The Apache Tika parser is like the Babel fish in Douglas Adam’s book, “The Hitchhikers’ Guide to the Galaxy” ¹ . The Babel fish translates any natural language to any other. Although Tika does not yet translate natural language, it starts to tame the tower of babel of digital document formats. As the Babel fish allowed a person to understand Vogon poetry, Tika allows an analyst to extract text and objects from Microsoft Word.

The general struggle Something that will make life easier in the long-run can be the most difficult thing to do today . For coders, prioritising the long term may involve an overhaul of current practice and the learning of a new skill.

If you read my reflection #1 on rOpenSci Onboarding, then you know I see value in the Onboarding process. A LOT of value even. This post is about where that value lies. This question has important corollaries which I will explore here based on my experience as a reviewer of bowerbird: How is a package peer reviewer’s time best spent? When is the best time in a software package’s life cycle to undertake peer review?

That’s a lot alike Data Science, isn’t it? Hydrologic Processes evolve in space and time, are extremely complex and we may never comprehend them. For this reason Hydrologists use models where their inputs and outputs are measurable variables: climatic and hydrologic data, land uses, vegetation coverage, soil type etc.

Olfactory Coding Detecting volatile chemicals and encoding these into neuronal activity is a vital task for all animals that is performed by their olfactory sensory systems. While these olfactory systems vary vastly between species regarding their numerical complexity, they are amazingly similar in their general structure.

I am happy to say that the latest issue of The R Journal includes a paperdescribing rentrez,the rOpenSci package for retrieving data from the National Center for Biotechnology Information(NCBI). The NCBI is one of the most important sources of biological data. The centreprovides access to information on 28 million scholarly articles through PubMed and 250million DNA sequences through GenBank.

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.

I love working with R and have been sharing the love with my friends and colleagues for almost seven years now.

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.

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.