I’m excited to announce a new package for accessing official statistics from the UK. nomisr is the R client for the Nomis database. Nomis is run by Durham University on behalf of the UK’s Office for National Statistics (ONS), and contains over a thousand datasets, primarily on the UK labour market, census data, benefit spending and general economic activity.
Our onboarding process, thatensures that packages contributed by the community undergo atransparent, constructive, non adversarial and open review process,involves a lot of work from many actors: authors, reviewers and editors;but how much work ? Managing the effort involved in the peer-reviewprocess is a major part of ensuring its sustainability and quality.
Our onboardingreviews,that ensure that packages contributed by the community undergo atransparent, constructive, non adversarial and open review process, takeplace in the issue tracker of a GitHub repository. Development of thepackages we onboard also takes place in the open, most often in GitHubrepositories.
On March the 17th I had the honor to give a keynote talk about rOpenSci’s package onboarding system at the satRday conference in Cape Town, entitled “Our package reviews in review: introducing and analyzing rOpenSci onboarding system”. You can watch its recording, skim through the corresponding slides or… read this series! What is rOpenSci onboarding?
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.
library(tidyverse)library(monkeylearn) This is a story (mostly) about how I started contributing to the rOpenSci package monkeylearn. I can’t promise any life flipturning upside down, but there will be a small discussion about git best practices which is almost as good 🤓. The tl;dr here is nothing novel but is something I wish I’d experienced firsthand sooner.
rOpenSci’s package review system (akaonboarding) is one of our keyactivities to improve quality and sustainability of scientific Rpackages. Theeditorial team are constantly working towards improving the experiencefor both authors and reviewers. After our first year, we surveyedauthors andreviewers whoparticipated in our onboarding process to help us better understandwhat’s working well and where there is room for improvement.
KO: What is your name, job title, and how long have you been using R? NR: I’m Noam Ross, I’m a Senior Research Scientist at EcoHealth Alliance, a non-profit that works at the intersection of conservation and health. I’ve done work in R for about 8 years, which is essentially from the start of graduate school (five years) and three years of work. KO: Did you love it [R] immediately? NR: No! And I’m mixed on whether I love it now.
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?
