Research infrastructures and services provide access to (meta)data via user interfaces and APIs. The more advanced services also support access through (Python, R, etc.) packages that users can use in computational environments. For scientific information as a particular kind of research data, the Open Research Knowledge Graph (ORKG) is an example of an advanced service that also supports accessing data from Python scripts. Since many research communities use R as the statistical language of choice, we have developed the ORKG R package to support accessing and processing ORKG data directly from R scripts. Inspired by the Python library, the ORKG R package supports a comparable set of features through a similar programmatic interface. Having developed the ORKG R package, we demonstrate its use in various applications grounded in life science and soil science research fields. As an additional key contribution of this work, we show how the ORKG R package can be used in combination with ORKG templates to support the pre-publication production and publication of machine-readable scientific information, during the data analysis phase of the research life cycle and directly in the scripts that produce scientific information. This new mode of machine-readable scientific information production complements the post-publication Crowdsourcing-based manual and NLP-based automated approaches with the major advantages of unmatched high accuracy and fine granularity.