arXiv:2604.23515v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) combines document retrieval with large language models (LLMs) to produce responses grounded in external evidence. While several R packages support core components of RAG workflows, integrated evaluation of RAG systems in R remains limited and is often conducted through Python-based tools, most notably the RAG assessment (RAGAS) framework. To address this gap, we introduce ragR, an R package that unifies document ingestion, embedding and vector storage, similarity-based retrieval, grounded generation, structured question-answer logging, and RAGAS-style evaluation within a single R-native workflow. Its evaluation module, ragR-RAGAS, uses an LLM as a judge to score four core RAGAS metrics: context precision, context recall, faithfulness, and answer relevance. To assess the credibility of these automated judgments, we compared ragR-RAGAS with two independent human annotators on three document collections. The human annotators demonstrated consistently high agreement, and ragR-RAGAS generally aligned with their judgments, although discrepancies were more pronounced for context precision and the identification of unsupported statements. By integrating RAG construction and evaluation within a reproducible workflow in R, ragR provides a practical framework for research, teaching, and moderate-scale experimentation on RAG systems entirely within the R ecosystem.