How to check whether at least one column is false, ignoring NA in a R dataset?

I have a table like that
| A | B | C |
| ——| ——-| ——-|
| TRUE | NA | FALSE |
| NA | FALSE | NA |
| NA | NA | TRUE |
| NA | NA | NA |

I would like to create a column that takes value TRUE if any column is TRUE (ignoring NA), otherwise, if all columns are FALSE or NA takes value FALSE, and finally takes NA only if all columns are NA.

I have tried with the if_any function, but it works only for the TRUE case.

df <- data.frame(A = c(T, NA, NA, NA),
                 B = c(NA, F, NA, NA), 
                 C = c(F, NA, T, NA))

df <- df %>%
      mutate (Global = if_any(.cols = c(A,B,C) ,I)

Is there an easy way to achieve that?

>Solution :

as.logical(rowMeans(df,na.rm=T))
[1]  TRUE FALSE  TRUE    NA