Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations

HIGHLIGHTS

SUMMARY

One such idea is "tidy data", which defines a clean, analysis-ready format that informs workflows converting raw data through a data_analysis pipeline (Wickham 2014). There is little guidance on how to handle missing values in a data_analysis workflow. Features of tidy data were formally described in Wickham, and were discussed in terms of their importance for data science by Donoho, and tools for data_analysis. Messy models have been partially addressed with the broom package (Robinson, Hayes, and Couch 2022), which tidies up model outputs into a tidy data format for data_analysis, and the . . .

 



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