Interoperable data exchange and reproducibility are increasingly important for modern scientific research. This paper shows how three open source projects work together to realize this: (i) the R project, providing the lingua franca for statistical analysis, (ii) the Open Geospatial Consortium's Sensor Observation Service (SOS), a standardized data warehouse service for storing and retrieving sensor measurements, and (iii) sos4R, a new project that connects the former two. We show how sos4R can bridge the gap between two communities in science: spatial statistical analysis and visualization on one side, and the Sensor Web community on the other. sos4R enables R users to integrate (near real-time) sensor observations directly into R. Finally, we evaluate the functionality of sos4R. The software encapsulates the service's complexity with typical R function calls in a common analysis workflow, but still gives users full flexibility to handle interoperability issues. We conclude that it is able to close the gap between R and the sensor web.