thousands of people in east Africa have been displaced and hundreds have died as a result of torrential rains which ended a drought but saturated soils and engorged rivers, resulting in extreme flooding in 2018. This post will explore these events using the R package smapr, which provides access to global satellite-derived soil moisture data collected by the NASA Soil Moisture Active-Passive (SMAP) mission and abstracts away some of the complexity associated with finding, acquiring, and working with the HDF5 files that contain the observations (shout out to Laura DeCicco and Marco Sciaini for reviewing smapr, and Noam Ross for editing in the rOpenSci onboarding process)....
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