Quantitative literacy is necessary to keep pace with the exponentially increasing magnitude of biological data and the complexity of statistical tools. However, statistical programming can cause anxiety in new learners and educators alike. In order to produce graduates that are well-prepared for quantitative research, overcoming the initial hurdles associated with statistical programming is a must. Often, valuable class time is dedicated to teaching introductory concepts of statistical programming, leaving instructors short on time. Here we present an introductory tutorial to statistical programming in the language R. Our tutorial is easily customizable, self-paced, and can be used in secondary through graduate level classrooms. Student questionnaire responses suggest that perceptions towards R became generally more favorable following an introduction to the program, with an increased likelihood of returning to R. Similar results were found across multiple formats for introducing statistical programming in R and suggest a tutorial-style introduction is as effective as a series of lectures and computer exercises for altering student perceptions towards statistical programming for graduate students. Our tutorial provides a self-paced introduction that covers basic programming in R and offers students an opportunity to learn the basic skills that often act as a roadblock to learning and utilizing more complex quantitative tools, while reserving class time for instruction.