This paper reports an evaluation of ChatGPT's capability of generating R programming language code from natural language input. A dataset specially designed for generating R program code was constructed with metadata to support scenario-based testing and evaluation of code generation capabilities in various usage scenarios of different levels of difficulty and different types of programs. The evaluation takes a multiple attempt process in which the tester tries to complete the code generation task through a number of attempts until a satisfactory solution is obtained or gives up after a fixed number of maximal attempts. In each attempt the tester formulates a natural language input to ChatGPT based on the previous results and the task to be completed. In addition to the metrics of average numbers of attempts and average amount of time taken to complete the tasks, the final generated solutions are then assessed on a number of quality attributes, including accuracy, completeness, conciseness, readability, well structuredness, logic clarity, depth of ex-planation, and coverage of parameters. Our experiments demonstrated that ChatGPT is in general highly capable of generating high quality R program code as well as textual explanations although it may fail on hard programming tasks. The experiment data also shows that human developers can hardly learn from experiences naturally to improve the skill of using ChatGPT to generate code.

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