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faster than diagnose if emphasis is on diagnosing missing values. Also, only shows the columns with any missing values.
diagnose_missing(df, ...)
dataframe
optional tidyselect
tibble summary
# NOT RUN { iris %>% framecleaner::make_na(Species, vec = "setosa") %>% diagnose_missing() # }
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