A new R-package, coder, has been developed, peer-reviewed by rOpenSci, accepted by CRAN, and published in a paper by the Journal of Open Source Software (JOSS). In this blog post, I will explain why this package might be useful for (epidemiological/medical/health care related) research.Clinical mess Once upon a time, in countries not far from ours, there were MDs and nurses making up funny names for any diseases they encountered.

Make ¹ -like pipelines enhance the integrity, transparency, shelf life, efficiency, and scale of large analysis projects.With pipelines, data science feels smoother and more rewarding, and the results are worthy of more trust. targets install.packages("targets") The targets ² package is a new pipeline toolkit for R.It recently cleared software review, and it is now on CRAN.

Many people in our community actively contribute to rOpenSci projects.

In this post I will explain the history behind BaseSet then a brief introduction to sets, followed by showing what you can do with BaseSet.Brief BaseSet history I study diseases to try to find what causes them at a research institute associated with an hospital.Thanks to recent technological advances we can analyze many things from a single patient’s sample.Having so much information available can be overwhelming, making it difficult to find

The rOpenSci community is supported by our Code of Conduct with a clear description of unacceptable behaviors, instructions on how to make a report, and information on how reports are handled. We, the Code of Conduct Committee, are responsible for receiving, investigating, deciding, enforcing and reporting on all reports of potential violations of our Code.

Our community is our best asset. It’s so important to us, it’s in our mission statement. We recognize that communities are not inclusive by default; they require deliberate attention, including an enforceable Code of Conduct. rOpenSci is committed to providing a safe, inclusive, welcoming, and harassment-free experience for everyone.

In a year where it has been hard to pay attention to anything not critical to our day-to-day lives, you have continued to share your time, expertise, enthusiasm, and willingness to try things with us. Our staff of developers, researchers, and community builders work to create technical and social infrastructure to lower barriers to working with research data, and you, our community, continually help us push farther.

Introduction Few months ago, I embarked on a full stack spatial data project at work. The project kicked off amazingly, until I was almost backed to the wall when I discovered that some of the data sources were served via a GraphQL API. Before now, I haven’t worked with GraphQL. But, I have heard a lot about it and how amazing it is for querying data.

With this blog post, I show how to use the mcbette R packagein an informal way.A more formal introduction on mcbettecan be found in the Journal of Open Source Science ¹ .After introducing a concrete problem, I will show how mcbettecan be used to solve it. After discussing mcbette, I will conclude withwhy I think rOpenSci is important and how enjoyablemy experiences have been so far.The problem Imagine you are a field biologist.

Emerging viruses might be on everyone’s mind right now, but as an epidemiologist and disease ecologist I’ve always been interested in how and why pathogens move from animal hosts to humans.The current pandemic of the disease we call COVID-19 is caused by Severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2), a virus that has emerged from wildlife like SARS coronavirus and Middle East respiratory syndrome (MERS) coronavirus