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

数据分析软件系统是科学结构中不可或缺的一部分。获取、处理和分析复杂数据的能力是高能物理、天文学、化学和生物学等学科的核心。虽然科学家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发展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，这些语言是开源的，允许快速、交互式的数据探索。该项目旨在加强和增强R软件基础设施。R是拥有最大的免费和可重用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。技术上的增强，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，并在R作为平台的基础上实现许多新的研究方向。最后，这个项目的参与和推广计划解决了用户和开发者社区的推广问题，并创建了一个未来贡献者的管道。对R基础架构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环境既需要支持向上扩展计算以支持核心外数据，也需要支持使用紧凑数据格式向下扩展计算。在部署方面，R语言需要支持多种部署格式。这个项目设想通过支持将R的一个子集分别编译为本地二进制文件和WebAssembly来解决这一需求，前者将获得性能上的好处，并且可以作为一个库链接，而后者将受益于JavaScript的无处不在，可以在任何浏览器中运行。在健壮性方面，R语言需要工具来验证和验证本机代码。该项目将通过对R语义的详细理解来扩展现有的检查器，并静态地自动检查各种错误，并可选择地为无法静态证明的情况插入运行时检查。在社区拓展方面，需要有一个管道来培训潜在的R开发人员。该项目将制作教育材料，并每年开办两所暑期学校。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.