Question: I have a problem that should be easy to solve but I simply cannot figure it out. I have a huge dataset with groups and a variable. Some groups are empty for this variable (filled only with NAs) and some contains values but also NAs.

For example:

```
ID <- c("A1","A1","A1","A1","B1","B1","B1","B1", "B1", "C1", "C1", "C1")
Value1 <- c(0,2,1,1,NA,1,1,NA,1,NA,NA,NA)
data <- data.frame(ID, Value1)

```

I would like to change all NAs to zeros but only in groups that otherwise contain information.

So like this:

```
ID <- c("A1","A1","A1","A1","B1","B1","B1","B1","B1","C1","C1","C1")
Value1 <- c(0,2,1,1,0,1,1,0,1,NA,NA,NA)

```

I tried to use group\_by(ID) and "replace" with the condition max(Value1)>=0 but either max() doesn't work as a condition or it doesn't work with NAs. Unfortunately I would need this kind of conditioning often in my work so I would also appreciate any suggestions on which are the best packages to treat groups selectively.

Answer: 
You can use a simple if` statement, i.e.

```
library(dplyr)
library(tidyr)

data %>% 
 group_by(ID) %>% 
 mutate(Value1 = if (all(is.na(Value1))){Value1}else{replace_na(Value1, 0)})

```

which gives,

> 
> 
> ```
> # A tibble: 12 x 2
> # Groups:   ID [3]
>    ID    Value1
>    <fct>  <dbl>
>  1 A1         0
>  2 A1         2
>  3 A1         1
>  4 A1         1
>  5 B1         0
>  6 B1         1
>  7 B1         1
>  8 B1         0
>  9 B1         1
> 10 C1        NA
> 11 C1        NA
> 12 C1        NA
> 
> ```
> 
>
Using `data.table`

```
setDT(data)
data[, Value1 := if (all(is.na(Value1))) NA else replace(Value1, is.na(Value1), 0), by = ID]

    ID Value1
 1: A1      0
 2: A1      2
 3: A1      1
 4: A1      1
 5: B1      0
 6: B1      1
 7: B1      1
 8: B1      0
 9: B1      1
10: C1     NA
11: C1     NA
12: C1     NA

```