When working with time series, missing data is an all too common problem. Especially when dealing with sensor data, all kind of sources of error(data recording, data transmission, data processing) can lead to missing values. These missing values quite often complicate later processing and analysis steps. Missing data visualization is the logical first step when encountering these problems. Visualizing the patterns in the missing data can provide information about the reasons for the missing values and give hints on how to best proceed with the analysis. In case it is decided to replace (impute) the missing values, visualizations are a useful tool to assess the imputation quality. This talk gives a short intro to the new/updated missing data plotting functions being introduced with the 3.1 version of the imputeTS CRAN package - while also mentioning useful functions from further CRAN packages.