NAVIGATING THE R PACKAGE UNIVERSE NAVIGATING THE R PACKAGE UNIVERSE
Earlier this month, I, along with John Nash, Spencer Graves, and Ludovic Vannoorenberghe, organized a session at useR!2017 focused on discovering, learning about, and evaluating R... NAVIGATING THE R PACKAGE UNIVERSE

Earlier this month, I, along with John Nash, Spencer Graves, and Ludovic Vannoorenberghe, organized a session at useR!2017 focused on discovering, learning about, and evaluating R packages. You can check out the recording of the session.

There are more than 11,000 packages on CRAN, and R users must approach this abundance of packages with effective strategies to find what they need and choose which packages to invest time in learning how to use. Our session centered on this issue, with three themes in our discussion.

Unification

John has been interested in working on wrappers, packages that call other, related packages for a common set of tasks. With a unified wrapper package, a user only has to learn one API but then can use many different implementations for a certain task. John has been particularly involved in numerical optimization techniques and presented possibilities there and beyond.

More generally, and as the session revealed in the breakout discussion, there are opportunities to merge either packages or their functionality. The key issues require, however, human cooperation and some give and take in a realm where egos can take precedence over the efficiency of the R ecosystem.

There were also suggestions that can be interpreted as the unification of the presentation of packages. Overlapping with the “guidance” and “search” themes, these ideas seek to provide selective presentations of packages.

Guidance

Julia explored resources that exist to guide users to packages for certains tasks. R users can turn to long-established resources like CRAN Task Views, or newer options under current development such as the packagemetricspackage or the CRANsearcher RStudio add-in. Julia organized a surveybefore useR about how R users learn about R packages that informed our discussion.

Moving forward

After the main presentation, we broke out into three smaller sessions focused on these topics for discussion and brainstorming. Both the main session and then our three following breakout sessions were well-attended. We are so happy about the participation from the community we saw, and hope to use people’s enthusiasm and ideas to move forward with some steps that will improve parts of the R ecosystem. In the coming weeks, look for three more blog posts (from me and the other contributors) on these three topics with more details and ideas on projects. Perhaps something will resonate with you and you can get involved!

 

Original Source

Julia Silge

Julia Silge

My background in the physical sciences and programming has given me the tools to apply sophisticated analytical techniques to complicated problems. I am a data scientist and analyst with an understanding of mathematics and statistical models. Analyzing, understanding, and communicating about data makes me happy and I am passionate about finding insights in data and building data products to meet the needs of an organization. I come from a background in physics and astronomy and have worked in academia and ed tech before moving into data science. My experience in the physical sciences and education has given me a solid foundation for using data to answer interesting questions, and then communicating those findings to decision makers. I work effectively in both independent and collaborative environments, I learn new skills and subjects quickly, and I have proven writing and speaking abilities.