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Provide a tidy table of the number of cases with 0, 1, 2, up to n, missing values and the proportion of the number of cases those cases make up.
miss_case_table(data)
a dataframe
pct_miss_case() prop_miss_case() pct_miss_var() prop_miss_var() pct_complete_case() prop_complete_case() pct_complete_var() prop_complete_var() miss_prop_summary() miss_case_summary() miss_case_table() miss_summary() miss_var_prop() miss_var_run() miss_var_span() miss_var_summary() miss_var_table() n_complete() n_complete_row() n_miss() n_miss_row() pct_complete() pct_miss() prop_complete() prop_complete_row() prop_miss()
pct_miss_case()
prop_miss_case()
pct_miss_var()
prop_miss_var()
pct_complete_case()
prop_complete_case()
pct_complete_var()
prop_complete_var()
miss_prop_summary()
miss_case_summary()
miss_case_table()
miss_summary()
miss_var_prop()
miss_var_run()
miss_var_span()
miss_var_summary()
miss_var_table()
n_complete()
n_complete_row()
n_miss()
n_miss_row()
pct_complete()
pct_miss()
prop_complete()
prop_complete_row()
prop_miss()
# NOT RUN { miss_case_table(airquality) # } # NOT RUN { library(dplyr) airquality %>% group_by(Month) %>% miss_case_table() # }
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