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you run a 100s-1000s of columns data frame through missingness it's hard to make sense of the results. Often wide data frames like that result from one-hot encoding or dummying, where lots of derived columns that have the same missingness. E.g.:\r\n\r\n```\r\ndata.frame(dateDTS = c(\"2018-07-31\", \"2018-07-31\", \"2018-08-01\", NA), y = 1:4) %>% \r\n  prep_data(impute = FALSE) %>% \r\n  missingness()\r\n```\r\n\r\nMake a summary method that lets the user know that x% of variables have some missingness and the most missingness is in y column and is z%. Let the user decide to have the column names listed with variables over a specified percent of missingness."}}
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