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.

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?

🔗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.

Remember our recent post showing that one can wrangle Markdown filesprogrammatically without regex? Thattech note showed how to convert Markdown bodies to XML in order toextract information from them.

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

You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our software packages can enable your research, or about our process for open peer software review and onboarding, how you can get connected with the community or tell us how we can help you do open and reproducible research. 🔗Where’s rOpenSci?

Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out.

In the second post of the series where we obtained data fromeBird wedetermined what birds were observed in the county of Constance, and wecomplemented this knowledge with some taxonomic and trait information inthe fourth post of theseries.

A while ago weonboarded anexciting package, codemetarby Carl Boettiger. codemetar is an R specificinformation collector and parser for the CodeMetaproject. In particular, codemetar candigest metadata about an R package in order to fill the termsrecognized by CodeMeta. This meansextracting information from DESCRIPTION but also from e.g. continuousintegration ¹ badges in the README!

You might have read my blog post analyzing the social weather ofrOpenScionboarding,based on a text analysis of GitHub issues. I extracted text out ofMarkdown-formatted threads with regular expressions. I basicallyhammered away at the issues using tools I was familiar with until itworked! Now I know there’s a much better and cleaner way, that I’llpresent in this note. Read on if you want to extract insights abouttext, code, links, etc.

Thanks to the second post of the series where we obtained data fromeBird we knowwhat birds were observed in the county of Constance. Now, not allspecies’ names mean a lot to me, and even if they did, there are a lotof them.