Irregularities in F0 tracking such as sudden jumps or the halving/doubling of F0 often arise from consonantal perturbations, voice quality modulations, or environmental noise. These irregularities are typically visually apparent to the researcher, but fixing such errors is a time-intensive process even with algorithms that provide heuristic assessments of potential errors. In this paper we describe PitchMendR: an R-based interactive visualization tool to rapidly identify and fix irregularities. We discuss the main features of the tool and a proof-of-concept analysis of how it can be used to reduce noise in a dataset for statistical modeling.