CCRI: ENS: Collaborative Research: Enhancing R for Scalability and Deployment

数据分析软件系统是科学结构不可或缺的一部分。获取、处理和分析复杂数据的能力是从高能物理、天文学、化学和生物学等学科的核心。虽然科学家们可以利用多年来优化和改进的工具库，但现代科学数据集的快速演变特征带来了重大的新挑战。新的分析依赖于交互式计算机编程语言，这些语言是开放源代码的，允许快速、交互、数据探索。该项目旨在加强和改进R软件基础设施。R是拥有最大的免费和可重复使用的数据分析软件包集合的语言和环境。这个项目将帮助R适应不同领域研究人员不断变化的需求。这些技术改进体现在新的和更新的软件组件上，将为研究人员提供机会，为社区基础设施做出贡献，并使一些新的研究方向能够建立在R作为平台的基础上。最后，该项目的参与和外展计划涉及到用户和开发人员社区的外展，并创建了一条未来贡献者的管道。对R基础设施的拟议增强解决了四个关键需求，并促进了研究。在可伸缩性方面，R环境需要支持扩展计算以支持核外数据，也需要支持使用紧凑数据格式缩减计算。在部署方面，R语言需要支持多种部署格式。该项目设想通过支持将R的子集单独编译到本机二进制文件和WebAssembly来满足这一需求，其中前者将具有性能优势并且可以作为库链接，而后者将受益于在任何浏览器中运行的无所不在的Java脚本。在健壮性方面，R语言需要工具来验证和确认本机代码。这个项目将扩展现有的检查器，对R语义有详细的了解，并自动静态检查各种错误，并可选地为无法静态证明的情况插入运行时检查。在社区外展方面，需要一条培训潜在R开发人员的渠道。该项目将创建教育材料，并每年举办两次暑期学校。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Data analytics software systems are integral to the fabric of science. The ability to acquire, process, and analyze complex data is at the core of disciplines that range from high-energy physics, astronomy, chemistry, and biology. While scientists can exploit repositories of tools optimized and refined over the years, significant new challenges are posed by the rapidly evolving characteristics of modern scientific datasets. New analyzes rely on interactive computer programming languages that are open source and that allow rapid, interactive, data exploration. This project aims to strengthen and enhance the R software infrastructure. R is the language and environment with the largest collection of free and reusable data analytics software packages. This project will help R adapt to the evolving needs of researchers in diverse fields. The technical enhancements, embodied by new and updated software components, will open opportunities for researchers to contribute to the community infrastructure and enable a number of new research directions building on R as a platform. Lastly, this project's engagement and outreach plans address both outreach to user- and developer-communities and create a pipeline of future contributors.The proposed enhancements to the R infrastructure address four key needs and are enablers to research. In terms of scalability, the R environment needs support for both scaling up computation to support out-of-core data and scaling down computations with compact data formats. In terms of deployment, the R language needs to support multiple deployment formats. This project envisions addressing this need by supporting separate compilation of a subset of R to both native binaries and to WebAssembly where the former would have the benefit of performance and could be linked as a library, while the latter would benefit from the ubiquity of JavaScript, running in any browser. In terms of robustness, the R language needs tools to verify and validate native code. This project will extend existing checkers with a detailed understanding of R semantics and to automatically check for a variety of errors statically, and optionally insert run-time checks for cases which cannot be proved statically. In terms of community outreach, there is a need for a pipeline for training potential R developers. This project will create educational material and operate two yearly summer schools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.