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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....
We’re very pleased to be introducing someone who needs no introduction in the R community. Join us in welcoming Maëlle Salmon to rOpenSci as a Research Software Engineer (part time, working from Nancy, France). We’d like to formally introduce her here and share a bit about the kinds of things she’ll be working on. Maëlle did a B.Sc. in Biology with an emphasis on maths and quantitative work, two Masters degrees - one in Ecology and one in Public Health - and a Ph....
🔗 DBI What is DBI? DBI is an R package. It defines an interface to relational database management systems (R/DBMS) that other R packages build upon to interact with a specific relational database, such as SQLite or PostgreSQL. 🔗 NoSQL NoSQL databases are a very broad class of database that can include document databases such as CouchDB and MongoDB, key-value stores such as Redis, and more. They are generally not row-column relational stores though, though can include that....
🔗 The problem Text-mining - the art of answering questions by extracting patterns, data, etc. out of the published literature - is not easy. It’s made incredibly difficult because of publishers. It is a fact that the vast majority of publicly funded research across the globe is published in paywall journals. That is, taxpayers pay twice for research: once for the grant to fund the work, then again to be able to read it....
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....
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