R remove NA values from 3 columns only when all 3 have NA

im trying to clean up a dataset and im encountering a problem, 3 colums are related so before i can continue to do some clean up, i would like to remove the NAs ONLY when they all 3 have them

example

      A.   B.   C.    
1.-   NA.  NA.  NA. <- to be removed
2.-   NA.  NA.  10. <- not to be removed
3.-   NA.  29.  NA  <- not to be removed
4.-   NA.  NA.  NA. <  to be removed

I have tried so far with:

subset(data, data$A == NA & data$B == NA & data$C == NA)

data_new <- data[complete.cases(data$A) & (data$B) & (data$C), ]

but nothing seems to work

any help will be much apreciated.

>Solution :

The complete.cases code can be with | condition as complete.cases returns TRUE for a non-NA value and FALSE for NA. Thus, by using the OR, we are subsetting a row having at least one non-NA

data[complete.cases(data$A) | complete.cases(data$B) | complete.cases(data$C),]

Or more easily with rowSums

data[rowSums(is.na(data[, c("A", "B", "C")])) < 3,]

Or with dplyr with if_all or if_any

library(dplyr)
data %>% 
  filter(!if_all(c(A, B, C), is.na))