Many researchers are becoming more aware of the importance of reproducibility.Although reproducibility involves a diverse array of topics and tools, one rOpenSci package has gained considerable attention for enabling reproducible analysis pipelines in R: targets, by Will Landau. Why a targets workshop?
Three members of the rOpenSci team - Scott Chamberlain, Jenny Bryan, and Rich FitzJohn - as well as many community members will give talks at useR!2019. Many other package authors, maintainers, reviewers and unconf participants will be there too. Don’t hesitate to ask them about rOpenSci packages, software peer review, community, or just say hello if you’re looking for a friendly face. We’ve listed their talks for you.
You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our software packages can enable your research, or about our process for open peer software review and onboarding, how you can get connected with the community or tell us how we can help you do open and reproducible research. Where’s rOpenSci?
Motivation A few weeks ago, as part of the rOpenSci Unconference, a group of us (Sean Hughes, Malisa Smith, Angela Li, Ju Kim, and Ted Laderas) decided to work on making the UMAP algorithm accessible within R. UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique that allows the user to reduce high dimensional data (multiple columns) into a smaller number of columns for visualization purposes (github,
tl;dr : we propose three calls to action: Share your curricular materials in the open. Participate in the rOpenSci Education profile series. Discuss with us how you want to be involved in rOpenSci Educators’ Collaborative.
In the first post of this series, we sketched out some of the common challenges faced by educators who teach with R across scientific domains. In this post, we delve into what makes a “good” educational resource for teaching science with R. For instructors teaching sciences with R, there are a number of open educational resources that they can reuse, tailor to their own teaching style, or use to inspire them in creating their own materials.
Educators who teach science using R tend to face common pedagogical problems, regardless of their scientific domain. Yet instructors who teach with R often feel isolated at their institutions. They may be the only ones in their departments to teach using R. Even if there are others, the culture of collaboration around teaching is generally impoverished, unlike the rich culture of collaboration around research.
Data == knowledge! Much of the data we use, whether it be fromgovernment repositories, social media, GitHub, or e-commerce sites comesfrom public-facing APIs. The quantity of data available is trulystaggering, but munging JSON output into a format that is easilyanalyzable in R is an equally staggering undertaking.
You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our software packages can enable your research, or about our process for open peer software review and onboarding, how you can get connected with the community or tell us how we can help you do open and reproducible research. Where’s rOpenSci?
For the fourth and last day of project recaps from this year’s unconf, here is an overview of the next five projects. (Full set of project recaps: recap 1, recap 2, recap 3, recap 4.) In the spirit of exploration and experimentation at rOpenSci unconferences, these projects are not necessarily finished products or in scope for rOpenSci packages.
