A new rOpenSci package provides access to data to which users may already have directly contributed, and for which contribution is fun, keeps you fit, and helps make the world a better place. The data come from using public bicycle hire schemes, and the package is called bikedata.

KO: What is your name, job title, and how long have you been using R? DS: My name is David Smith. I work at Microsoft and my self-imposed title is ‘R Community Lead’. I’ve been working with R specifically for about 10 years, but I’d been working with S since the early 90s.

One of rOpenSci’s aims is to build capacity of software users and developers and foster a sense of pride in their work. What better way to do that than to encourage you to participate in Hacktoberfest, a month-long celebration of open source software! It doesn’t take much to get involved Beginners to experts. Contributors and package maintainers welcome.

What is rrricanes Why Write rrricanes? There is a tremendous amount of weather data available on the internet. Much of it is in raw format and not very easy to obtain. Hurricane data is no different. When one thinks of this data they may be inclined to think it is a bunch of map coordinates with some wind values and not much else. A deeper look will reveal structural and forecast data.

En rOpenSci, creamos y curamos software para ayudar a quienes hacen ciencia en el ciclo de vida de los datos. Estas herramientas acceden, descargan, gestionan y archivan datos científicos de forma abierta y reproducible. Desde el principio, nos dimos cuenta de que esto sólo podía ser un esfuerzo comunitario.

At rOpenSci, we create and curate software to help scientists with the data life cycle. These tools access, download, manage, and archive scientific data in open, reproducible ways. Early on, we realized this could only be a community effort. The variety of scientific data and workflows could only be tackled by drawing on contributions of scientists with field-specific expertise. With the community approach came challenges.

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!

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.

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 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.