Missing data exploration: highlighting graphical presentation of missing pattern

Zhongheng Zhang


Functions shipped with R base can fulfill many tasks of missing data handling. However, because the data volume of electronic medical record (EMR) system is always very large, more sophisticated methods may be helpful in data management. The article focuses on missing data handling by using advanced techniques. There are three types of missing data, that is, missing completely at random (MCAR), missing at random (MAR) and not missing at random (NMAR). This classification system depends on how missing values are generated. Two packages, Multivariate Imputation by Chained Equations (MICE) and Visualization and Imputation of Missing Values (VIM), provide sophisticated functions to explore missing data pattern. In particular, the VIM package is especially helpful in visual inspection of missing data. Finally, correlation analysis provides information on the dependence of missing data on other variables. Such information is useful in subsequent imputations.