miss_var_run {naniar}

Find the number of missing and complete values in a single run

Description

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.

Usage

miss_var_run(data, var)

Arguments

data

data.frame

var

a bare variable name

Value

dataframe with column names "run_length" and "is_na", which describe the length of the run, and whether that run describes a missing value.

See Also

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()

Examples

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)