Identify if there are any or all missing or complete values

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)
#> [1] TRUE
all_na(complete)
#> [1] FALSE
all_na(mixture)
#> [1] FALSE
all_complete(misses)
#> [1] FALSE
all_complete(complete)
#> [1] TRUE
all_complete(mixture)
#> [1] FALSE

any_na(misses)
#> [1] TRUE
any_na(complete)
#> [1] FALSE
any_na(mixture)
#> [1] TRUE

# for data frames
all_na(airquality)
#> [1] FALSE
# an alias of all_na
all_miss(airquality)
#> [1] FALSE
all_complete(airquality)
#> [1] FALSE

any_na(airquality)
#> [1] TRUE
any_complete(airquality)
#> [1] TRUE

# use in identifying columns with all missing/complete

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
# for printing
aq <- as_tibble(airquality)
aq
#> # A tibble: 153 × 6
#>    Ozone Solar.R  Wind  Temp Month   Day
#>    <int>   <int> <dbl> <int> <int> <int>
#>  1    41     190   7.4    67     5     1
#>  2    36     118   8      72     5     2
#>  3    12     149  12.6    74     5     3
#>  4    18     313  11.5    62     5     4
#>  5    NA      NA  14.3    56     5     5
#>  6    28      NA  14.9    66     5     6
#>  7    23     299   8.6    65     5     7
#>  8    19      99  13.8    59     5     8
#>  9     8      19  20.1    61     5     9
#> 10    NA     194   8.6    69     5    10
#> # ℹ 143 more rows
# select variables with all missing values
aq %>% select(where(all_na))
#> # A tibble: 153 × 0
# there are none!
#' # select columns with any NA values
aq %>% select(where(any_na))
#> # A tibble: 153 × 2
#>    Ozone Solar.R
#>    <int>   <int>
#>  1    41     190
#>  2    36     118
#>  3    12     149
#>  4    18     313
#>  5    NA      NA
#>  6    28      NA
#>  7    23     299
#>  8    19      99
#>  9     8      19
#> 10    NA     194
#> # ℹ 143 more rows
# select only columns with all complete data
aq %>% select(where(all_complete))
#> # A tibble: 153 × 4
#>     Wind  Temp Month   Day
#>    <dbl> <int> <int> <int>
#>  1   7.4    67     5     1
#>  2   8      72     5     2
#>  3  12.6    74     5     3
#>  4  11.5    62     5     4
#>  5  14.3    56     5     5
#>  6  14.9    66     5     6
#>  7   8.6    65     5     7
#>  8  13.8    59     5     8
#>  9  20.1    61     5     9
#> 10   8.6    69     5    10
#> # ℹ 143 more rows

# select columns where there are any complete cases (all the data)
aq %>% select(where(any_complete))
#> # A tibble: 153 × 6
#>    Ozone Solar.R  Wind  Temp Month   Day
#>    <int>   <int> <dbl> <int> <int> <int>
#>  1    41     190   7.4    67     5     1
#>  2    36     118   8      72     5     2
#>  3    12     149  12.6    74     5     3
#>  4    18     313  11.5    62     5     4
#>  5    NA      NA  14.3    56     5     5
#>  6    28      NA  14.9    66     5     6
#>  7    23     299   8.6    65     5     7
#>  8    19      99  13.8    59     5     8
#>  9     8      19  20.1    61     5     9
#> 10    NA     194   8.6    69     5    10
#> # ℹ 143 more rows