mutate rowSums exclude one column

If you want to keep non-numeric columns in the result, you can do this:

dat %>% mutate(total=rowSums(.[, sapply(., is.numeric)]))

UPDATE: Now that dplyr has scoped versions of its standard verbs, here's another option:

dat %>% mutate(total=rowSums(select_if(., is.numeric)))

UPDATE 2: With dplyr 1.0, the approaches above will still work, but you can also do row sums by combining rowwise and c_across:

iris %>% 
  rowwise %>% 
  mutate(row.sum = sum(c_across(where(is.numeric))))

This should work:

#dummy data    
df <- read.table(text="a x y z

1 name1 1 1 1
2 name2 1 1 1
3 name3 1 1 1
4 name4 1 1 1",header=TRUE)

library(dplyr)

df %>% select(-a) %>% mutate(total=rowSums(.)) 

First exclude text column - a, then do the rowSums over remaining numeric columns.

You can use rich selectors with select() inside the call to rowSums()

df %>% transmute(a, total = rowSums(select(., -a)))