Mara Averick is a non-profit data nerd, NBA stats junkie, and most recently, tidyverse developer advocate at RStudio. She is the voice behind two very popular Twitter accounts, @dataandme and @batpigandme. Mara and I discussed sports analytics, how attending a cool conference can change the approach to your career, and how she uses Twitter as a mechanism for self-imposed forced learning. KO: What is your name, job title, and how long have you been using R?

Just last week we organised the 2nd rOpenSci ozunconference, the sibling rOpenSci unconference, held in Australia. Last year it was held in Brisbane, this time around, the ozunconf was hosted in Melbourne, from October 26-27, 2017. At the ozunconf, we brought together 45 R-software users and developers, scientists, and open data enthusiasts from academia, industry, government, and non-profits. Participants travelled from far and wide, with people coming from 6 cities around Australia, 2 cities in New Zealand, and one city in the USA.

A new rOpenSci package provides access to data to which users may already have directly contributed, and for which contribution is fun, keeps you fit, and helps make the world a better place. The data come from using public bicycle hire schemes, and the package is called bikedata. Public bicycle hire systems operate in many cities throughout the world, and most systems collect (generally anonymous) data, minimally consisting of the times and locations at which every single bicycle trip starts and ends.

David Smith is a Blogger and Community Lead at Microsoft. I had the chance to interview David last May at rOpenSci unconf17. We spoke about his career, the process of working remote within a team, community development/outreach and his personal methods for discovering great content to share and write about. KO: What is your name, job title, and how long have you been using R? DS: My name is David Smith.

A growing community of scientists from a variety of disciplines is moving the norms of scientific research toward open practices. Supporters of open science hope to increase the quality and efficiency of research by enabling the widespread sharing of datasets, research software source code, publications, and other processes and products of research. The speed at which the open science community seems to be growing mirrors the rapid development of technological capabilities, including robust open source scientific software, new services for data sharing and publication, and novel data science techniques for working with massive datasets.