Remove Rows Containing Missing Values

drop_na() drops rows that contain NA values in selected columns.

library(tidyr)library(dplyr)
df <- tibble(id = c(1, 2, NA, 4),             first_name = c("Alice", "Bob", NA, "David"),             last_name = c("Smith", "Johnson", NA, "Miller"),             age = c(25, NA, 35, 40))df

Output:
# A tibble: 4 × 4
     id first_name last_name   age
  <dbl> <chr>      <chr>     <dbl>
1     1 Alice      Smith        25
2     2 Bob        Johnson      NA
3    NA <NA>       <NA>         35
4     4 David      Miller       40

Remove rows containing missing values NA. Only rows not containing NA are retained in the output.

df %>% drop_na()

Output:
# A tibble: 2 × 4
     id first_name last_name   age
  <dbl> <chr>      <chr>     <dbl>
1     1 Alice      Smith        25
2     4 David      Miller       40

Remove rows that contain NA values in the id and first_name column.

df %>% drop_na(id, first_name)

Output:
# A tibble: 3 × 4
     id first_name last_name   age
  <dbl> <chr>      <chr>     <dbl>
1     1 Alice      Smith        25
2     2 Bob        Johnson      NA
3     4 David      Miller       40

Selection helpers can be used to conveniently select a block of columns. E.g., here we remove rows that contain NA values in numeric columns.

df %>% drop_na(where(is.numeric))

Output:
# A tibble: 2 × 4
     id first_name last_name   age
  <dbl> <chr>      <chr>     <dbl>
1     1 Alice      Smith        25
2     4 David      Miller       40

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