dplyr >= 1.0.0 using across
sum up each row using rowSums
(rowwise
works for any aggreation, but is slower)
df %>%
replace(is.na(.), 0) %>%
mutate(sum = rowSums(across(where(is.numeric))))
sum down each column
df %>%
summarise(across(everything(), ~ sum(., is.na(.), 0)))
dplyr < 1.0.0
sum up each row
df %>%
replace(is.na(.), 0) %>%
mutate(sum = rowSums(.[1:5]))
sum down each column using superseeded summarise_all
:
df %>%
replace(is.na(.), 0) %>%
summarise_all(funs(sum))