library(tidyverse)
library(tidyselect)
library(janitor)
library(lubridate)

Keep only rows that contain missing data

Code

airquality %>% 
  filter(if_any(everything(), ~ is.na(.x)))

Output

# A tibble: 42 × 6
   Ozone Solar.R  Wind  Temp Month   Day
   <int>   <int> <dbl> <int> <int> <int>
 1    NA      NA  14.3    56     5     5
 2    28      NA  14.9    66     5     6
 3    NA     194   8.6    69     5    10
 4     7      NA   6.9    74     5    11
 5    NA      66  16.6    57     5    25
 6    NA     266  14.9    58     5    26
 7    NA      NA   8      57     5    27
 8    NA     286   8.6    78     6     1
 9    NA     287   9.7    74     6     2
10    NA     242  16.1    67     6     3
# … with 32 more rows

Filter but keep missing data

Code

airquality %>% 
  filter((Temp < 80) %>% replace_na(TRUE))

Output

# A tibble: 80 × 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
# … with 70 more rows

Remove empty rows

Code

mtcars %>% 
  remove_empty("rows")

Output

# A tibble: 32 × 11
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
 2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
 3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
 4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
 5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
 6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
 7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
 8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
 9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
# … with 22 more rows