miss_var_run | R Documentation |
It us useful to find the number of missing values that occur in a single run.
The function, miss_var_run()
, returns a dataframe with the column names
"run_length" and "is_na", which describe the length of the run, and
whether that run describes a missing value.
miss_var_run(data, var)
data |
data.frame |
var |
a bare variable name |
dataframe with column names "run_length" and "is_na", which describe the length of the run, and whether that run describes a missing value.
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()
miss_var_run(pedestrian, hourly_counts) ## Not run: # find the number of runs missing/complete for each month library(dplyr) pedestrian %>% group_by(month) %>% miss_var_run(hourly_counts) library(ggplot2) # explore the number of missings in a given run miss_var_run(pedestrian, hourly_counts) %>% filter(is_na == "missing") %>% count(run_length) %>% ggplot(aes(x = run_length, y = n)) + geom_col() # look at the number of missing values and the run length of these. miss_var_run(pedestrian, hourly_counts) %>% ggplot(aes(x = is_na, y = run_length)) + geom_boxplot() # using group_by pedestrian %>% group_by(month) %>% miss_var_run(hourly_counts) ## End(Not run)
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