Computational reproducibility is fundamental to scientific research, yet many published code supplements lack the necessary documentation to recreate their computational environments. While researchers increasingly share code alongside publications, the actual reproducibility of these materials remains poorly understood. In this work, we assess the computational reproducibility of 296 R projects using the StatCodeSearch dataset. Of these, only 264 were still retrievable, and 98.8% lacked formal dependency descriptions required for successful execution. To address this, we developed an automated pipeline that reconstructs computational environments directly from project source code. Applying this pipeline, we executed the R scripts within custom Docker containers and found that 25.87% completed successfully without error. We conducted a detailed analysis of execution failures, identifying reproducibility barriers such as undeclared dependencies, invalid file paths, and system-level issues. Our findings show that automated dependency inference and containerisation can support scalable verification of computational reproducibility and help identify practical obstacles to code reuse and transparency in scientific research.

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