any-all-na-complete {naniar}

Identify if there are any or all missing or complete values

Description

It is useful when exploring data to search for cases where there are any or all instances of missing or complete values. For example, these can help you identify and potentially remove or keep columns in a data frame that are all missing, or all complete.

For the any case, we provide two functions: any_miss and any_complete. Note that any_miss has an alias, any_na. These both under the hood call anyNA. any_complete is the complement to any_miss - it returns TRUE if there are any complete values. Note that in a dataframe any_complete will look for complete cases, which are complete rows, which is different to complete variables.

For the all case, there are two functions: all_miss, and all_complete.

Usage

any_na(x)

any_miss(x)

any_complete(x)

all_na(x)

all_miss(x)

all_complete(x)

Arguments

x

an object to explore missings/complete values

See Also

all_miss() all_complete

Examples

# for vectors
misses <- c(NA, NA, NA)
complete <- c(1, 2, 3)
mixture <- c(NA, 1, NA)

all_na(misses)
all_na(complete)
all_na(mixture)
all_complete(misses)
all_complete(complete)
all_complete(mixture)

any_na(misses)
any_na(complete)
any_na(mixture)

# for data frames
all_na(airquality)
# an alias of all_na
all_miss(airquality)
all_complete(airquality)

any_na(airquality)
any_complete(airquality)

# use in identifying columns with all missing/complete

library(dplyr)
# for printing
aq <- as_tibble(airquality)
aq
# select variables with all missing values
aq %>% select(where(all_na))
# there are none!
#' # select columns with any NA values
aq %>% select(where(any_na))
# select only columns with all complete data
aq %>% select(where(all_complete))

# select columns where there are any complete cases (all the data)
aq %>% select(where(any_complete))