🔗webmockr webmockr is an R library for stubbing and setting expectations on HTTP requests.It is a port of the Ruby gem webmock. webmockr works by plugging in to another R package that does HTTP requests. It currently only works with crul right now, but we plan to add support for curl and httr later.

rOpenSci’s mission is to enable and support a thriving community of researchers who embrace open and reproducible research practices as part of their work. Since our inception, one of the mechanisms through which we have supported the community is by developing high-quality open source tools that lower barriers to working with scientific data.

Earlier this month we released a new version of the tesseract package to CRAN. This package provides R bindings to Google’s open source optical character recognition (OCR) engine Tesseract. Two major new features are support for HOCR and support for the upcoming Tesseract 4. 🔗hOCR output Support for HOCR output was requested by one of our users on Github.

[This interview occurred at the 2017 rOpenSci unconference] SK: I’m Sean Kross, I’m the CTO of the Johns Hopkins Data Science Lab. Today I’m interviewing Julia Stewart Lowndes. Julia, what is your current preferred job title? JSL: I’m calling myself a marine data scientist - I’m the Science Program Lead for the Ocean Health Index.

For a fifth year running, we are excited to announce the rOpenSci unconference, our annual event loosely modeled on Foo Camp. rOpenSci unconferences have a rich history. You can get a feel for them by reading collected stories about people and projects from unconf17.

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.

🔗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.

🔗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.

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.