```
tibble(
A = c("A","A","B","B"),
x = c(NA,NA,NA,1),
y = c(1,2,3,4),
) %>% group_by(A) -> df
```

desired output:

```
tibble(
A = c("B","B"),
x = c(NA,1)
y = c(3,4),
)
```

I want to find all groups for which all elements of `x`

and `x`

only are all `NA`

, then remove those groups. `"B"`

is filtered in because it has at least 1 non `NA`

element.

I tried:

```
df %>%
filter(all(!is.na(x)))
```

but it seems that filters out if it finds at least 1 NA; I need the correct word, which is not `all`

.

### >Solution :

This will remove groups of column `A`

if all elements of `x`

are `NA`

:

```
library(dplyr)
df %>%
group_by(A) %>%
filter(! all(is.na(x)))
# A tibble: 2 × 3
# Groups: A [1]
# A x y
# <chr> <dbl> <dbl>
#1 B NA 3
#2 B 1 4
```

Note that group `"A"`

was removed because both cells in the column `x`

are not defined.