If you do data analysis, you encounter missing data. Missing data upsets data analysis workflow because you have to make decisions on how to deal with it - do you impute the values? Remove them? These each have consequences! The data we often encounter does not always arrive with a research question in mind, so how do you understand why you have missing values? When I first encountered missing data I was incredibly frustrated at how hard it was to understand and explore it. This frustration led me to create two R packages to explore missing data, {naniar} and {visdat}. In this talk I will showcase how to use these tools to explore missing data, as well as new features that have not been presented, and planned advances.
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