An Ode to Testing, my first review

  Charles T. Gray   | MARCH 13, 2018

To give you an idea of where I am in my R developer germination, I’d just started reading about testing when I received an email from @rOpenSci inviting me to review the weathercan package. Many of us in the R community feel like imposters when it comes to software development. In fact, as a statistician, it was a surprise to me when I was recently called a developer. In terms of formal computer science training, I took one subject in first year, with the appropriate initialism OOF.

Integrating data from weathercan

  Steffi LaZerte   | MARCH 6, 2018

I love working with R and have been sharing the love with my friends and colleagues for almost seven years now. I’m one of those really annoying people whose response to most analysis-related questions is “You can do that in R! Five minutes, tops!” or “Three lines of code, I swear!” The problem was that I invariably spent an hour or more showing people how to get the data, load the data, clean the data, transform the data, and join the data, before we could even start the “five minute analysis”.

The prequel to the drake R package

  Will Landau   | FEBRUARY 6, 2018

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 Sisyphus. My dissertation project was intense. The final computational challenge was to analyze multiple genomics datasets using an emerging method and its competitors.

5 Things I Learned Making a Package to Work with Hydrometric Data in R

  Sam Albers   | JANUARY 16, 2018

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. With an ever evolving R package development landscape (R, GitHub, external data, CRAN, continuous integration, users), there is a strong possibility that you will be taken into regions of the R world that you never knew existed.

Announcing a New rOpenSci Software Review Collaboration

  MaĆ«lle Salmon   |   Noam Ross   |   Scott Chamberlain   |   Karthik Ram   | NOVEMBER 29, 2017

rOpenSci is pleased to announce a new collaboration with the Methods in Ecology and Evolution (MEE), a journal of the British Ecological Society, published by Wiley press 1. Publications destined for MEE that include the development of a scientific R package will now have the option of a joint review process whereby the R package is reviewed by rOpenSci, followed by fast-tracked review of the manuscript by MEE. Authors opting for this process will be recognized via a mark on both web and print versions of their paper.

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