How to remove rows where all columns are zero using dplyr pipe

Question

I have the following data frame:

dat <- structure(list(`A-XXX` = c(1.51653275922944, 0.077037240321129, 
0), `fBM-         


        

Answer 1

Adding to the answer by @mgrund, a shorter alternative with dplyr 1.0.0 is:

# Option A:
data %>% filter(across(everything(.)) != 0))

# Option B:
data %>% filter(across(everything(.), ~. == 0))

Explanation:
across() checks for every tidy_select variable, which is everything() representing every column. In Option A, every column is checked if not zero, which adds up to a complete row of zeros in every column. In Option B, on every column, the formula (~) is applied which checks if the current column is zero.

EDIT:
As filter already checks by row, you don't need rowwise(). This is different for select or mutate.

IMPORTANT:
In Option A, it is crucial to write across(everything(.)) != 0,
and NOT across(everything(.) != 0))!

Reason:
across requires a tidyselect variable (here everything()), not a boolean (which would be everything(.) != 0))