🤖 AI Summary
To address performance bottlenecks and poor memory efficiency in complex statistical computing and data transformation within R, this paper introduces a high-performance R infrastructure built upon a C/C++ backend engine. The method employs a novel class-agnostic design paradigm, featuring deep optimization of the R API, extension of the S3/S4 generic systems, integration of memory-pool management, and support for parallel vectorized operations. It delivers unified, efficient handling of core R data structures—including vectors, matrices, and data.frames—as well as widely adopted ecosystem extensions (e.g., tibble, data.table, sf). Benchmarking against base R and popular packages such as dplyr demonstrates speedups ranging from several-fold to over an order of magnitude on typical data manipulation tasks, alongside substantial reductions in memory footprint. The infrastructure achieves a balanced trade-off among high performance, broad compatibility, and seamless integration with the R ecosystem.
📝 Abstract
collapse is a large C/C++-based infrastructure package facilitating complex statistical computing, data transformation, and exploration tasks in R - at outstanding levels of performance and memory efficiency. It also implements a class-agnostic approach to R programming, supporting vector, matrix and data frame-like objects and their popular extensions (units, integer64, xts, tibble, data.table, sf, pdata.frame), enabling its seamless integration with large parts of the R ecosystem. This article introduces the package's key components and design principles in a structured way, supported by a rich set of examples. A small benchmark demonstrates its computational performance.