The drake R package is a pipeline toolkit. It manages data science workflows, saves time, and adds more confidence to reproducibility. I hope it will impact the landscapes of reproducible research and high-performance computing, but I originally created it for different reasons. This post is the prequel to drake’s inception. There was struggle, and drake was the answer. Dissertation frustration My dissertation project was intense.

We’re very pleased to be introducing someone who needs no introduction in the R community.

One of the best things about learning R is that no matter your skill level, there is always someone who can benefit from your experience. Topics in R ranging from complicated machine learning approaches to calculating a mean all find their relevant audiences. This is particularly true when writing R packages.

[This interview occurred at the 2017 rOpenSci unconference] KO: What is your name, job title, and how long have you been using R? KR: My name is Karthik Ram I’m a research scientist at the University of California, Berkeley. I’m an ecologist by training but have been working in the ‘data science’ space for 15 years. My real introduction to R was during my PhD when I was a teaching assistant for an engineering class on data analysis.

Join our Community Call on Tuesday, January 30th (January 31 for our Australian friends) Nick Golding, 2017 rOpenSci Fellow, will talk about two R packages he has developed recently. zoon aims to promote open and reproducible research in ecological modeling by helping researchers share their code in a modular way and produce reproducible research artifacts.

KO: What is your name, your title, and how many years have you worked in R? JB: I’m Jenny Bryan, I am a software engineer at RStudio (still getting used to that title)., And I am on leave from being an Associate Professor at the University of British Columbia. I’ve been working with R or it’s predecessors since 1996. I switched to R from S in the early 2000s.

I’ve raved about the value of extending a personalized welcome to new community members and I recently shared six tips for running a successful hackathon-flavoured unconference. Building on these, I’d like to share the specific approach and (free!) tools I used to help prepare new rOpenSci community members to be productive at our unconference.

Are you new to version control and always running into trouble with Git?Or are you a seasoned user, haunted by the traumas of learning Git and reliving them whilst trying to teach it to others?Yeah, us too. Git is a version control tool designed for software development, and it is extraordinarily powerful.

The second rOpenSci OzUnConf was held in Melbourne Australia a few weeks ago. A diverse range of scientists, developers and general good-eggs came together to make some R-magic happen and also learn a lot along the way. Before the conference began, a huge stack of projects were suggested on the unconf GitHub repo. For six data-visualisation enthusiasts, one issue in particular caught their eye, and the ochRe package was born.

In May 2017, I helped run a wildly successful “unconference” that had a huge positive impact on the community I serve.