Abstract
<p>Text-as-data methods aim to extract quantitative information from natural language. Traditionally,this was accomplished by using separate steps in each analysis, such as part-of-speech tagging, sentimentanalysis, and named entity recognition. Modern Large Language Models (LLMs) hold the promise ofdrastically simplifying this process, as they can be flexibly instructed to extract specific information.However, transferring data to LLM providers at scale and receiving directly usable data back has previously required application-specific programming. In contrast, the rapidcodeR package offers a highlyflexible approach to coding quantitative data from text at maximum speed and minimal cost. Here, wepresent an example of using the package to visualize international relations, directly coded from RussianUN speeches between 1946 and 2024. The results correspond well to formal alliance structures. Similaruse cases can involve coding of archival information, researching social media discourse, and extractingevent data from news sources.</p>