Data cleaning is acknowledged as a major bottleneck in data analysis. Common hurdles include data not in the state expected , and missing data. Two R packages currently under development are presented that assist in widening this bottleneck: ggmissing and visdat. ggmissing extends ggplot2 by allowing it to include missing values, and visdat provides simple functions for visualising whole dataframes, giving a birds eye view of the data. Examples of how these packages can be incorporated into a workflow are then described.